dean wallace - analytics in the oil and gas industry
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Analytics in the Oil and Gas
Industry
Analytics, Big Data and the Cloud Conference
April 24, 2012
Production Intelligence Analysis Insight Action
Framework for Analytics
Analytics Science
Process Model
Process
Optimization
and Control
Analysis
Action
Insight
Prescribe
Predict
Describe
Production Intelligence Analysis Insight Action
Fallacies Arising from the Simplest Form of
Analysis – The Infamous “Average”
• An argument can be made on the same basis that the tree frog,
on average, is black. However, this analysis is about as relevant
as the knowledge drawn from overly-simplistic mathematical
calculations that often are carried out on large data sets.
Production Intelligence Analysis Insight Action
The Averaging Equivalent in the Oil Sand Industry
• Bitumen content
• PSD
• D50
• Fines content
• Connate water chemistry
• Na
• Mg
• K
• Ca
• Cl
• pH
• MBI
Production Intelligence Analysis Insight Action
Production Environments that Might
Benefit from the Application of Analytics?
Refineries and Upgraders?
~637 complexes world-wide
Estimated 16,000 operational years
Process fundamentals well established
Process simulators standardized
From a purely process perspective, perhaps not so much value
Production Intelligence Analysis Insight Action
Production Environments that Might
Benefit from the Application of Analytics
Conventional oil and gas/EOR/In situ oil sands?
Approximately 160,000 operating oil and gas wells (ca. 2005) in
Alberta
Over 5000 new wells certified in 2011
The challenge associated with the use of analytics in a
production optimization environment relates to the fact that the
raw data themselves often are averaged data
Productivity is averaged over a long producing interval, sometimes
through a variety of geologic facies
Productivity is described by a time-series
Process models therefore are built on a phenomenological basis
Physical and mathematical modeling at AITF
Calibration of those models to the fields scale at CMG
Production Intelligence Analysis Insight Action
Production Environments that Might
Benefit from the Application of Analytics?
Surface-mined oil sands
Production Intelligence Analysis Insight Action
Unique Features of Extraction Operations
(Surface Mined Oil Sands)
Lack of operational experience
Less than 100 operations-years in Alberta
Experience base has not been built to the same extent as in
refineries
Nature of the data
The oil sand in the circuit at any point in time can be related back
to a clearly defined geographical coordinate (and therefore
clearly defined ore characteristics)
Perhaps as many as 100 – 120 additional process variables
The frequency of the data from the input variables in the process
>>>> the frequency of process decisions
Input variables recorded on the scale of seconds to tens of minutes
Process decisions result in a measureable response in ~45 minutes
Production Intelligence Analysis Insight Action
Unique Features of Extraction Operations
(Surface Mined Oil Sands)
Nature of the process
The oil sand extraction
process is one that is
controlled by interactions
Response to primary
variables tend to be non-
linear
No firm consensus in the
industry about dominant
process mechanisms
Production Intelligence Analysis Insight Action
The Bitumen Recovery Process
V = C * r2 * (d1 – d2)
µ
Production Intelligence Analysis Insight Action
An Example of Controlling Interactions
• 2-D boundaries between different middlings classifications
can be shifted as well by particle size distribution of solids,
mineralogy of solids, shear, temperature….. most of which
have non-linear responses
Production Intelligence Analysis Insight Action
Analytics as the Solution – The Production
Intelligence Suite of Analytics Solutions
Objective was to understand the effect of interactions on end-of-line
measures (e.g. recovery) for the purpose of optimizing the process
It was necessary to consider a probabilistic solution in addition to
deterministic solutions
Required a solution that was not biased by the individual doing the
modeling
Association Discover*E was an appropriate tool for this application
Data-driven rule generation provides an unbiased perspective
Rule structure results in transparency so people can assimilate
knowledge developed in the analytics process
Transparency also results in identification of correlation of supposedly
independent variables
Allows for simultaneous analysis of quantitative and descriptive variables
Possibly a precursor to a control system without human intervention
Production Intelligence Analysis Insight Action
OreInsight (A Solution to the Underlying
Contribution of Ore Variability)
Production Intelligence Analysis Insight Action
Core ID
Dean-Stark PSD Chemical Analysis Mineralogy BEU
Bitumen % … D50 Fines Na K Ca Mg pH … MBI … Recovery …
1 10.2 166 12.7 67 8 4 2 7.9 3.8 85.7%
2 9.6 93 28.1 104 17 7 5 8.2 7.1 72.4%
3 12.5 102 15.3 89 9 8 2 7.3 4.3 94.8%
…
399 8.9 97 25.0 128 13 5 4 9.1 8.4 85.3%
400 14.8 115 21.5 291 11 11 8 8.2 4.9 89.6%
From Coring Data...
Rule ID
Dean-Stark PSD Chemical Analysis Mineralogy BEU AD Statistics
Bitumen % … D50 Fines Na K Ca Mg pH … MBI … Recovery WoE …
R1 [7,9) [5,9) [90-95%] 0.8
R2 [5,7) [25.0,27.5) [ < 75%] 2.1
…
R158 [100,110) [0,4) [7.0,7.5) [95-100%] 0.8
To Associative Rules which are the foundation of prediction
The Heart of OreInsight – Development of
Associative Rules
Production Intelligence Analysis Insight Action
Output from OreInsight (Mine Analyzer)
Production Intelligence Analysis Insight Action
Output from OreInsight (Mine Analyzer)
Production Intelligence Analysis Insight Action
Shovel Modeling with OreInsight
Production Intelligence Analysis Insight Action
Shovel Modeling with OreInsight
Production Intelligence Analysis Insight Action
Closing Comments
We have found that a statistically-based analytics approach has
been able to unlock knowledge about the oil sand extraction process
that was not possible using conventional statistical techniques
and/or deterministic modeling
Elimination of bias during the process and transparency of the
results are critical
Analytics has led in some cases and has collaborated in others with
subject-matter expertise
The nature of the process being modelled determines if analytics
can provide value
Frequency of the data must be much greater than the frequency of
actions
The greater the influence of interactions on the process, the greater the
value of or necessity for an analytics solution
Production Intelligence Analysis Insight Action
Production Intelligence Analysis Insight Action
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