the impact of big data mining on oil and gas production...
TRANSCRIPT
The Impact of Big Data Mining on Oil and Gas Production
Efficiency:
An Exploratory Case Study of Petroleum Development Oman’s
Real Time Production Portal “Nibras”.
A study submitted in partial fulfilment
of the requirements for the degree of
MSc Data Science
at
THE UNIVERSITY OF SHEFFIELD
by
JAIFAR AL HINAI
September 2016
Abstract Background: Over the last few years businesses in every industry utilized the advancement
in technology to collect data about almost everything. As a result, businesses were
confronted with datasets that are beyond the ability of normal tools to process. Mining
such big data has been the approach adopted by many companies to extract the value of
the data by turning it to information and knowledge. Oil and gas companies are not an
exception in this regard. Big data mining solutions have been adopted at different areas of
oil and gas industry to increase efficiency.
Aims: This research aimed to investigate the impact of big data mining on oil and gas
production efficiency. The research study explores the impact of adopting big data mining
solution “Nibras” on the production operation efficiency at Petroleum Development Oman
(PDO).
Methods: An inductive approach was adopted in this research study to design a qualitative
data collection method. Semi-structured interviews were used as the instrument to collect
data from the research participants at PDO.
Results: Nibras was developed and implemented to integrate different datasets from
different data source at PDO and utilize different data mining techniques to extract
knowledge out of it. The datasets that Nibras bring together are stored at different
corporate systems that are used by different PDO’s production operation teams. Nibras
enabled different teams to have more insight into production operations at the field by
providing information about different aspect of production processes at one platform.
Moreover, it enabled effective collaboration between different teams and disciplines
working for production operations by standardizing and automating data integration and
analysis. As a result production operation teams managed to change their working
approach from reactive to proactive approach as well as having more time to fix issues.
Conclusion: The conclusion tends to highlight the vital role of big data mining solution
“Nibras” in increasing the efficiency of PDO’s production operation. This was recognized by
the increase in the average daily production and the decrease in none productive time after
the implementation of Nibras.
Keywords: Big Data; Data Mining; Oil and Gas; Production Efficiency
Table of Contents Chapter 1: Introduction ...................................................................................................... 1
1.1- Background of the research ...................................................................................... 1
1.2- Statement of the problem ........................................................................................ 2
1.3- Purpose of the research ........................................................................................... 2
1.4- Objectives of the research ........................................................................................ 2
1.5- Research question .................................................................................................... 3
1.6- Structure of the research .......................................................................................... 3
Chapter 2: Literature Review .............................................................................................. 4
2.1- Introduction .............................................................................................................. 4
2.2- Background ............................................................................................................... 4
2.2- Big Data ..................................................................................................................... 5
2.3- Data Mining .............................................................................................................. 6
2.4- Big Data Mining and Visualization in Oil and Gas ..................................................... 6
2.4.1- Exploration ............................................................................................................. 6
2.4.2- Drilling .................................................................................................................... 7
2.4.3- Production ............................................................................................................. 7
Chapter 3: Research Methodology ................................................................................... 12
3.1- Introduction ............................................................................................................ 12
3.2- Research approach ................................................................................................. 12
3.3- Research strategy ................................................................................................... 13
3.4- Research design ...................................................................................................... 13
3.5- Sampling ................................................................................................................. 14
3.6- Data collection method .......................................................................................... 14
3.7- Data collection process and data analysis .............................................................. 15
3.8- Limitations of the methodology and future suggestions ....................................... 16
3.9- Ethical practices ...................................................................................................... 17
3.10- Summary ............................................................................................................... 17
Chapter 4: Nibras .............................................................................................................. 18
4.1- Introduction ............................................................................................................ 18
4.2- Background ............................................................................................................. 18
4.3- Scope of Nibras ....................................................................................................... 18
4.4- Nibras architecture ................................................................................................. 20
4.4.1- Overview .............................................................................................................. 20
4.4.2- Data Sources ........................................................................................................ 21
4.4.3- Data Abstraction Layer ........................................................................................ 22
4.4.4- Services Layer ...................................................................................................... 23
4.4.5- Visualization & Reporting Layers ......................................................................... 23
4.5- Exception Based Surveillance ................................................................................. 24
4.5.1- Overview .............................................................................................................. 24
4.5.2- How EBS Works .................................................................................................... 24
4.5.3- Alarms Types ........................................................................................................ 24
4.5.4- EBS Alarms Life Cycle ........................................................................................... 27
Chapter 5: Research results and findings ......................................................................... 28
5.1- Introduction ............................................................................................................ 28
5.2- Decision making process in PDO production operations before Nibras
implementation ............................................................................................................. 28
5.2.1- Production Programming .................................................................................... 28
5.2.2- Well and Reservoir Management ........................................................................ 29
5.2.3- Facility management ........................................................................................... 29
5.3- Main derivers to data mining solution (Nibras) ...................................................... 30
5.4- Key enablers of Nibras implementation ................................................................. 31
5.5- Areas of Nibras impact ........................................................................................... 32
5.5.1- Production Programming .................................................................................... 32
5.5.2- Well and Reservoir Management ........................................................................ 33
5.5.3- Facility management ........................................................................................... 34
5.6- Data quality assurance............................................................................................ 34
5.7- Summary ................................................................................................................. 35
Chapter 6: Discussion and Analysis .................................................................................. 36
6.1- Introduction ............................................................................................................ 36
6.2- Decision making at PDO’s production operations before the adoption of Nibras . 36
6.3- Key derivers and enablers of Nibras implementation at PDO ................................ 37
6.4- The impact of Nibras on production operations at PDO ........................................ 37
6.4.1- More insight ......................................................................................................... 37
6.4.2- Collaboration ....................................................................................................... 38
6.4.3- Less time on data gathering and analysis ............................................................ 38
6.4.4- More confidence.................................................................................................. 38
6.6- Summary ................................................................................................................. 39
Chapter 7: Conclusion ....................................................................................................... 40
7.1- Conclusion .............................................................................................................. 40
7.2- Research limitations and Recommendation for Future Research .......................... 41
References: .................................................................................................................... 42
Appendix 1: Ethical Approval Form .............................................................................. 46
Appendix 2: Informed Consent Forms .......................................................................... 47
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Chapter 1: Introduction
1.1- Background of the research
Recent years have witnessed a rapid increase in the amount of data generated,
collected, and stored all over the world. The data is generated from different sources, in
different formats, at different speeds, and for different purposes. Such a data explosion
outpaced our ability to process, analyze, store, and derive insight out of it (Fan & Bifet,
2013). Consequently, we are confronted with the challenge of “Big Data”. That is, datasets
that are beyond the ability of normal tools to acquire, manage, and analyze in an
acceptable elapse of time (Chakraborty & Gonnade, 2014). Although there is no common
accepted definition of “Big Data” as of today, it is always define as data that is
characterized as big by some combination of the five Vs: Volume, Variety, Velocity, Value,
and Veracity(Keith, 2013). Volume: is where the data to be acquired and processed is too
big in size for normal tools to handle. Variety: is where the data is coming from different
sources and in different formats. Velocity: is where the data is generated at high rates.
Value: is where the data is assumed to have big value for an organization. Veracity: is
where quality of the data is not always high and required to be assessed.
Big data is like crude oil. It is valuable, but can’t be used if not refined or mined.
Therefore, mining big data is necessary to extract knowledge and insight out of it. In fact,
“Big Data” and “Data Mining” were very relevant terms from the beginning. The first book
mentioning the term “Big Data” was a data mining book (Fan & Bifet, 2013). As a result, big
data mining opened new opportunities in all sectors – at different levels – to enhance
productivity and efficiency(McKinsey & Company, 2011).
The oil and gas industry is mainly about exploring, extracting, processing, and
transporting hydrocarbons. Traditionally, it is divided into three main categories: upstream,
midstream, and downstream. Upstream oil and gas includes exploration of hydrocarbon
reserves, drilling wells to gain access to the hydrocarbon reserves, and then producing the
hydrocarbon. It is characterized to be a competitive and highly regulated business.
Fluctuations in oil and gas demands and prices impose a big challenge on oil and gas
companies to increase their production efficiency and optimize the costs (Baaziz &
Quoniam, 2013). Upstream oil and gas companies have been dealing with big data for many
years. Compared with other industries, upstream data is already “big”. For example, when
looking at volume, a typical seismic data centre can easily contain as much as 20 petabytes
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of information (Perrons & Jensen, 2015). It is clear that the business model of other
industries depending on social networks and online retailing is far different from that of oil
and gas. And therefore, big data delivers value to the oil and gas industry in a way that is
different from other industries. When compared with other industries, efficiency is a key
competitive advantage for oil and gas companies. A small improvement in efficiency can
make a significant economic difference(Cowles, 2014).
1.2- Statement of the problem
The large number of measurement points streaming through the sensors and
instruments utilized in oil and gas production operations, supported by advance
communication technologies, has led to a significant increase in the volume of data stored
in various data repositories. In addition, the variety of the data source is also increasing
from relational databases, time series data, text reports, and visual images. Many oil and
gas companies implemented different big data mining solutions around the production
operations. However, in order for oil and gas companies to enable the production of new
difficult oil through complex production operations and to optimize production from
existing fields, they need to adopt technologies that semantically integrate and analyze
diverse datasets around the production operations. Although some oil and gas companies
have started adopting such approaches for big data mining and knowledge extraction, the
impact of such solutions on production efficiency is still to be explored.
1.3- Purpose of the research
Main Aim: The main aim of this study is to investigate the added value and benefits derived
from implementing Big Data Mining and knowledge extraction solutions on real time data
of oil production operations. In particular, the researcher aims to investigate the efficiency
increase in the day-to-day business of production management teams at Petroleum
Development Oman(PDO) after adopting data mining solution Nibras.
1.4- Objectives of the research
To gain a wider understanding of the possible applications, impact, and key
considerations when using big data mining in the oil and gas production operation.
To explore the decision making process and efforts before the adoption of Data
Mining on the production data at Petroleum Development Oman.
To identify main derivers of implementing Data Mining on the production data at
Petroleum Development Oman.
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To investigate the production operation’s efficiency measures in Petroleum
Development Oman and the impact of adopting Data Mining on it.
To explore the critical success factors associated with the implementation of the
production operations’ big data mining at Petroleum Development Oman.
To highlight and discuss key components of PDO’s Data Mining architecture Nibras.
To investigate the Data Mining techniques and visualizations used in Nibras.
1.5- Research question
How is production efficiency measured in PDO? And how was it measured before
the implementation of “Nibras”?
What were the key enablers of Nibras’ successful implementation?
What are the different data sets that “Nibras” bring together and mine?
How much in savings did PDO achieve by adopting data mining on real time
production data?
How important is the data quality for successful data mining solutions
implantation?
1.6- Structure of the research
This research study is presented in seven chapters. The first chapter introduces the
concept of big data mining and the oil and gas industry. Moreover, it defines the aim,
objectives, and the questions of this research study. Chapter two is a review of literature
about big data mining and the use of big data mining in oil and gas production operations.
It will first review literature about the concept of big data and data mining. Then it will
review literature about the application of big data mining in the oil and gas industry with
more focus on literature about the application of big data mining in oil and gas production
operations. Chapter three presents and explains the methodological procedures and tools
used in this research study as well as providing justification as to why these methodological
tools and procedures are employed in this study. Chapter four presents a high level
overview about the case study of this research study with more focus on the data mining
part of it. Chapter five presents the results obtained using the methodological procedures
and tools highlighted in chapter three. Chapter six discusses and analyzes the data in
chapter four and five in accordance with the research aim and objectives developed for this
research study. Further, data analysis results are backed up with viewpoints reviewed in
chapter two. Finally, chapter seven presents conclusions about findings obtained in chapter
six.
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Chapter 2: Literature Review
2.1- Introduction
This literature review addresses the study’s first research objective: To gain a wider
understanding of the possible applications, impact, and key considerations when using big
data mining in the oil and gas production operation. Many publications have discussed the
use of big data mining in the oil and gas industry and have taken different approaches.
Some of them like Baaziz & Quoniam (2013) and Cowles (2014) addressed the areas of big
data in the oil and gas industry and discussed the possible applications of data mining
across the industry. Others discussed the application of data mining in a specific area of the
oil and gas industry. For example Aminzadeh (2005) discussed how big data mining
technologies help in the area of exploration in the oil industry. In another example,
Guangren, Yixiang, Shiyun, Jinshan, & Jun (2014) addressed a very focused problem in the
oil and gas industry where they used regression and classification to analyze well logs data
that are used by petroleum engineers to understand oil well characteristics. Therefore, this
literature review will first give a background to the oil and gas industry and its data, and
then it will discuss the phenomenon of big data and data mining. Finally, it will address the
applications of big data mining in the oil and gas industry with a focus on the production
operations of oil and gas.
2.2- Background
The oil and gas industry is usually broken down into three main categories (Cowles,
2014): upstream, midstream, and downstream. Upstream is concerned with finding
hydrocarbons underground and putting them into production. It includes exploration,
drilling, and production. Midstream includes transportation of hydrocarbons, wholesale
and marketing, and manufacturing and refinement of crude. Downstream is concerned
with the delivery of the refined product to consumers. Generally, the oil and gas industry is
built on experience and individuals’ knowledge. It employs highly skilled and experienced
scientists and engineers that are very good at what they do and they have been doing it for
long time.
The oil and gas industry has been dealing with large amounts of data longer than
most, some even calling it the “original big data industry” (Cowles, 2014). Because of oil
and gas business complexity, large increases in the quantity, resolution, and frequency of
data supported by the advances of attached sensors, devices, and appliances being
combined with large amounts of historical data imposed big data challenges to the
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industry. The majority of big data mining application is found in upstream business where
risk and uncertainty are high, and efficiency is a corner stone for profitability.
2.2- Big Data
According to Baaziz & Quoniam(2013), “Big Data is the oil of the new economy”
was the most famous citation in the years 2011 to 2013. Experts and researchers are
expecting data to be the natural resource that will power the new industrial revolution just
like oil did for the last industrial revolution. But what is “Big Data”? And what are the
reasons behind it? As per Chavan & Phursule (2014), the term “Big Data" was figured
prominently by John Mashey in a lunch table conversation in the mid-1990s. Since then,
“Big Data” has attracted considerable attention from researchers and businesses.
There are many definitions of the term “Big Data”. Most of them are based around
the fact that the world is producing huge amounts of data in an increasing trend every day
(Fan & Bifet, 2013). However, many publications define “Big Data” as a phenomenon that
imposes opportunities and challenges. For example Kaisler, Armour, Espinosa, & Money
(2013) and Gerard, Martine, & Alex (2014) focused on size as the dimension to define “Big
Data” as any dataset of a size beyond technology’s ability to manage it efficiently. Vitolo,
Elkhatib, Reusser, Macleod, & Buytaert (2015) added complexity as another dimension to
the size in their definition of “Big Data”. Furthermore, Gartner defined “Big Data” to be
data that is characterized as being big in three dimensions: volume, velocity, and variety
(Chen, Mao, & Liu, 2014). Fan & Bifet (2013) added another two dimensions: variability and
value. However, there is no standard agreed benchmark defining the scale of these
dimensions. Moreover, these dimensions are not independent from each other (Gandomi &
Haider, 2014). A change in one dimension always results in changes to other dimensions.
Others like Wu, Zhu, Wu, & Ding (2014) and Vilas (2012) defined “Big Data” based on HACE
theorem where they describe “Big Data” as principally large-volume but heterogeneous
and coming from autonomous sources and when someone seeks to explore complex and
evolving relationships among the data. Generally, “Big Data” is datasets that are big in size
and complex in structure and are beyond the current normal computation technology’s
ability to manage (Chakraborty & Gonnade, 2014).
Big data has not been a result of a single factor. Rather, it is a result of different
interrelated advancements in technology (Perrons & Jensen, 2015). Technology
advancement in three main areas makes “Big Data” possible: (a) the exponential increase in
the data generation technologies such as social media applications, interconnected mobile
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devices, sensors networks, and web transactions, (b) the decline in data storage cost, and
(c) the continued growth in computation and data processing capabilities.
2.3- Data Mining
“Big Data” is worthless by itself. The main challenge for “Big Data” is to extract
information and knowledge from it for further actions and decision making (Vilas, 2012). In
fact, the value of “Big Data” lies in the knowledge extracted out of it using data mining tools
and applications. PhridviRaj & GuruRao ( 2014) defines data mining as” the process of
discovering hidden patterns and information from the existing data”. Data mining
encapsulates several techniques to process and analyze big data in order to extract
valuable knowledge out of it. These techniques may include – but are not limited to –
classification, clustering, association, pattern matching, prediction, and data visualization
(Liao, Chu, & Hsiao, 2012). Depending on the data dimensions and the targeted goals, one
or a combination of techniques is used in the data mining process.
2.4- Big Data Mining and Visualization in Oil and Gas
“Big Data” is not new to oil and gas companies. Upstream oil and gas companies
have been dealing with large and diverse data for a long time (Daniel, 2014). The upstream
oil and gas journey with data started with the digital oilfield solutions through which the
industry succeeded to gain competitive advantage by utilizing digital oilfield technologies to
make better decisions (Perrons & Jensen, 2015). However, data revolution is not finished
for the oil and gas industry. Advance exploration and production technologies in the
upstream business are surrounded by the enormous number of data streams that have
enabled the digitization and automation of many areas of upstream oil and gas industry
(Martinotti, Nolten, & Steinsbø, 2014).
It is clear that the industry succeeded in collecting data about almost everything.
However, the next step is to employ big data mining technologies to gain more insight out
of such data. Big data mining has many applications in the upstream oil and gas companies
such as:
2.4.1- Exploration
Due to the advancement in seismic acquisition technologies, storage capability, and
advance processing power, exploration data has grown large in volume and comes at high
velocity. Big data mining technologies turned this to 4D (X, Y, Z, and Time) seismic that gives
much better insight into what lies under the ground (Daniel, 2014). Such insight enabled
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the industry to achieve several new discoveries that were not possible with traditional
technologies.
2.4.2- Drilling
Well drilling is the most expensive operation during the oilfield’s development
lifecycle in which more than 60% of the capital expenditure is spent(Tavallali & Karimi,
2016). Drilling operations are known to generate huge amounts of data at a very high
speed. This is due to the risk associated with the drilling operations and the close real time
monitoring requirement. Therefore, big data mining tools are used to predict and detect
any anomalies that may impose risk to assets or people (Baaziz & Quoniam, 2013).
Moreover, big data mining technologies are used for real time remote monitoring, drilling
optimization, and predictive maintenance.
Although there are many potential opportunities and benefits in utilizing big data
mining solutions in the upstream oil and gas industry, the biggest value return seems to be
in the production operation phase (Martinotti et al., 2014). Figure 1 shows six areas where
oil and gas experts see big data mining has the potential to add value.
Figure 1: The highest-impact automation opportunities in upstream (Martinotti et al.,
2014)
2.4.3- Production
Oilfield production phase is the longest in any oilfield lifecycle. Most of the
upstream oil and gas companies have issues in sustaining production-efficiency
improvements during the production phase (Martinotti et al., 2014). However,
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improvements in production-efficiency – even a small percentage – can make a big
difference in the upstream oil and gas industry (Daniel, 2014). Therefore, big data mining
has proved successful in different areas around the oil and gas production-efficiency. This
may include – but is not limited to – performance monitoring, production optimization,
safety improvement, and risk prevention (Baaziz & Quoniam, 2013).
Production efficiency and high recovery factor1 are important competitive
advantages for any oil and gas company during the production phase of an oilfield lifecycle.
Hence, digital oilfield technologies have attracted huge investments from oil and gas
companies in the past two decades (Daniel, 2014). Digital oilfield provided an infrastructure
to collect sensors’ data – in real time – around production operations. It helped production
operations’ teams in real-time production optimization and decision making (Singh,
Pandey, & Shankar, 2015). However, digital oilfield technologies serve in providing only part
of the picture. It collects the real-time data from automation networks and provides it to
different teams.
Big data mining has been utilized by oil and gas companies at different levels to
increase efficiency and recovery factor. For example, big data mining is a key enabler of the
collaboration centres business model adopted by many upstream oil and gas companies. It
helps to integrate data from different sources, with different formats, and with different
resolutions on one platform (Edward, n.d.). This has enabled experts and engineers from
different disciplines to sit around one table looking at one picture of truth. The key point in
this is that different datasets that belong to different production disciplines are brought
together in real time. Such setup of collaboration centres proves to increase efficiency and
reduce costs at different areas of oil and gas production operations such as:
2.4.3.1- Well and Reservoir Management
Oil is not usually found separately in the reservoir under the ground. It is usually
found mixed with gas and sometimes with water. The reservoirs of easy oil – where oil is
flowing naturally from the reservoirs to the surface – have been producing for decades and
the world is working on the oil and gas that is left behind (Tsoskounoglou, Ayerides, &
Tritopoulou, 2008). Such oil cannot be extracted with traditional techniques and skill sets.
The main success factor is to have a fully clear picture of what is happening between the
reservoir and the sale point of oil and gas (Zhong & Du, 2011). For this reason, thousands of
1 Recovery Factor: the amount of oil that can be produced out of the existing oil in a certain
reservoir.
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sensors and measurement points have been utilized by the oil and gas companies to enable
oil and gas extraction. According to James (2006), having data collected about every drop of
oil is of high importance to the oil and gas companies for two reasons. The first is to be able
to allocate back the oil, gas, and water to the reservoir it has been produced from. Such
allocation process is important for tax payment and income sharing between partners. The
second aspect is to enable oil and gas operators to properly manage wells, reservoirs and
production facilities.
To leverage the power of the data collected from all sensors installed and utilized
along the oil and gas extraction process, Cheung et al. (2015) proposed a framework to
integrate data from different sources in order to extract maximum insight out of it using
different data mining tools. In addition to the overall framework, Cheung et al. (2015) also
discussed several data mining applications that enable the proper management of wells,
reservoirs, and production facilities management.
2.4.3.2- Facility Management
All hydrocarbons (oil and gas) produced from the reservoir through production
wells initially flow to production facilities for processing. The volume of oil and gas
produced and the cost of production are always affected by decisions made – on a daily
basis – about the production facilities (Tavallali & Karimi, 2016). These decisions are usually
made at different levels of the organization. Big data mining applications have been key
enablers to these decisions at different levels of production facilities spanning from facility
managers down to technician level (Vennelakanti, 2016). Sponseller (2015), – business
director for Oil and Gas solutions at Kepware – indicated four main areas that big data
mining can be utilized for better production facilities management: (a) measurement,
verification and constant commissioning, (b) root cause analysis and remote
troubleshooting, (c) capacity planning, and (d) safety, security, and compliance.
Several data mining applications have proven successful in oil and gas facility
management. For example, Cheung et al. (2015), illustrated how data mining of sensors
streaming data was used not only to detect equipment failures but also to predict the
occurrence of the failure event ahead before it happens. The same approach had proved to
be successful in predicting the failure of many other different types of equipment used in
the oil and gas industry.
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2.4.3.3- Maintenance
Upstream oil and gas uses very expensive assets such as drilling rigs, production
wells, pipelines, production stations, and oil storage tank farms. According to Nicholson,
Madden, Feblowitz, & Bigliani (2010), the average cost of assets in the big industry players
can reach $100 billion. For this reason, ensuring assets’ integrity and that they are at zero
risk is fundamental for two main reasons (Rabatel, Bringay, & Poncelet, 2011). First, the
cost of these equipments is very high and losing such equipment results in big capital lose
to companies. Second, the None Productive Time (NPT) of these equipment is usually
results in high production lose and consequently financial lose.
Big data mining application has been used in different areas of oil and gas
equipments maintenance. For example, Sivertsen, Hjertaker, Kjenner, & Stjernberg (2012),
presented how mining motor current data can be used to detect certain faults on the
subsea oil pumps. The first benefit of this is to detect the motor failure before it happens.
Second, is to know the type of failure before pulling out the pump to the surface as it is a
very expensive operation. In another example, Hashemian & Bean (2011), presented a
predictive maintenance solution based on a data mining approach to time series sensors
data. The presented model showed how useful the data mining application is to detecting
blockage in the pressure sensing line based on the sensor data.
2.4.3.4- Production Planning and Forecasting
Figure 2 illustrates a typical oilfield that contains multiple oil and gas reservoirs.
These reservoirs usually contain oil, gas, water, or a mixture of the three. Complex
operations took place in order to produce the hydrocarbons out of the reservoir. An
integrated production operations plan is a must to ensure the hydrocarbons are produced
from the correct reservoir, flow to the right facility, are processed correctly, and
transported or injected back to the right reservoir (Tavallali & Karimi, 2016). A huge amount
of data is collected around processing of hydrocarbons production on a daily basis.
According to James (2006), Shell used big data mining applications to derive value out of
the collected data. First, data mining has been used for hydrocarbon accounting and
allocation. This has enabled field development teams to know what hydrocarbon has been
produced, from which reservoir, where it has been processed and how, and finally where it
has gone. Secondly, big data mining applications have been utilized for production facilities
optimization as well as overall production forecasting.
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Figure 2: hydrocarbon field with multiple reservoirs and associated production
infrastructure (Tavallali & Karimi, 2016).
2.4.3.5- Soft sensing
Another example at lower level, big data mining can be utilized in the concept of
“soft sensing” in which historical data combined with process data are used to predict
measurement (Teixeira, Castro, Teixeira, & Aguirre, 2014). This reduces the cost of
installing a physical sensor and reduces asset damage risks. The same concept and
techniques have been used to forecast the performance of equipments. By combining
different datasets about the equipment together with data mining techniques such as
classification and prediction, production operation engineers were able to optimize the way
they operate these equipments (Wang, Gao, & Li, 2012). In such context, Teixeira et al.
(2014), developed a data driven model to measure the gas-lift well down hole pressure
based on other surface measurement without the need for the pressure sensor at the
bottom of the well. This would potentially reduce the cost of installing or maintaining
pressure sensors at the bottom of the wells.
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Chapter 3: Research Methodology
3.1- Introduction
Saunders, Lewis, & Thornhill, (2009) defined research as something that people do
systematically in order to find out things that increase their knowledge. In addition, they
have differentiated between methods and methodology. They referred to methods as the
techniques and tools used to collect and analyze data. In contrast, they referred to
methodology as the strategy of how the research will be conducted.
This chapter explains the tools and techniques used in this study for purposes of
achieving the research aim and objectives. In some details, the chapter discuses the
research approach, research design, research strategy, sampling process, data collection
techniques used, and the data analysis approach adopted. Moreover, the limitations of the
adopted methodology will be discussed and suggestions for future research will be
presented.
3.2- Research approach
Research approach is the plan or strategy to be adopted for conducting the
research in order to facilitate the process of gaining new knowledge or to have a better
understanding of the subject or topic being researched (Saunders et al. 2009). There are
two main types of research approach commonly used: the deductive approach and the
inductive approach. Deductive approach research tests or examines if the data of a specific
instance are consistent with an existing theory or hypothesis following a top-down
inferential approach (Thomas, 2006). The deductive approach is mostly adopted in the
research of natural sciences. In contrast, the inductive approach uses data about a specific
instance to draw general concepts or themes following a bottom-up inferential approach
(Thomas, 2006). Inductive approach is mostly adopted in social sciences research.
The inductive approach was adopted in this research study. The reason behind
choosing the inductive approach was because the research seeks to establish a general fact
from a specific instance. Therefore, the research explores the impact of big data mining
solutions on the production efficiency in a specific organization (Petroleum Development
Oman) and then uses the findings to draw a general conclusion for both other functions
within Petroleum Development Oman and other oil and gas companies.
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3.3- Research strategy
Research strategy is the overall plan that the researcher will undertake in order to
meet the research objectives and answer the research question (Wedawatta, Ingirige, &
Amaratunga, n.d.). There are several research strategies adopted by the researchers to
conduct research such as: experiment, survey, case study, action research, grounded
theory, ethnography, and archival research (Saunders et al. 2009). Each of these strategies
has its own advantages and disadvantages when used to research certain topics. Therefore,
it is important to select the appropriate research strategy or combination of them when
conducting research.
Case study research strategy has been adopted in this research study. The selection
of the case study research strategy is driven by two main reasons. First, the study seeks to
explore in an in-depth specific instance (Petroleum Development Oman) to retain a holistic
and meaningful understanding about the impact of big data mining solutions on the
production operations’ efficiency of the oil and gas industry. Second, case study strategy is
the appropriate strategy for the exploratory researches that seek to provide answers to
research questions such as what and how (Runfola, Perna, Baraldi, & Luca, 2016).
3.4- Research design
Research design refers to a detailed outline that focuses on establishing a research
project out of the research questions and objectives (Saunders et al. 2009). The research
project includes the methods adopted to collect and analyze the data. According to
Saunders et al. (2009), there are two methods for data collection and analysis; quantitative
and qualitative. In simple terms, quantitative method refers to the collection and analysis
of numeric data such questionnaires and graphs. In contrast, qualitative method refers to
the collection and analysis of non-numeric data such as interviews and categorization.
Hence, it is important to select the appropriate research design in order to answer the
research question. Research design can employ quantitative method or qualitative method
for data collection and the same is applicable for the data analysis method. However, a
combination of the two methods can be adopted in the data collection and analysis
methods (Saunders et al. 2009).
Qualitative research design was adopted in this research study. This is because this
research study seeks to explore the impact of big data mining solutions on the production
efficiency and the enablers of implementing such solutions at Petroleum Development
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Oman. According to Jamshed (2014), it is more appropriate to adopt qualitative research
design when the research is in a new field of study (big data mining in oil and gas).
3.5- Sampling
The workforce in Petroleum Development Oman(PDO) constituted the study
population from which a representative sample size was to be obtained for this research
study. Judgment sample strategy was adopted in selecting the participants’ sample where
the researcher selected the most productive sample to answer the research questions
(Marshall, 1996). To facilitate the process of data collection, 7 interviewees were
interviewed from different disciplines and levels. This included a production and operation
manager who is responsible for all the production operations of Oman North Oil. In
addition, the head of Real Time Operations team and the head of Nibras development team
were interviewed in order to facilitate the technical data collection about Nibras solution
and its architecture. Moreover, people from production programming, well optimization,
and reservoir management were interviewed. In this respect, all the participants
interviewed have worked for PDO for a period more than 10 years. This facilitated the
process of understanding the use of data in the decision making process before the
implementation of a big data mining solution as well as how the adoption of a data mining
solution (Nibras) impacted the production efficiency. Finally, a member of Nibras
implementation steering committee was interviewed to explore the business derivers
behind the adoption of data mining solution and the key success factors of Nibras
implementation.
3.6- Data collection method
Data collection method refers to the tools and techniques used to gather
information and data from research participants for the purposes of fulfilling research aims
and objectives. Interviews and focus groups are the two common data collection methods
in qualitative researches (Gill, Stewart, Treasure, & Chadwick, n.d.). Although, both data
collection methods are useful, this research study employed interviews as the main tool for
collecting data from study participants. Interviewing was considered an appropriate tool for
data collection in qualitative research because it allows researchers to explore the views,
experiences, beliefs and motivations of participants about the researched topic or subject
(Gill et al., n.d.). In such context of information seeking, interviewer and interviewees are
15
expected to divert from the fixed actual interview points of discussion. This should be
welcomed and encouraged in order to facilitate gathering better quality and valid data as
long as it does not compromise the research objectives, hence the quality of research
results.
There are three types of qualitative data collection interviews: structured,
unstructured, and semi-structured interviews. According to Longhurst & Zealand (2009), in
structured interviews, the interviewer asks a list of predetermined questions the same way
and in the same order to all participants who are not encouraged to deviate from these
questions. In contrast, participants are encouraged to freely discuss the points of discussion
in unstructured interviews. Semi-structured interviews are set between the two extremes
where the interviewer asks predetermined questions but at the same time allows the
participants (interviewer and interviewee) to explore more issues they feel are significant.
Semi-structured interviews were adopted as a data collection method for this
research study. This is because semi-structured interviews support the collection of
sufficient and reliable information as they allow the researcher to explore the subject in as
much depth and at as many angles as required (Longhurst & Zealand, 2009). This is deemed
vital to achieve the research aim and objectives developed for this research study.
3.7- Data collection process and data analysis
The researcher being a PDO employee has made the data collection process easier
and straight forward. However, PDO management has been made aware of the research
aim and objectives prior to the start of the data collection process via e-mail. Once
agreement and support was assured from PDO’s side, the researcher proceeded to design
the interview questions and identify the research participants. This was followed by an
introductory e-mail to candidate participants introducing the researcher and the research
aim and objectives as well as checking candidates’ willingness to be part of the research
study. Upon the reception of candidates’ agreement to be part of the research study, an
invitation e-mail for a semi-structured interview accompanied by the interview questions
and the Informed Consent Form (Appendix 2) was sent to the candidate participants.
Moreover, in the interview invitation e-mail, participants were asked to read, understand,
and ask questions if required prior to the signing of the Informed Consent Form at the
beginning of the interview as an indication of agreeing to be part of the research study.
16
The semi-structured interviews were conducted face-to-face at different meeting
rooms at PDO headquarters office during working hours. During the interview process,
participants were given sufficient time to express their answers and point of view about the
research. Nevertheless, additional detailed questions have been asked to ensure the
researcher’s understanding of the participants’ answers and points of view. All the
interviews were audio recorded and then transcribed by the researcher for further analysis.
Qualitative analysis approach was adopted in analyzing the collected data.
Qualitative analysis approach usually includes summarization, categorization and
structuring of the data in order for the researcher to explore relationships, develop and test
propositions, and draw conclusions (Saunders et al. 2009). Therefore, data obtained from
the semi-structured interviews were summarized, structured, and categorized by the
researcher based on the interview questions and research objectives. Then, from the
differences and similarities identified, the researcher generalized the respondents’
thoughts and ideas to come up with well-grounded conclusions. Finally, the conclusions
reached were compared and backed up by the literature discussed in chapter 2.
3.8- Limitations of the methodology and future suggestions
Although the research methodology adopted in this research study was intended to
ensure the success of this research work, it is still limited in some ways. First, this study
adopted a qualitative research approach for both data collection and analysis. This
permitted the researcher to draw conclusions only analytically but not statistically. Second,
the research study adopted case study as a research study where PDO was studied as a
case company. Given that PDO is a national oil and gas company it may not be a true
representation of the situation in other international oil and gas companies working all over
the world. Finally, having the researcher as an employee in the study’s case company may
introduce a bias to the process of data analysis and therefore the research results.
Given this, it is suggested that future research uses more than one case company to
study. Selected companies could be from different sizes and presence in the industry for
comparative purposes. Additionally, mixed data collection methods (qualitative and
quantitative) can be used as a data collection instrument.
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3.9- Ethical practices
The data collected for this research study depended mainly on a human sample
obtained from PDO’s workforce. PDO – as the national oil company for Oman – is strict
about data confidentiality. Therefore, ethical guidelines have been given the right attention
when dealing with data collection and analysis. Firstly, the researcher ensured no questions
about confidential data were raised during the interviews. This was achieved through
asking interviewees not to disclose any information classified as confidential according to
PDO’s guidelines. Secondly, in data analysis process, the interviewees’ names were
replaced with their position in the organization or with their role in Nibras. Furthermore, all
participants have been introduced to the research aim and objectives before the actual
data collection process to obtain their permission to be part of the research study. Finally,
all data is to be stored securely and not be used in future research without participants’
consent as per the Information School’s ethical guidelines. Appendix 1 contains the
obtained Ethical Approval Form.
3.10- Summary
Inductive approach was adopted in this research study to explore the impact of big
data mining on oil and gas production efficiency. The study adopted case study research
strategy and qualitative research design to answer the research questions. A sample of 7
candidates participated in semi-structured interviews. The qualitative data analysis
approach has been adopted to analyze the collected data. This research also critically
evaluated the limitations during the research process and pointed out the measures to
avoid ethical issues. For example, privacy and anonymity of study participants is protected.
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Chapter 4: Nibras
4.1- Introduction
This chapter presents the big data mining platform (Nibras) that is adopted at
Petroleum Development Oman (PDO). The information presented in this chapter was
collected through the semi-structured interviews with members of Nibras’s development
team as well as a hands-on walk through the platform. First, background about Nibras and
its scope will be discussed. Then, the platform architecture and its components will be
presented. Finally, the big data mining concept of Nibras (EBS) will be explained in a high
level overview.
4.2- Background
PDO has long been focused on exploiting data to optimize production from its
reservoirs, as well as helping it to work more efficiently and economically. One of the first
major initiatives in this area was a data platform called Shurooq. Launched more than a
decade ago, Shurooq provided a web based data visualization (with no analysis) that
allowed users to view a collection of well data gathered from sensors and gauges placed in
and around the wells.
Shurooq was a useful tool but it had its limitations. Shurooq users were required to
spot anomalies which might require further investigation. Even with a trained and
experienced eye this was not always an easy task, given the enormous quantity of data
placed at employees’ fingertips.
In April 2013, PDO lunched Nibras as a successor for Shurooq with more automated
and flexible features. Nibras is a wholly different animal to Shurooq, with many more facets
to what it offers PDO. This is because although it was built in-house, it makes use of a
number of third party solutions that are combined to create a powerful business tool.
4.3- Scope of Nibras
At Petroleum Development Oman, the strategic goal of investing in big data mining
solutions such as Nibras is to utilize technology to bring all corporate data together, analyze
it in an automated way and highlight problem areas by identifying operational exceptions.
These operational exceptions are to be handled in human business processes that are
supported by Nibras with rich data content. The standard business processes are to be
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documented in standard operating procedures (SOPs) and can be monitored against SLAs.
Finally, this gives PDO’s management the tools to further optimize the process using the
LEAN approach and ultimately increase production efficiency.
One of the key strengths of Nibras is that it focuses on strong cross-discipline
process integration in order to ensure organizational alignment and end-to-end business
process focus. This has been driven by PDO’s adoption of LEAN methodology which has its
roots in automotive and manufacturing and which focuses heavily on waste elimination,
standardization and incremental process improvement.
To achieve this, Nibras has three main embedded elements:
Exception Based Surveillance (EBS) which uses a combination of alarms known as
Parametric Alarms, Smart Alarms and Statistical Alarms in order to ensure that operators
and engineers are not flooded with alarms or a duplication of alarms for the same problem.
Automated Workflow (AWF) which supports the standard operating procedures (SOP) that
should be followed to handle all EBS Alarms in a consistent way independent of experience
levels.
Management Dashboard (MD) which is the implementation of the leader standard work
used in the LEAN methodology.
Although, the above three elements are firmly embedded into Nibras core
functionality, AWF and DM will not be covered in this research study as they are out of big
data mining scope.
In order to achieve the main business goals behind the adoption of big data mining,
Nibras has standard building components that enabled and supported the following
functionalities:
1. Access to corporate data sources such as well test data, completion data, work
orders, integrity and process safety data.
2. A standard data platform known as a Data Abstraction Layer links and groups data
between a variety of corporate data sources and implements links between the
data of different disciplines. This is a key enabler to access big data required for
data mining solutions. This is a largely scalable data layer which currently
implements 3 million data points across wells, facilities equipment and
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communication devices. Approximately 750,000 data points with 30 years of
history are readily available for analysis utilizing the data abstraction layer.
3. Exception Based Surveillance (EBS) is a business function based on automated data
mining algorithms that support the following functions:
Parametric Alarms which simply set limits on a single parameter and report an
alarm whenever the acquired value falls outside the limits.
Smart Alarms which are a combination of limits and parameters that highlight
the root cause of the issue instead of a symptom of it. Moreover, the alarms
are extended to be generated based on statistical data analysis instead of raw
values.
Automated Workflows supporting the business processes with pre-defined
roles and responsibilities.
Visual Management functionalities for monitoring the business processes that
enable continuous improvements.
4. Customized components based on big data mining and knowledge extraction
designed to achieve business objectives within specific disciplines such as:
automated well performance review, facilities live limit diagrams, creaming curve
to maximize production for a given constraint, automated configuration tools for
wells commissioning, and customized reporting and trending services.
4.4- Nibras architecture
4.4.1- Overview
Nibras platform consists of different third-party tools and technologies combined and
integrated together to facilitate different functionalities:
Desktop client based on Silverlight.
Mobile client based on native Android.
Data Extraction and Asset Modelling Platform from OSISoft.
Application Databases using SQL Server.
High Performance Application Servers with load balancing on application level and
network level for high availability.
Figure 3 is a high level architecture diagram of the main data sources and application
components that are integrated together in Nibras platform. As shown in the diagram,
there are two main data streams. Firstly, the real time data collected from the sensors
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connected to field devices on a minute-by-minute basis and made available in real time – as
well as history – in the corporate real-time data historian. Secondly, the less or never
changing data such as design or static data sets are stored in Relational Databases and
linked to Nibras through the corporate Data Warehouse.
Figure 3: Nibras platform architecture. (Source: researcher)
4.4.2- Data Sources
For Nibras to support cross-discipline business process, a large variety of data
needs to be integrated and processed from different data sources. Therefore, the purpose
of the data abstraction layer is to integrate with any data sources. As long as the database
is accessible from the office domain, it can be integrated into the solution. Data accessed
by Nibras typically consists of real-time data from the corporate data historian and the data
from other 3rd party applications that are typically relational databases.
Real Time Data source:
PDO standardized on Plant Information (PI) – from OSISoft.com – as a corporate data
historian for the purpose of collecting and storing real-time data from wells and facilities.
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3rd party Application data sources:
There are many datasets from 3rd party applications that are required to be integrated with
the real-time data for the purpose of big data mining in Nibras. However, some of these
applications are doing their own data analysis with their results being presented in Nibras.
These datasets are centrally available in the corporate data warehouse platform. Nibras is
connected to the corporate data warehouse to retrieve the required data from these
databases such as:
(EC) Energy Components for production allocation data.
(EDM) Engineering Data Model for well intervention data.
(ALD) Artificial Lift Database for pump information of artificial lift methods.
(E-WIMS) Engineering Well Integrity Management System for integrity data.
(SM) Sample Manager for chemical sample analysis.
(RegeoNT) Well Database for well bore information.
(OFM) Oil Field Manager for well and reservoir simulation.
(SAP) Company ERP System for financial information and work orders.
(SW) Solar Winds for telecommunication network monitoring.
The Data Warehouse replicates data from these underlying relational databases to
optimize data access performance, accessibility and aggregation. The Data warehouse
structure is optimized specifically for serving Nibras needs.
Nibras Application data sources:
Nibras platform has a number of database instances that are used to store its metadata
such as:
Nibras Database for security and exception information.
Nibras Configuration Database for server monitoring and error logging.
AF Configuration for hierarchical information.
WF Database for Workflow persistent context data.
4.4.3- Data Abstraction Layer
The consolidation of data happens in the “Data Abstraction layer” which essentially
combines data stored in different databases and associates it back to physical equipment in
the field. This will allow making smart interpretations and analysis to see patterns in the
“Big Data”.
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The OSIsoft Asset Framework (AF) implementation defines a hierarchical semantic
structure for all key data in the system. All key data attributes covering subsurface
equipments, wells, facilities and communication equipments are modelled on an Asset
Model for all of the Company’s production operations equipments. Templates are utilized
to ensure consistency of these Data Models across all fields. All Workflow and Service Layer
components of Nibras consume the data through this Data Services Layer for two main
reasons. First, to disconnect the key business logics and data processing from the
underlying data sources. Second, to unify the data access for all services in Nibras platform.
4.4.4- Services Layer
In this layer, the key business services are implemented which consist of:
Exception Based Surveillance Engine to run the core logic based on MS Workflow
Foundation.
Caching Services for time consuming data queries enabling efficient use of the
presentation layer.
Custom data transfer services for Well Test Collection and Daily Volume data
transfers to EC.
Workflow Support services which move a workflow instance to a different state,
based on events outside the Nibras scope.
The output of these services is information ready for consumption by the Visualization
Layer.
4.4.5- Visualization & Reporting Layers
The Nibras Visualization Layer is implemented as a thin client integrated web
portal. This facilitates all interactions with Nibras to be fully embedded within the web
portal. Nibras user interfaces are role based and customized to each individual’s role,
accountabilities and priorities. This makes the Visualization Layer extremely effective in
guiding work processes and setting priorities.
The mobile version of Nibras runs on a tablet called smart mobile worker in which
similar but limited functionalities are offered to the end users working outside the offices in
the field. This provides engineers and operators with up-to-date information at any time,
immediate feedback on actions and enables them to communicate with others and close
the actions.
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4.5- Exception Based Surveillance
4.5.1- Overview
The Nibras EBS Alarm limits and logic are defined by the engineers and operators
responsible for monitoring production operations on a daily basis. This ensures that they
are fit-for-purpose based on the experience of seniors and assure that they are adopted by
the users from day one.
The EBS engine applies these predefined alarm rules and logic using Operating
Envelopes (OE). The Alarms, Logic and Operating Envelopes can be adjusted by user Roles
for each Element.
4.5.2- How EBS Works
EBS integrates all the available corporate data and applies logic or rules such that
alarms are generated, announced, managed and taken towards resolution. EBS works to
ensure:
Alarms are generated when an element faults or moves out of the OE using simple
or complex exception logic.
The OE can only be modified by authorized Roles.
A scheduled checking for alarms is done for quick and automated problem
detection in a consistent and systematic manner.
Call for attention when any alarm is generated which is assigned to an Owner
ensuring nothing goes un-noticed or un-acknowledged.
Assign all exceptions to Roles (individuals).
Facilitate focused, informed and quick decision making to fix problems using
Automated Workflows to investigate and fix the problem.
Help in prioritizing work.
Highlight any issue in the quality of the data used.
Enables a standard way to measure the overall asset health and provide focus at
corporate level.
4.5.3- Alarms Types
Nibras’s EBS engine combines the real-time data streaming from the field devices
with data from other corporate data sources to generate alarms. Alarms are generated
when the element (well, pump, valve, communication device, etc.) measures value moves
outside the defined limits known as the Operating Envelope (OE). The creation and
25
clearance of an EBS Alarm depends entirely on the conditions defined for an alarm. The
applicable alarm conditions vary according to the alarm type. Three types of alarms are
used:
4.5.3.1- Parametric Alarms
Parametric Alarms have a single parameter measured value, which is compared
with a predefined Target/Tolerance or Minimum/Maximum. A Parametric Alarm is
triggered the moment the parameter measured value is outside the defined alarm limits
(OE) as shown in figure 4 below. An example of parametric alarm is the pressure at the
wellhead. EBS engine will generate an alarm once the measured (real-time) pressure
exceeds or goes below the predefined pressure limits.
Figure 4: An example of a parametric alarm. (Source: researcher)
4.5.3.2- Smart Alarms
Smart Alarms typically use measured values of multiple parameters about the same or
connected equipments. They are designed based on analytical diagnostics where a logical
combination of two or more parameter values moves outside the OE Alarm Limits. Smart
Alarms are generally characterized as having the following features:
The alarm trigger conditions consider more than one parameter measured value to
identify a possible issue on an element using AND/OR logic.
26
All the possible alarms needed on an element are arranged in a sequence to
eliminate the possibilities of multiple alarms for the same element being triggered
for a single root cause.
A Smart Alarm is triggered the moment the measured values about an element satisfy
the trigger logic moving outside the defined alarm limits (OE) as shown in figure 5 below.
Moreover, on some occasions alarms are raised after a certain delay time to avoid noise in
the alarms.
Figure 5: An example of smart alarm. (Source: researcher)
4.5.3.3- Statistical Alarms
Statistical alarms triggered based on variable operating limits. The operating
envelope is calculated based on variance of historical data. When data has been stable
historically, then the OE band becomes narrow and process upsets will trigger on small
process upsets. When the parameter has been noisy or unstable in the past, then this is
seen as normal conditions and the OE band will automatically become wider.
27
Figure 6: An example of statistical alarm. (Source: researcher)
4.5.4- EBS Alarms Life Cycle
EBS alarms may be triggered by any of the three alarm types as described in the
previous section and provide focus on elements working outside their OE or are in an alarm
condition in the process of resolution. The Elements in an alarm condition need:
Timely acknowledgement by the asset owner and
Either investigative or corrective actions to be taken by the action party to bring
the element back into an acceptable OE.
Both actions above are to be accomplished using the automated workflow (AWF) engine in
Nibras and measured against the defined SLA.
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Chapter 5: Research results and findings
5.1- Introduction
The data and information gathered from the interviews conducted with the
research respondents at Petroleum Development Oman (PDO) will be presented in this
chapter. The main topic of the interviews was the impact of big data mining on the oil and
gas production efficiency. However, this chapter will present the data and information
gathered about the decision making process in PDO’s production operations before
Nibras’s implementation, the main drivers to implement Nibras, the key enablers for Nibras
implementation, the impact of Nibras implementation on the efficiency of different areas
of production operation, and finally data and information gathered about tools and
practices to ensure data quality will be presented.
5.2- Decision making process in PDO production operations before Nibras
implementation
Although the production operations practices have not changed significantly in PDO
during the last 20 years, there have been different tools used to support the decision
making process. These tools were designed either to fulfill the requirement of a specific
discipline in production operations or customized for a specific team in PDO. Nevertheless,
the impact of utilizing these tools on increasing the production efficiency was not
recognized.
5.2.1- Production Programming
Production programming is mainly depending on data to achieve its objective of
scheduling and optimizing production and injection operations with a careful consideration
to Well, Reservoir, Facility, and Safety. Therefore, all the necessary information required to
make any decision needs to be available before implementing it in the field. For that reason
the decision making process was slow before Nibras implementation as most of the time
was spent in collecting and analyzing the required data. The Head of South Production
Programming said “with the old system “Shurooq” I have to check 2000 wells one-by-one by
looking at the trends”.
In another proposition, production programming needs to monitor the production
processes from different aspects that impact the production. To achieve this, information
and investigation results from different disciplines need to be combined together in order
to make the right decision. It was difficult to make timely decisions without this information
29
available with the production programming team. As stated by the Head of South
Production Programming: “Most of the time we were in doubt, and if we are not quite sure
about the status in the field, we have to send someone to check and come back with the
right information.” In addition to that, the collaboration between different disciplines was
difficult as each team has its own data in a separate application.
5.2.2- Well and Reservoir Management
Well and reservoir management in the oil and gas industry is about long-term value
maximization. Well and reservoir management teams are responsible for all optimization
decisions, manage existing assets, and ensure the delivery of the remaining reserves and
production. To achieve this, it requires a structured and integrated approach to gather
data, analyze/model data, review information, and make decisions to optimize the asset
value. In PDO, Well and Reservoir management was not possible with Shurooq as it was
providing dashboard functionality only. Reservoir engineers were required to manually
gather data from different disciplines in order to know the status of the wells and
reservoirs. As stated by the reservoir engineer interviewed “knowing how much we
produce, knowing how much we inject, knowing how many wells we have, used to take days
before Nibras”. For that reason, many reservoir management decisions used to be made
based on peoples’ experience and competency. The Petroleum Engineer interviewed said
“before Nibras, quite often, the data that I need to make decisions – at least on the well
level and definitely reservoir level – were simply not available”.
Another aspect that was affecting the decision making process in PDO before
Nibras was the time spent in order to be able to make a decision about wells and
reservoirs. The Petroleum Engineer said “in a survey conducted in 2010 – where 100
petroleum engineers responded – we asked them: what do you spend your time on? The
answer was 53% of their time was spent going to the different data sources, extracting the
relevant information, putting it together in Excel in a certain way, and then analyzing it in
Excel in order to come up with a decision”
5.2.3- Facility management
Facility management is mainly concerned with making sure that the produced fluids
are transferred from the wells, processed correctly, and shipped or injected back optimally.
To achieve this, production operation teams are required to have close eyes on the
production process 24 hours a day 7 days a week using the data collected about the
30
production facilities. Therefore, production operation teams need to effectively coordinate
their activities with other teams and disciplines. PDO’s production operations teams were
suffering from a lack of such coordination before Nibras implementation. The North
Operation Manager mentioned a few issues causing such lack of coordination and said:
“information was not readily available, was not accessible, the accuracy of data was
questionable, and the ability of all teams to read the same information was not applicable”.
5.3- Main derivers to data mining solution (Nibras)
Throughout the number of interviews conducted, below are the main derivers behind
the adoption of Nibras as a platform heavily dependent on big data mining:
1. More complex operations: PDO started to produce oil from very complex reservoirs
using Enhanced Oil Recovery technologies. This imposes a lot of challenges at
different levels of the company such as reservoir and facilities. Hence, having a clear
insight about the production operation processes has been a must to sustain the
business.
2. Workforce turnover: The oil and gas industry has been expanding during the last
few years. This has resulted in a large number of brains with valuable knowledge
leaving PDO to work for other companies.
3. Collaborative Working Environment (CWE): PDO changed its business model to
enable more collaboration between different teams in different business areas.
Collaboration centres were designed to facilitate experts and engineers from
different production operations disciplines at the headquarters working together
and looking at the same screen. Therefore, it was essential to have a standard
platform that could integrate information from different data sources, automatically
analyze it, and visualize it so experts and engineers from different disciplines could
discuss it.
4. Efficiency: This is to ensure that production data stored at different databases is
mined to extract the necessary knowledge out of it. This is to enable maximizing
technical expertise through the standardization of data analysis across all production
operation teams in the company. In addition to that, efficiency increases through
the reduction or even the elimination of time and resources spent on data
gathering, analysis, and communication.
5. Increase information availability to make timely decisions: With this, PDO was
looking for a platform that integrates data from different data sources concerning
31
production operations. On top of that, such a platform should enable the
transformation of this data to information and knowledge.
5.4- Key enablers of Nibras implementation
Although Nibras was initially adopted and developed for one production operation team
in PDO, it proved successful and was adopted across all production teams in PDO very
quickly. The interview participants believed that the enablers of Nibras implementation are
around four areas:
1. Management ownership: The production operation management team was a strong
believer in the power of the data and the value that can be derived once it is utilized
correctly. It was a very challenging task to convince people working in the field to
rely on information to run the business. However, with the support of higher
management, Nibras proves that information was able to provide better insights
compared to data.
2. Dynamic system architecture: Nibras’s architecture has been designed to be able to
pull the data from different data sources for the purpose of mining and knowledge
extraction. This has enabled different production operation teams to pilot their own
data mining solution through the same platform. Once a solution proved successful,
other teams adopted it. In response to the question, what do you think Nibras
implementation success factors are? the interviewed Reservoir Engineer stated that:
“I think the key success factor is the fact that Nibras can help people in their daily job
and people own the solution they develop in Nibras and have their own success story
to be shared with other PDO teams.”
3. Specialized skills set of technology and business: The close coordination between
the Nibras development team and production operation process owners was a
cornerstone to the wide acceptance of the solutions. The Nibras taskforce was able
to establish a strong alignment between IT and business.
4. Evolutionary journey: the approach that has been followed to implement Nibras in
different teams and in different areas of production operation has a big impact on
the quick adoption of the solution. Nibras’s implementation approach was to
provide a solution that helps people to do their job instead of telling people what
they should do. This has been highlighted by the Petroleum Engineer interviewed
who is also a member of Nibras’s steering committee. When he was asked about the
32
key enablers of Nibras implementation, he said:” I think a lot of the other solutions
come and tell people that we are the best solution, all you need to do is to implement
it. But the Nibras approach was: you know best how to manage your asset, we will
help you to do that more easily, simply, and systematically.”
5.5- Areas of Nibras impact
There are several areas and processes where data mining concepts and solutions
have been adopted under the umbrella of Nibras. Nibras’s tools and solutions have been
adopted at different levels of the production operations business. They range from field
technicians at remote locations to the managing director at the headquarters. When asked
about the extent of Nibras’s impact in the production operation, the North Operations
Manager stated: “Nibras has become an essential component of our daily business and
people just can’t do their job without it.”
5.5.1- Production Programming
With a platform that integrates information concerning the production operations
from different sources, production programming teams have more confidence in the
information based decisions they make on a daily basis. This is because the data used in
Nibras is maintained by engineers from different disciplines in which everyone is ensuring
the required quality of data about his\her area of expertise is always maintained. As a
result of that, production programming teams can often eliminate the need for a physical
well or equipment inspection. The head of south programming teams explained that in his
answer to the question about the impact of Nibras in his area. He said: “In a recent planned
field shutdown, I was able to monitor how quick the wells are recovering, and also I can
book the correct deferment without sending anybody to the field.” He also added that the
time the production programming teams used to spend looking for data and analyzing it,
they can now spend on more production optimization activities.
In another application of big data mining, Nibras enabled the production
programming teams to predict failures of wells and equipments ahead of time. This gives
the teams enough time to plan for the fix so none productive time (NPT) of the wells and
equipments is kept at a minimum. The head of south production programming emphasized
the role of Nibras in reducing the NPT when he said: “In the past, it was very difficult to
know that a well was having a problem, but now you can see that there is an alarm. You can
still keep producing, but you also have some time to plan for the fix.”
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5.5.2- Well and Reservoir Management
Switching from the traditional well and reservoir management to Exception Based
Surveillance (EBS) via Nibras platform has completely transformed the way PDO manages
its wells and reservoir. This can be noticed through the reduction in the decline rate of
production from PDO’s fields. Similar to any oil and gas fields in the world, the production
from PDO’s fields were declining at a certain rate before Nibras. However, after the
implementation of different data mining solutions through Nibras, PDO managed to reduce
the decline rate by more than 4.2% per year. This was highlighted by the petroleum
engineer we interviewed. He said: “In 2012 when we were at 9% decline rate, last year, we
were up to 4.8% decline rate which is unheard of in the industry for a company 45 years old.
Not only that, in some areas we are predicting to have a negative decline.” The fact is that
Nibras is not adding any oil to PDO’s reserves, but it enables PDO to produce more from its
reservoirs than it did before. This is due to two main reasons: proactive approach in well
and reservoir management and time.
One of the main changes Nibras introduced to well and reservoir management
teams in PDO is the approach adopted to managing wells and reservoirs. Switching from a
reactive approach in managing the wells and reservoirs to a proactive approach has a
significant impact in terms of cost and time as highlighted by the reservoir engineer
interviewed. When she was asked about the impact of Nibras in her area, she said: “With
Nibras, you know better what is happening and then you are more in control. And that
means you are on the proactive side rather than the reactive side.” This was also supported
by the petroleum engineer interviewed when he said: “With Nibras, a reservoir pattern that
you could miss for six months – and consequently damage the reservoir – can now be
recognized in one month.”
The other factor that Nibras brought to impact on the well and reservoir
management in PDO is time. Previously, well and reservoir teams used to spend around an
hour each morning searching for problems. Using Nibras, every engineer will get a report of
problems prioritized according to their severity. The petroleum engineer interviewed said:
“In a survey conducted in 2010, our petroleum engineers and reservoir engineers spent
more than 53% of their time collecting data and analyzing it. When we repeated the survey
5 years later, that 53% went down to 14%. So this is a significant release of engineers’ time
to focus on solving the problems rather than finding them.”
34
5.5.3- Facility management
The adoption of Nibras enabled production operations teams to have their hands
on information about not only the production facilities but the full loop of production
processes around the facilities. This enabled different teams around the production
facilities to have more control over the process and be able to react timely to mitigate risks.
The North operations manager highlighted this when he said: “With Nibras nowadays, I can
know if there are any integrity issues either on the wells side or the facilities side. This
allows us to act on them at the right time.” As a result of such insight into production
facilities via Nibras, production operation teams have more confidence in their diagnosis
and are able to eliminate the need for physical inspection of production facilities.
Furthermore, with different big data mining solutions available through Nibras,
production operation teams are receiving optimization alarms on a daily basis. Production
optimization alarms are triggered by the Nibras EBS engine and sent to the appropriate
team associated with all supporting information like historical trends and facility
schematics. This allows production operation teams to run the production facilities’
exercises on a daily basis rather than monthly basis.
5.6- Data quality assurance
The quality of the data used in the decision making process has always been a
concern in PDO and the oil and gas industry. This is due to the high risk associated with
both long-term and short term decisions. Therefore, big data mining solutions and tools in
Nibras have been adopted to ensure data quality from different dimensions and at different
levels such as:
Real-time data: As shown in figure(***), the real-time data utilized in Nibras is collected
from two main sources: production facilities’ control systems and wells production system.
About one million measurement points are configured in the corporate data historian to
store the real-time data streaming. When asked about how Nibras users can trust the
alarms triggered by Nibras, the head of Nibras’s development team replied: “Nibras checks
the quality of the real-time data stream before analyzing it for operational EBS alarms. This
is to ensure that the production operation alarms are based on good quality data.” The
Nibras EBS engine is utilized to highlight any data quality issues in the streaming of real-
time data from two perspectives. First, the EBS engine analyzes the streaming data and
classifies it as good or bad. If the data stream is classified as bad, then it will be highlighted
and visualized as bad whenever used in Nibras. Second, all the real-time data streams that
35
have bad data quality are mined via the EBS engine to point out the area of the fault. As a
result, an alarm will be raised to the team or engineer responsible for fixing that issue.
Corporate databases: the alarms triggered by Nibras are – most of the time – based on
data from different corporate databases. During the alarm diagnosis process, different
datasets from different databases are brought together for analysis. This process always
highlights any data quality or inconsistency in any of the corporate databases. The head of
south production programming commented on this aspect when he said: “I can spot if the
data is reasonable or not. From all the information on my screen, I can tell if a value is
correct or not.”
Corporate hydrocarbon accounting figures: Accurate production allocation was a long-
term challenging issue in PDO. Before the adoption of Nibras, it was difficult to know the
amount of production that came from which well or which reservoir. This was because of
the uncertainty about the status of the well. A well could be reported as producing for
many days before the production operations team came to know it had stopped. The team
knows the total production of the field, but not from which well or which reservoir. With
Nibras, wells’ issues are highlighted in real-time and in exceptional basis. Therefore, the
production can be easily allocated back to reservoirs and wells. In this regards, the
petroleum engineer interviewed said: “Nibras has helped to improve the reconciliation
factor by 5% to 10%. Which means that our data accuracy for the future, for forecasting,
and for making better decisions in terms of where we invest really has been improved by
Nibras.”
5.7- Summary
The findings in this chapter show that the decision making process in PDO before
the adoption of the big data mining solution “Nibras” was characterized to be slow and of a
reactive nature. The complex production operation, workforce turnover, the need to work
collaboratively, and the focus on efficiency improvements were the main derivers behind
PDO adoption of big data mining solution “Nibras”. Management ownership, the dynamic
system architecture, and the specialized technology and business skill set were found to be
the key enablers of Nibras implementation. The main impact of Nibras on different areas of
production operations was clearer insight into the full business process with more free time
to fix issues rather than trying to find them. Most of the interviewees highlighted that there
is a recognized average daily production increase of 3 to 5 percent as a result of adopting
big data mining solution “Nibras”.
36
Chapter 6: Discussion and Analysis
6.1- Introduction
This chapter analyzes the data and results presented in Chapter Five. The chapter
begins by analyzing and discussing the findings about the decision making process at PDO’s
production operations before the adoption of Nibras. Then, the results about the main
derivers and enablers of Nibras’s implementation are discussed. Finally, the impact of
adopting Nibras’s solution on the production efficiency is analyzed and discussed. The
findings are validated through theoretical propositions in literature reviewed in Chapter
Two.
6.2- Decision making at PDO’s production operations before the adoption of
Nibras
PDO started the journey of digitizing its production operations a long time ago.
From the research, it is evident that PDO’s production operation teams were collecting and
storing large amounts of data each for its interests. Although there were several initiatives
to use the collected data to improve the production operation business, these initiatives
were scattered to serve specific needs. There was no common platform where information
about different areas of production operations were integrated and analyzed semantically.
Therefore, building a comprehensive insight about production operations was to be done
by manually collecting, integrating, and analyzing data from different sources. As
highlighted by Baaziz & Quoniam (2013), without such a common platform, the decision
making process will be slow and not always accurate.
As a result of missing a common platform for data integration and analysis, the
data gathering and analysis part (in the decision making process) consumed most of the
experts and engineers’ time. Research results revealed that engineers spent about 53% of
their time gathering and combining data from different sources. The time and efforts
dedicated to data gathering and analysis was replicated across all different teams trying to
achieve the same objective. This means more time was wasted in finding issues with the
process and less time to fix them. However, most of the time, each team had a different
picture or conclusion about the same subject.
Although large amounts of data about production operations in PDO were
collected, the value out of it was missed. From the research, it was evident that PDO’s
production operation teams were adopting a reactive decision making approach instead of
37
proactive approach. Data was used to find issues after they happened (for example: a well
stopped producing). This resulted in loss of production due to high Non Productive Time
(NPT).
6.3- Key derivers and enablers of Nibras implementation at PDO
From the research, there were five main derivers that motivated PDO to adopt big
data mining solutions: complex processes required to run the new enhanced oil recovery
operations, emigrant brains and knowledge to other companies, the need to work more
collaboratively, the need to improve production efficiency, and the need to have more
insight in order to make the right decision. All these derivers were centred around PDO’s
ability to maximize the use of data to increase production operations efficiency. This is in
line with Feblowitz (2012)statement about the main reason motivating oil and gas
companies to adopt big data mining solution.
From the interviewees’ responses, it was established that there were four main
factors which enabled the successful implementation and adoption of big data mining
solutions at PDO. These factors are: management ownership of the concept, the
evolutionary approach that has been taken to implement the solutions across different
teams and units in PDO, the dynamic infrastructure of Nibras, and the specialized skills set
employed to derive the solution implementation and maintenance.
6.4- The impact of Nibras on production operations at PDO
From the research, it was established that adopting big data mining has positively
impacted the efficiency of PDO’s production. The positive impact can be recognized by the
increase in the average daily production by 3 to 5 percent in comparison to the average
daily production prior to the adoption of Nibras. According to the research interviewees,
the production increase was mainly due to the increase in the production efficiency at
different areas of production operations. However, production efficiency increase was
allowed by more insight driven from the data, more collaboration of information and
activities, faster decision making and more control over the production processes.
6.4.1- More insight
The research revealed that Nibras enabled PDO’s production operation teams to
have more insight into their field operations. Nibras does not only integrate information
about different areas of production operations but also applies different data mining
techniques to derive more insight out of it. As it has been highlighted by Cowles (2014), the
38
stronger the insight about different parts of the production system the more efficient the
production operations will be. Therefore, the insight Nibras delivers to PDO’s production
operation teams at different levels significantly impacted PDO’s production efficiency.
6.4.2- Collaboration
PDO realized the challenges associated with the production of hydrocarbons from
the remaining difficult fields in Oman using enhanced oil recovery methods. This
necessitates dramatic changes in its business model part of which are the collaborative
monitoring centres. These are centres where experts and engineers from different teams
and disciplines sit in the same room at headquarters to discuss and make real-time
decisions in collaboration with field workforces. From the research, it was evident that
Nibras was a key enabler of these collaborative monitoring centres. According to Brulé
(2013), a platform that consolidates and mines data from different data sources is a key
requirement to streamline the collaboration of multidisciplinary teams. From the research,
it was established that Nibras is integrating and analyzing data from different sources that
are of interest to different teams and disciplines for the collaborative decision making.
6.4.3- Less time on data gathering and analysis
Due to the cost and risk associated with decisions they made, production operation
teams usually don’t make any decision without the necessary information in place. From
the research, it was clear that production operation teams were able to reduce the time
they spend in gathering and analyzing data necessary for decision making. Moreover,
Nibras enabled the standardization of the datasets to be integrated as well as the analysis
process to be adopted across all production operation teams in PDO. This enabled the
replication tools and solutions developed by one team to other teams across the company.
6.4.4- More confidence
From the research, it was established that production operation teams have more
confidence in the decisions they make based on Nibras. This is due to the trust they have in
the quality of information delivered by Nibras. Such trust was established on two
dimensions. First, the different datasets that Nibras integrates for multivariate analysis.
This enabled the data quality cross checking. Second, Nibras utilized its EBS engine to test
and verify the quality of the data stream it integrates and analyses. As a result, Nibras
highlights any data quality issues on the information presented for the decision makers.
39
6.6- Summary
From the analysis in this section, it is revealed that adopting big data mining
solutions had a positive impact on oil and gas production efficiency. It is evident that the
data mining solution “Nibras” enabled production operation teams at PDO to have more
insight into the production operation processes and activities. Moreover, Nibras enabled
more collaboration between different teams and disciplines at PDO. In addition, the
standardization and automation of data integration and analysis saved PDO time and
efforts and established more confidence in the information delivered by Nibras for decision
making. As a result, PDO was able to change its business model from reactive to proactive.
This has resulted in an increase in the average daily production and decrease in none
production time (NPT).
40
Chapter 7: Conclusion
This research study investigated the impact of adopting big data mining on oil and
gas production efficiency. The adopted big data mining solution “Nibras” at Petroleum
Development Oman (PDO) was explored as a case study. Key stakeholders of Nibras at PDO
were interviewed via a series of semi-structured interviews to collect the data required for
this research study. This chapter of the study is provided to sum up the findings in chapters
four, five, and six. Moreover, it discusses the limitations of this study. It also presents some
recommendations for the future studies in this research area.
7.1- Conclusion
Throughout the literature review, it was noted that oil and gas companies collect
and process large volumes of data since long time. The main objective to collect data about
almost everything was to mitigate risks associated with hydrocarbons exploration and
production processes. Although there are several case studies of deriving business value
out of the collected data, literatures revealed that oil and gas companies lack of solutions
maximizing the value extraction from big data. Nevertheless, many literatures presented
areas of potential uses of big data mining in oil and gas industry.
The data collected about the researched case study (PDO) highlighted that PDO was
not an exception from other oil and gas companies with regard to the large volumes of data
collected. Different datasets collected and stored in different data repositories at PDO.
These datasets were used by different production operation teams for their daily decision
making without further integration or analysis. Therefore, in such business model, decision
making process lacked the comprehensive insight and characterized to be slow and
reactive.
Further, research data revealed that several challenges derived PDO to invest in
maximizing the value of data by adopting big data mining solutions. Mainly, the complex
hydrocarbon extraction operations PDO engaged with and the aging workforce that
necessitated more collaboration between different teams and disciplines. Moreover, PDO
adoption of the proactive production operation management model which required more
information to be made available in order to make timely decisions.
Four reasons were highlighted by the research study as the main enablers of big
data mining solution “Nibras” at PDO. As per the data collected in this research study,
management ownership, the dynamic infrastructure of the platform, the specialized skills
41
set employed and the evolutionary approach adopted to implement the solution were the
key enabler of successful implementation and adoption of the solution.
The impact of adopting big data mining was recognized in the increase of
production efficiency at PDO after Nibras implementation. The research study revealed that
adopting big data mining resulted in more insight about production operations and
processes, more collaboration between teams and disciplines, and more confidence on the
decisions across all functions and teams of production operations at PDO. Moreover, it
reduced the time spent on data gathering and analysis and allowed more time to be spent
in production optimization. The ultimate result was more efficient production operations at
PDO.
7.2- Research limitations and Recommendation for Future Research
Despite the achievements made by this research study in fulfilling the outlined
research aim and objectives, there existed some limitations. Firstly, this research study was
only limited to Petroleum Development Oman (PDO). Although the company provided an
ideal context for the researcher to obtain enough data for analysis, the findings and
conclusions reached cannot be fully generalized to oil and gas industry. This is based on the
fact that PDO is a national oil and gas company and known of its high adaptation of new
technologies in its hydrocarbons extraction processes. Additionally, the researcher only
used interviews to collect data from interviewees. However, interviews are prone to bias.
Especially giving that the researcher is working at the same company of the case study. As
such, it is suggested that future research consider combining interviews and questionnaire
as data collection methods from different oil and gas companies to address this limitation.
42
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Appendix 1: Ethical Approval Form
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Appendix 2: Informed Consent Forms
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49
50
51
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Information
School.
Access to Dissertation
A Dissertation submitted to the University may be held by the Department (or School) within which
the Dissertation was undertaken and made available for borrowing or consultation in accordance
with University Regulations.
Requests for the loan of dissertations may be received from libraries in the UK and overseas. The
Department may also receive requests from other organisations, as well as individuals. The
conservation of the original dissertation is better assured if the Department and/or Library can fulfill
such requests by sending a copy. The Department may also make your dissertation available via its
web pages.
In certain cases where confidentiality of information is concerned, if either the author or the
supervisor so requests, the Department will withhold the dissertation from loan or consultation for
the period specified below. Where no such restriction is in force, the Department may also deposit
the Dissertation in the University of Sheffield Library.
To be completed by the Author – Select (a) or (b) by placing a tick in the appropriate box
If you are willing to give permission for the Information School to make your dissertation available in
these ways, please complete the following:
√ (a) Subject to the General Regulation on Intellectual Property, I, the author, agree to this dissertation being made
immediately available through the Department and/or University Library for consultation, and for the Department
and/or Library to reproduce this dissertation in whole or part in order to supply single copies for the purpose of
research or private study
(b) Subject to the General Regulation on Intellectual Property, I, the author, request that this dissertation be withheld
from loan, consultation or reproduction for a period of [ ] years from the date of its submission. Subsequent to
this period, I agree to this dissertation being made available through the Department and/or University Library for
consultation, and for the Department and/or Library to reproduce this dissertation in whole or part in order to
supply single copies for the purpose of research or private study
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Name: JAIFAR AL HINAI
Department : Information School
Signed JAIFAR AL HINAI Date: 29/08/2016
To be completed by the Supervisor – Select (a) or (b) by placing a tick in the appropriate box
THIS SHEET MUST BE SUBMITTED WITH DISSERTATIONS BY DEPARTMENTAL
REQUIREMENTS.
(a) I, the supervisor, agree to this dissertation being made immediately available through the Department and/or
University Library for loan or consultation, subject to any special restrictions (*) agreed with external organisations
as part of a collaborative project.
*Special
restrictions
(b) I, the supervisor, request that this dissertation be withheld from loan, consultation or reproduction for a period of [
] years from the date of its submission. Subsequent to this period, I, agree to this dissertation being made available
through the Department and/or University Library for loan or consultation, subject to any special restrictions (*)
agreed with external organisations as part of a collaborative project
Name
Department
Signed Date