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Completion Course Work

Digital Management of Oil Fields

Executive Post Graduate in Oil & Gas

March 13, 2007 / February 28, 2008

18ª Class

Coordinator: Suzana Kahn Ribeiro

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Summary of work submitted to the COPPE / UFRJ as part of the requirements for

obtaining the Diploma of Specialization in Executive Post Graduate in Oil and Natural

Gas.

DIGITAL MANAGEMENT OF OIL FIELDS

Flávio Ferreira da Fonte

February/2008

Advisor: Prof. Gilberto Ellwanger

This study aims to examine the emerging technologies being employed in the

Digital Management of Oil Fields, which aims to maximize production, increase the rate

of recovery of oil and optimize the costs of exploration and production.

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Curriculum Summary

The author, Flavio Ferreira da Fonte, has been working at Oracle since 2004 as a

Senior Sales Consultant and expert in technology solutions from Oracle to customers

such as Petrobras, PDVSA and PEMEX. In June 2007 attended the Oil & Gas Oracle

Global Industry Business Unit training. He also participed in the development of a

Business Intelligence Dashboards for the Upstream area.

Mr. Fonte worked at Petrobras in the Information Technology area from 2000 to

2004 onn Downstream systems. He also participated in the implementation and

deployment of the Petrobras e-Marketplace, called Petronect and participated in the

ISO-9001 certification process at Petrobras IT.

Graduated in Technologist in Data Processing, the author also completed the

courses of specialization of Systems Analysis and Post-Graduate at IAG Master, both

from Catholic University from Rio de Janeiro (PUC-RIO).

The author is doing an MBA at IBMEC Business School at Rio de Janeiro.

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Acknowledgements

I want to thank my family and co-workers from Oracle, Andres Prieto, David Shimbo,

Miguel Cruz, Eduardo Lopez, Elizabeth Faria, João Fernandez and Samy Szpigiel for

the support received.

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Index

1. Introduction ..........................................................................................................................6 2. Analysis of key technologies used in Digital Oilfields projects.....................................8

2.1.Gathering information in real time...............................................................................8 2.2.Information Management ...........................................................................................15 2.3. High Performance Computing ..................................................................................17 2.4. Centers of command and remote monitoring ........................................................18 2.5 Sistems for analysis and simulations of hydrocarbon reservoirs .........................20 2.6. Systems for analysis and decision support ............................................................22

3. Conclusions........................................................................................................................28 References .............................................................................................................................30

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1. Introduction

The world`s geopolitical dependence on fossil fuels, the duration of hydrocarbon

reserves, the rapid economic expansion of China and India, and a complex petroleum

refining and supply chain have caused oil prices to skyrocket.

With the price of oil above US$125/Bbl, companies are investing more in research

and development of new technologies to improve recovery and reduce operating costs.

Chevron has spent more than $5 billion of its budget in the past 5 years on industrial

automation and information technology.

Despite this large capital investment, the oil industry still has a shortage of

operational resources, such as drilling rigs and production platforms and associated

skilled oilfield workers. These scarce resources are now being leased or constructed

and immediately utilized, thereby increasing the costs associated with exploration and

production (E & P) operations.

Given this competitive environment, oil companies need to optimize profitability and

reduce operating costs. The major oil companies began increasingly to innovate and

implement projects with intensive use of automation and information technologies in

the area of E & P, aiming to mitigate operational risk, accelerate production, improve

recovery of reserves and optimize costs.

These projects which integrate operational, technical and financial data are called

"Digital Oilfields" [1]. Examples are: Shell, with Smart Fields [2], BP [3] with the Fields

of Future (which envisages achieving the goal of 1 billion barrels by incremental use of

new technologies) and Chevron [4], with the i-Field.

In Brazil, Petrobras is running a Digital Oilfield programme called GeDIg [5]. This

program provides the integrated management of E&P production processes through

the use of oilfield information, automation, modeling and simulation technologies to add

value to E&P assets.

In this project six pilot programmes are being evaluated, seeking to establish

standards and "benchmarks" which are most suitable and profitable for several different

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types of oilfields – deepwater offshore, shallow water offshore, onshore brown field,

heavy oil, etc.

The main topics discussed by GeDIg are: the testing of software provided by oilfield

service companies (Halliburton / Landmark, Schlumberger, etc.); recommending new

oilfield procedures and workflows; implementation of remote centres of operations;

assessment of intelligent completions; optimization of artificial lift, and management of

real time operational data.

The figure below shows the vision of technology of Chevron and Shell for these

Digital Oilfield projects [6], which includes obtaining information in real time, information

management, high performance computing, visualization systems, reservoir simulation,

centres of command and remote monitoring, analysis and decision support systems.

FIGURE 1

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Digital Oilfield projects are one of the major strategic initiatives for all oil companies

The technologies used must be innovative and are often borrowed from other

industries so this new technology must be analyzed and studied before being used.

The next topic details the main technologies used in these Digital Oilfield projects.

This information can be used as an initial guide for those who wish to study this issue

or work on related oilfield automation projects.

2. Analysis of key technologies used in Digital Oilfields projects

2.1. Real Time Information

Oilfield operational events, such monitoring the operation of a turbine, must be

captured and mapped in a technology platform that enables real time monitoring,

analysis and decision-making.

Several emerging technologies such as radio frequency identification (RFID),

sensors, Wi-Max, and satellites are being used to obtain real time information. For the

acquisition of data from oil wells in real time, companies are upgrading their facilities

infrastructure, implementing process control systems and installing sensors and fibre

optic across platforms and risers.

During the drilling of wells, the sensors are used to obtain information about drilling

in real time. These real time drilling systems can read the pressures, formation density,

torque, vibration and so on.

Figure 2 shows LWD (Log While Drilling) equipment capable of obtaining

petrophysical information (well logs) during drilling.

FIGURE 2

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With real time logging and drilling information, engineers can identify geologic

formations immediately and determine if that formation contains oil and/or gas.

In this case, the operational challenges for downhole sensors include high wellbore

temperatures/pressures, corrosive fluids that can damage the downhole sensors, and

mechanical abrasion that can physically damage the equipment

In the production phase, these sensors monitor the production of oil, gas and water

versus cumulative time and volume; differences in downhole pressure versus wellhead

pressure; the flow efficiency of artificial lift systems; etc.

The figure below shows two pressure and temperature sensors specifically

designed for the monitoring of the bottom of oil well. This sensor is a version of the

PDG conventional optical fiber (Permanent Downhole Gauge).

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FIGURE 3

The fiber optic sensor technology has been developing rapidly in recent years.

The main reasons for implementation of these sensors in the systems of

measurement are inherent characteristics of the optical fibres such as low weight,

flexibility, long-distance transmission, low reactivity of the material, electrical insulation

and electromagnetic immunity. Besides these, in many cases there is the possibility of

multiplexing the signals from several sensors, including various ampliitudes along the

same fiber sensor. These technological advantages that contribute to the fibre-optic

sensors will replace the conventional sensors in various applications. [7]

As an example of the use of these sensors in the petroleum industry, the

Norwegian company StatoilHydro [8] uses a system called Catamaran TurboWatch,

supplied by the company Shipcom Wireless [9], which tracks more than 200 devices on

eight oil platforms in the North Sea. This system collects operational information from

various machines and feeds other business and maintenance systems for the

company.

Figure 4 shows screens monitoring equipment from Catamaran TurboWatch

system.

FIGURE 4

Figure 5 identifies the eight Statoil platforms using the Catamaran TurboWatch

system.

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FIGURE 5

The sensors can be installed on the wellhead, in the production tubing and on other

wellbore equipment. The data collected by the sensors is transferred to supervisory

systems devices called SCADA (Supervisory Control and Data Aquisition). In the

SCADA system each sensor is seen as a single "tag", or a unique identifier, which

gather and stores operational data.

In addition to the SCADA system, some companies use other layers of software to

maintain a history of these measures obtained. Usually the first interface with the

SCADA system is made by a data historian. One of the historians systems currently

used is called OSI / IP, from OSIsoft company [10].

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However, beyond this data historian layer, companies also use a relational

database manager system (RDBMS), which stores all the information related to fields

and wells into relational tables.

Currently, Oracle’s RDBMS (Relational Database Manager System) [11] is the most

widely used data base that stores critical E&P information for oil companies.

Figure 6 shows the flow of information between the SCADA systems, OSI / PI and

RDBMS.

FIGURE 6

Figure 6 also shows that it is necessary to have a layer of applications that create a

user-friendly experience. In this case, the use of a personalized portal on the company

Intranet is strongly recommended to unify all systems and applications that the user

needs as a single point of interaction.

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This kind of information portal can be developed by the company customized to

meet specific oilfield requirements or can be provided by oilfield service companies

such as Landmark [12] or Schlumberger [13].

Figure 7 shows an offshore process control centre, provided by the company ABB.

FIGURE 7

Each type of well (mature, light oil, heavy oil, deep water, etc.) may have a different

levels of automation, which can range from simple one way monitoring to complex

subsurface controls with intelligent completions [14].

The Petrobras GeDIg of Petrobras selected the Carapeba field as a pilot project. It

is a mature field composed of 3 wells located in the northeastern part of the Campos

Basin which has installed automated subsurface sensors in the wells.

Figure 8 details the configuration of the Carapeba field.

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FIGURE 8

In the Carapeba pilot project, production rates, well pressures, total flow versus

time, pressure/temperature versus depth, and operational alerts are measured using

RFID.

The wells that use these technologies for monitoring, tracking and control are called

Smart Wells or Intelligent Wells.

Smart Well technology benefits include:

1) Reduction of well maintenance time

2) Easier detection of abnormal conditions

3) Accelerated problem analysis

4) Reduction of mechanical failures

5) Prioritizes the scheduling of operational activities,

6) Optimization of the use of crew and equipment resource

7) Improved reservoir management

8) Mitigation of operational risk.

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.

2.2.Information Management

Depending on the technology used in wells, the number of operational sensors, and

the ranges of measurements, more than 10 GB (gigabyte) of data per day is generated

for a single offshore field. This is a large concern for CIOs (Chief Information Officers)

of oil companies.

Because of the rapid increase of Digital Oilfield data, Information Lifecycle

Management is very important for oil companies. It is necessary to understand which

data sets are dynamically changing (ex, SCADA) or static (ex. seismic) and which

device is best suited for storing this data. There are several types of storage devices

with different technologies and different data management speeds.

The figure below shows two types of storage equipment from EMC [15].

FIGURE 9

Storage systems with faster access to data cost more than those who offer slower

access to data.

With a proper understanding of the data sources and their related applications, you

can save space and money on efficient use of storage.

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Another major concern is the availability of such data to users, given the agreed

levels of service and security necessary.

To ensure the high speed data access performance, regular studies of capacity and

new technology (hardware and software) should be carried out.

Every user should have a customized security profile which includes a digital

credential that is verified and authenticated (user identification, password, biometrics,

etc.), Role-based security is also necessary to access restricted information. The figure

below illustrates an authentification and authorization system designed by Oracle.

FIGURE 10

Confidential information should be restricted and carefully monitored and, if

possible, should be kept in encrypted form.

Access to confidential data via the Internet must be conducted using the VPN

(Virtual Private Network) or other secure protocols (i.e. HTTPS).

System and data auditing must be archived so that if an illegal access takes place,

alerts are rapidly fired to security administrators.

Petroleum data management standards are critical to maintaining an open, flexible,

best-of-breed system. Major E&P initiaitives include PPDM for an upstream data

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model, Energistics for WITSML and PRODML data exhange formats, PODS for

pipeline data, and MIMOSA for real time monitoring. Addtionally, IT standards such as

SOA and web services should be evaluated for an oilfield architecture.

It is also necessary that companies have solutions for disaster recovery, ensuring

the least possible time of interruption in case of a disaster in the main site.

Today, Oracle provides solutions for data management, information security and

disaster recovery for various oil & gas companies.

2.3. High Performance Computing

The amount of data generated by Digital Oilfields projects and the need for

geophysicists and engineers to access huge amounts of information in real time, have

forced oil companies to use high-performance servers for data processing.

These systems large multiprocessor systems are called High Performance

Computing (HPC) systems. The term High Performance Computing refers to the use

of parallel processors and clusters of computers linked to multiple processors on a

single grid.

A high level of technical knowledge is required to assemble and use these systems,

but they can be created from existing components in the market.

Because of its flexibility, high processing capacity, and relatively low cost, the HPC

systems are increasingly dominating the world of supercomputing.

The use of high performance computing has significantly improved performance of

E&P applications, mainly in the areas of seismic processing, velocity modeling, 3D

earth models and reservoir simulation.

Landmark, Schlumberger, SGI [16], Oracle and Sun [17] have adopted high

performance grid computing as a key part of their technology strategy.

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2.4. Centers of command and remote monitoring

The oil industry has undergone rapid growth in recent years but still lacks human

resources to meet the needs of these companies.

Companies usually produce oil in inhospitable regions, such as deepwater offshore,

in deserts, or in politically dangerous places that are difficult to access where not many

qualified people want work.

There are a lot of situations where specialized knowledge is needed. Development

efforts are often delayed because of the lack of skilled employees and the inability to

relocate these experts to the oilfield work site.

Oil companies are investing heavily in command centres and remote monitoring, so

that the operations specialists, engineers and geoscientists do not need to travel to

remote oilfields.

Cross-disciplinary teams composed of geoscientists, engineers, operations

managers and financial analysts now interact in remote command centres,

encouraging teamwork and collaboration and solving problems faster.

As an example of successful use of such technology is Statoil. At Kristin platform

located 240 km off the coast of Norway, Statoil saved USD $36.5 million in yearly

operational costs by minimizing the number of employees on the platform, reducing the

number of shifts, reducing safety incidents, improving security, accelerating problem

resolution and improving the quality of life for its employees. [18]

Figure 11 shows the Center for operations managers at the Kristin platform, which

is connected continuously with the onshore command center.

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FIGURE 11

In Brazil, Petrobras’ GeDig Digital Oilfield projects have already implemented two

Command Centres using the command centre technology.

In these centres, multidisciplinary teams work collaborate on monitoring production,

detecting problems, developing solutions and using Best Practice decision making

processes. The teams from these centres interact with the teams that are on platforms

in real time.

Figure 12 shows one of the centers of remote control of Petrobras.

FIGURE 12

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Oil companies are using the name “Integrated Operations” for this new concept,

where different offshore and onshore departments work in an integrated manner,

increasing productivity and efficiency.

2.5 Systems for Analysis and Simulation of Hydrocarbon Reservoirs

The E&P industry is very advanced is the modeling and simulation of reservoirs but

the supporting systems are still in technology silos. Great improvements have been

incorporated into existing software, so that accurate geologic reservoir models can be

easily visualized, loaded, and modeled. The earth model of a reservoir is very

important because it is used for reserve estimates, production forecasts and field

development plans.

Until very recently in the North Sea region, because of poor recovery analysis and

inaccurate reservoir models, oil companies drilled more wells than necessary and

constantly revised their production forecasts, causing delays and extra costs for oil field

development.

The earth model is built initially from the seismic data, then is refined to a geological

model using seismic interpretation software. This geologic model is combined with

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petrophysical and drilling data to build a 3D earth model which can be input into a

reservoir simulator.

Because of its extreme importance with respect to production forecasting and

reserves analysis, these models are updated with field data, which thwn use simulation

sensitivities to understand the behavior of the reservoir under the influence of various

factors.

Most Digital Oilfield projects makes intensive use of software for visualization and

simulation of reservoirs during inital phases of field development. With the additon of

real time operational information, these systems can be used for real time production

management and well monitoring. Well operations and drilling data is loaded

continuously into reservoir and well simulators, allowing more accurate computer

modeling of production facilities and providing more realistic forecasts.

From the existing models, different scenarios can be assembled to assess

development sensitivities and their possible results. Historical data from analagous

fields can be used to predict reservoir performance of undeveloped fields.

The figure 13 exemplifies a typical reservoir simulation image from the Roxar Field

[19].

FIGURE 13

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2.6. Systems for analysis and decision support

The E&P industry is adopting several new concepts. One of the concepts is "Fast-

Loop" versus "Slow-Loop" information processing, depending on the needs of business

operations.

For example, oilfield operations (flow, pressure, temperature, etc.) can be classified

as "Fast Loop". This type of information must be displayed and analyzed as soon as

possible.

"Slow Loop" processes may include longer term transactional information such as

ERP data or monthly costs of E&P projects.

The use of real time information from producing oil wells, intervention status, loss

details for each well, costs of materials and labour costs have become essential to E&P

operations and are the basis for many real time decisions.

The volume of “Fast Loop” information is increasing exponentially and is creating

data management problems for Digital Oilfield managers. The task of analyzing both

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“Fast Loop” and “Slow Loop” data without specialized software can waste a lot of time

for engineers in the field.

To provide the right information to the right people in time, the oil companies are

investing heavily in Business Intelligence software projects for Fast Loop and Slow

Loop information.

According to Wikipedia [20], Business Intelligence is a business term, which refers

to applications and technologies that are used to obtain, provide access and analyse

data and information in accordance with the operations of companies.

Business Intelligence can help companies understand the factors affecting its

business, assist in decision-making via KPIs, and is currently one of the main needs of

E&P companies.

A Business Intelligence solution is composed of a data warehouse

("Datawarehouse", "DataMarts") and tools to analyse and display results to users

through analytical reports.

There are several tools on the market for construction of these reports. These

reports use web portals to display important KPIs (production of oil and gas, alarms of

production below the optimal point, etc.).

The data for the assembly of these reports comes from many different sources,

such as Landmark or Schlumberger, company databases and ERP systems (Oracle E-

Business Suite, JD Edwards, SAP, etc.).

British Petroleum, OXY, Marathon, Chevron, XOM, Shell and many other IOCs

have already started Business Intelligence projects that analyze the rapid cycle

information.

BP’s Gulf of Mexico operation is using a Business Intelligence solution that

integrates information from its various systems and publishes web reports that help in

increasing productivity and reducing costs.

This system can be configured so that different people can have different visions of

operational and corporate data, according to their work needs. Each employee using

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the system has a customized profile that manages the transactions needed for their

daily work.

The figure 14 shows a web portal customized for the user that is used to monitor

the production of oil, gas and water. The user interacts with the plot and can drill down

into specific details if necessary.

In this example, production is declining, and the user can drill down for more detail

(Figure 15) by clicking on the line graph.

The dashboard from figures 14 and 15 was built using the Oracle’s Business

Intelligence software.

FIGURE 14

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FIGURE 15

Business Intelligence tools can also provide a series of graphs (Figure 16), which

combine structured and unstructured data and help in understanding both operational,

technical and financial information.

FIGURE 16

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Another important feature of such tools is the integration with Microsoft Office . The

reports and graphics built in Business Intelligence tool can be opened and used in

Excel and Powerpoint (Figure 17).

FIGURE 17

With these new Business Intelligence tools, the engineers can analyze the overall

field performance, identify which wells are not producing according to plan, analyze

costs and access real-time KPIs. These key indicators include revenue and profit per

barrel, lifting costs, etc.

Spatial performance maps can significantly improve the understanding of many

Digital Oilfield operational situations. These portals allow operators to make decisions

better and faster, encouraging safer and more efficient operations.

Information originating from different geographical locations and company

departments which previously took weeks to be gathered, are now rapidly analyzed

from a single control panel.

Business Intelligence applications continue to evolve and are integrating GIS

(Geographical Information Systems) systems of companies, making a spatial

connection between data and its specific location on a map.

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With this type of GIS integration, intuitive applications are being built for hurricane

tracking, personnel safety, production monitoring and facilities management. (see

Figure 18)

FIGURE 18

Long cycle information can also be spatially visualized including actual vs budgeted

AFEs, revenue versus expenditure, financial reports for government agencies and HSE

compliance reporting.

For this types of information, there is specialized Business Intelligence software

which facilitates the tasks performed by users, increases productivity and derives

detailed management information for improved decision-making.

Usually these systems are integrated with those previously used in the rapid cycle

information.

Another E&P need is detailed analysis of existing data to find patterns and predict

situations. Companies are using Data Mining to identify areas to be drilled, optimize

results of well interventions, select candidates for hydraulic fracture versus chemical

treatment and analyze exploration anomalies.

There are two types of Data Mining - Descriptive and Predictive.

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Descriptive Data Mining is used on exploration data to discover patterns and

relationships that are repeated in similar geologic structures.

Predictive Data Minin is being used on maintenance data to anticipate possible

equipment failures [21].

Data Mining tools make intensive use of statistical algorithms. These include

Prioritized Allocation, Classification and Prediction, Regression, Clusters, Rules of

Association, Extraction of Features, Text Mining, BLAST, Decision Models Trees and

SVM.

3. Conclusions

The oil industry is going through a phase of unprecedented technological

developments with their rapid implementation of Digital Oilfields.

Current advances are allowing oil companies to improve recovery and accelerate

production but all of this information and technology is not being fully utilized.

The exploitation of oil reserves has grown because of new oilfield technologies and

better definition of existing fields. One of the companies that have achieved great

success in this field is Saudi Aramco, which has had significant incremental production

increases.

Saudi Aramco increased its production from 10 million barrels per day in 2004 to 11

million barrels per day in 2008. All new wells are equipped with permanent downhole

monitoring, submersible pumps, intelligent completions and are connected to a central

remote command centre which have multidisciplinary teams managing production

operations.

More expensive energy sources, such as heavy oil from the Orinoco basin and

Canadian Tar Sands, are now economically viable at prices greater than $60/Bbl.

Companies such as Petrobras and Chevron, through the use of technologies cited

in this work are already drilling in ultra-deep waters using fully automated Digital Oilfield

technology.

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The IOCs and NOCs are focused on programs to reduce costs and increase

productivity in order to achieve their operational and financial objectives. The net profit

of Exxon Mobil was USD $40B in 2007, USD $18B for Chevron and USD $11B for

ConocoPhillips in 2007. [22]

Use of the technology by itself does not guarantee a company better results. It is

also necessary to invest in human capital management and technology training. Only

with a well trained and motivated workforce can deliver increased productivity at lower

costs. Currently one of the biggest challenges for the oil and gas industry is to attract

and train skilled employees. The oil companies are investing heavily in training

programmes, in partnerships with educational institutions and in joint ventures with

oilfield service and technology companies.

The exchange of experiences and collaboration on a global scale is causing an

increasing number of electronic communities geared to the oil and gas industry. The

use of blogs and wikis for dissemination of oilfield knowledge and the use of virtual

environments such as "Second Life" for promotion of companies and new technologies

is continuing to be adopted by progressive companies.

Within this context the SPE (Society of Petroleum Engineers) [23], the IBP

(Brazilian Institute of Oil Gas and Biofuels) [24] and COPPE / UFRJ (Luiz Alberto

Coimbra Institute of Post-Graduate Engineering and Research) have provided valuable

contributions.

Oil companies must continue to develop the skills of their employees. They must

merge engineering expertise, exploration and production skills, and information

technology to fully leverage the Digital Oilfield.

According to the Vice President of Chevron, Donald L. Paul, there will soon be a

new generation of applications for integrated seismic interpretation, earth modeling and

reservoir simulation. He expects major advances in underwater robotics and the

inevitable exploration and production of offshore oil in the Arctic [25].

With all these technological advances in Digital Oilfield projects, the amount of

information being processed will continue to expand exponentially, leading to advanced

software development and more complex integrated software applications.

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Critical data can be cached in memory, thus allowing faster access. Software from

the market such as "Oracle Times Ten (In-Memory Database)" [26], which can carry

information from the database to the server memory will become widely used in

industry.

In the field of technological research some companies are investing in

nanotechnology and biotechnology. In the field of nanotechnology one of the major

applications is the creation of nano robots capable of being inserted into a petroleum

reservoir to collect reservoir description information. In the field of biotechnology one of

the lines of research is related to the development of bacteria capable of turning heavy

oil into lighter oil while still in the reservoir. Another line of research is investigating the

use of enzymes to increase oil recovery. Another important technology research area

are Health/Safety/Environmental (HSE) issues, which require that companies make

their operations safer and more eco-sensitive.

I think the industry will continue to meet the growing global needs for energy.. IOCs

and NOCs will seek out new technologies to improve recovery, find more reserves,

explore new alternative sources, optimize the costs of E & P and work in a more

secure, collaborative manner.

The Digital Oil Field will be deployed on a large scale by most of E & P companies

and the technologies used in these projects will be increasingly employed in this

industry.

References

Jacobs – “Digital Oil Field of the Future Lessons from Other Industries” Cambridge Energy Research Inc (CERA) Lima e outros - SPE PAPER 112191 – GEDIG Carapeba – A journey from Integrated Intelligent Field Operation to Asset Value Chain Optimization www.shell.com www.bp.com www.chevron.com www.energyinsight.com www.gaveasensors.com

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http://www.statoilhydro.com www.shipcomwireless.com www.osisoft.com www.oracle.com www.halliburton/landmark www.schlumberger.com José Eduardo Thomas – Book Fundamentos de Engenharia do Petróleo www.emc.com www.sgi.com www.sun.com Digital Energy Journal (Nov & Dec 2007 issue) Digital Energy Journal (Jun 2006 issue) Wikipedia – www.wikipedia.com Shahab D Mohaghegh – SPE PAPER 84441 – Essential Components for a Data Mining Tool for the Oil & Gas Industry. O Globo Newspaper – 04 March 2008 www.spe.org www.ibp.org.br Journal of Petroleum Technology - October 2007 – Special Commemorative Issue Oracle Times Ten - http://www.oracle.com/technology/products/timesten/index.html