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Analytics in the Oil and Gas Industry Analytics, Big Data and the Cloud Conference April 24, 2012


Framework for Analytics Describe

Analytics

Science

Analysis

Predict

Process Model

Insight

Prescribe

Process Optimization and Control

Action

Production Intelligence

Analysis

ďƒ˜

Insight

ďƒ˜

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 nonlinear 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

ďƒ˜

Insight

ďƒ˜

Action


The Heart of OreInsight – Development of Associative Rules From Coring Data... Dean-Stark Core ID

Bitumen %

1

PSD …

Chemical Analysis

Mineralogy …

MBI

BEU

D50

Fines

Na

K

Ca

Mg

pH

Recovery

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%

To Associative Rules which are the foundation of prediction Dean-Stark Rule ID

Bitumen %

R1

[7,9)

R2

[5,7)

PSD …

D50

Chemical Analysis Fines

Na

K

Ca

Mg

Mineralogy pH

MBI

BEU

[5,9) [25.0,27.5)

AD Statistics

Recovery

WoE

[90-95%]

0.8

[ < 75%]

2.1

[95-100%]

0.8

… R158

[100,110)

[0,4)

[7.0,7.5)

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

Production Intelligence

Analysis

Insight

Action

Dean Wallace - Analytics in the Oil and Gas Industry  
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