Dean Wallace - Analytics in the Oil and Gas Industry

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


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.


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 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



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



Benefit from the Application of Analytics?

Surface-mined oil sands


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


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


The Bitumen Recovery Process

V = C * r2 * (d1 – d2)

µ


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


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


OreInsight (A Solution to the Underlying

Contribution of Ore Variability)


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


Output from OreInsight (Mine Analyzer)


Output from OreInsight (Mine Analyzer)


Shovel Modeling with OreInsight


Shovel Modeling with OreInsight


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



Documents

Dean Wallace - Analytics in the Oil and Gas Industry