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USE AND APPLICABILITY OF MACHINE LEARNING TO FORMATION EVALUATION LEARNING TO FORMATION EVALUATION Quentin GROSHENS (Supélec) Emmanuel CAROLI (EXPLO/GTS/COP/ITD) Sébastien GUILLON (EXPLO/GTS/IGR/CIG) Pierre GOUTORBE (EXPLO/GTS/IGR/CIG/GMD)

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USE AND APPLICABILITY OF MACHINE LEARNING TO FORMATION EVALUATIONLEARNING TO FORMATION EVALUATION

Quentin GROSHENS (Supélec)

Emmanuel CAROLI (EXPLO/GTS/COP/ITD)

Sébastien GUILLON (EXPLO/GTS/IGR/CIG)

Pierre GOUTORBE (EXPLO/GTS/IGR/CIG/GMD)

CONTENT

DEEP LEARNING TOOLS

APPLICATION TO LOG INTERPRETATION

SAID / Big Data and Machine Learning applied to Petrophysics 2

INTERPRETATION

CONCLUSION & WAYFORWARD

CONTENT

DEEP LEARNING TOOLS

APPLICATION TO LOG INTERPRETATION

SAID / Big Data and Machine Learning applied to Petrophysics 3

INTERPRETATION

CONCLUSION & WAYFORWARD

MACHINE LEARNING – BASIC PRINCIPLES

● Mixing signal processing and statistics

● Objective of the learning process : minimizing a loss

● Goals : provide meaning to the data

SAID / Big Data and Machine Learning applied to Petrophysics

- Choose a model

- Discover relations

- Identify

- Represent

- Group or separate

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DEEP FEED FORWARD NETWORKS

● Each layer fully connected to the previous one

Inputs Hidden Layers Outputs

● Very expensive for

big inputs

SAID / Big Data and Machine Learning applied to Petrophysics

big inputs

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DEEP CONVOLUTIONAL NETWORKS

● Convolutional layers extracts features

● Very light to train (compared to fully connected layers)

- Sub-sampling layers reduce the resolution of the data

SAID / Big Data and Machine Learning applied to Petrophysics 6

Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger Grosse

WHAT TO USE AND WHEN

SAID / Big Data and Machine Learning applied to Petrophysics

● Input relatively small

● Classification or regression

● Input with spatial organization (images, geographic data …)

● Classification

http://www.asimovinstitute.org/neural-network-zoo/

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CONTENT

DEEP LEARNING TOOLS

APPLICATION TO LOG INTERPRETATION

SAID / Big Data and Machine Learning applied to Petrophysics 8

INTERPRETATION

CONCLUSION & WAYFORWARD

NEW WELL LOG INTERPRETATION CONTEXTS

● Hundreds or thousands of wells becomes a common situation nowdays (new ventures, DRO, unconventional…)

● How to deal with logs fromunknown geological contexts

● Decades of log interpretations in various contexts

● Generally uniform and wellstructured log database

The challenge The opportunity

SAID / Big Data and Machine Learning applied to Petrophysics

unknown geological contexts

● Fast screening, tight agenda

- Impossible to answer with classicaldeterministic approaches

- Generally, only interpret a subset of wells (a dozen max)

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● Almost always the same logs are run (classical triple combo)

Deep learning may be a solution

FIRST ATTEMPTS

>> Objective: Predict the classical outputs of the well interpretation

● Input data: minimum log dataset

• Gamma Ray (GR)

• Neutron porosity (NP)

• Resistivity (RT)

• Density (RHOB)

SAID / Big Data and Machine Learning applied to Petrophysics

• Density (RHOB)

● Training on interpreted wells logs (all from same soft)

- Total and effective porosity (PHIT & PHIE)

- Total and effective water saturation (SWT & SWE)

- Volume of clay (VCL)

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TRAINING #1 ON DEEP OFFSHORE WELLS

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3 hidden layers212 neurons per layerTraining only on offshore dataset (100000 learning points)

TRAINING #2 ON SHELF WELLS

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Same neurons3 hidden layers212 neurons per layer

Training only on shelf dataset (only 9000 learning points)

WELL LOG INTERPRETATION DATABASE

● Training well database

- 39 interpreted wells for different geographic areas

• Deep offshore (turbidites) : 24 wells

• Shelf (delta): 15 wells

- Each sample represent the result for one depth

• sampling each ½ ft (15.24 cm)

- 140k inputs for training, 15k for validation and testing

SAID / Big Data and Machine Learning applied to Petrophysics

● Pre processing

- Inputs normalized between [0,1]:

• GR/200 and clipped between [0,1]

• Density normalized between {1.95 – 2.95 g/cc} and clipped between [0,1]

• Neutron normalized between {-0.15 – 0.45 V/V} and clipped between [0,1]

• Log(Rt) normalized between {0.2 – 2000 W.m} and clipped between [0,1]

- Tests with and without convolution window (90 cm)

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WELL LOG INTERPRETATION DATASET

● Predict the output of the well interpretation

- Minimum log inputs

• Gamma Ray (GR)

• Neutron porosity (NP)

• Resistivity (RT)

• Density (RHOB)

- Extra-input required from model

SAID / Big Data and Machine Learning applied to Petrophysics

- Extra-input required from model

• Temperature (TEMP)

• Water salinity (SALW)

● Based on interpreted wells logs (all from PETROLAN)

- Total and effective porosity (PHIT & PHIE)

- Total and effective water saturation (SWT & SWE)

- Volume of clay (VCL)

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WELL LOG INTERPRETATION RESULTS: DEEP OFFSHORE

SAID / Big Data and Machine Learning applied to Petrophysics

Absolute error after clipping: PHIT: 2.6%, SWT: 2.7%, PHIE: 2.5%, SWE: 4.7%, VCL: 16.1%

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WELL LOG INTERPRETATION RESULTS: SHELF

SAID / Big Data and Machine Learning applied to Petrophysics

Absolute error after clipping: PHIT: 2.2%, SWT: 9.5%, PHIE: 2.3%, SWE: 9.9%, VCL: 6%

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DATASET SIZE INFLUENCE

(log)

Ref. 90% training60% training40% training

SAID / Big Data and Machine Learning applied to Petrophysics 17

Learning steps

Err

or

(log)

INFLUENCE OF THE NUMBER OF LAYER

Err

or

(log)

One layer networkTwo layers networkThree layers network

SAID / Big Data and Machine Learning applied to Petrophysics 18

Learning steps

Err

or

>> A shallow but large network (212) has a better training efficiency>> There is however a threshold not to exceed: 213

INFLUENCE OF THE NUMBER OF LAYER

With convolution(log)

Conv. with one hidden layer Conv. with two hidden layersConv. with three layers networkRef. with one layer, no conv.

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Learning steps

Err

or

WELL LOG INTERPRETATION TRAINING

● Extensive testing to chose the proper width and depth of the network

● Best network for now:

- One convolutional layer of 10 filters of size 3

- Two hidden layers 4096 neurons each

PHIT SWT PHIE SWE VCL

SAID / Big Data and Machine Learning applied to Petrophysics

● L2 loss for training completed with secondary metric:

Addition of external constrains to force physical relations

• PHIT x (1 – SWT) = PHIE x (1 – SWE)

• Archie formula, PHIT = (PHID + NP)/2 + ERR

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PHIT SWT PHIE SWE VCL

Average error (%) 2.9 5.1 2.6 7.8 12.5

WELL LOG INTERPRETATION RESULTS: SHELF #1

SAID / Big Data and Machine Learning applied to Petrophysics

Absolute error after clipping: PHIT: 2.2%, SWT: 9.5%, PHIE: 2.3%, SWE: 9.9%, VCL: 6%

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WELL LOG INTERPRETATION RESULTS: SHELF #2

SAID / Big Data and Machine Learning applied to Petrophysics

Absolute error after clipping: PHIT: 1.6%, SWT: 6.9%, PHIE: 1.3%, SWE: 13.2%, VCL: 7.9%

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INFLUENCE OF THE PHYSICAL CONSTRAINTS

Only {PHIT x (1 – SWT) – PHIE x (1 – SWE)} tested(log)

Ref. with one hidden layer + conv. Tol. Lc = 1Tol. Lc = 0.5Tol. Lc = 0.1Tol. Lc = 0.01

Local minimum

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Learning steps

Err

or

(log)

Local minimum

…constrain but not too much !

Better initial performances, but…

CONTENT

DEEP LEARNING TOOLS

APPLICATION TO LOG INTERPRETATION

SAID / Big Data and Machine Learning applied to Petrophysics 24

INTERPRETATION

CONCLUSION & WAYFORWARD

CONCLUSION

● Using a window with convolutional filter improves the accuracy

● Better results with shallow and wide network

● Good capacity of generalization even outside the training geological context

SAID / Big Data and Machine Learning applied to Petrophysics 25

● Still difficult to learn the “saturated” data

• Small gain from the integration of the physical constraints

KEY ELEMENTS OF DEEP LEARNING

● Database creation

- Clearly define the objective of the training

- Pre-process the data to create homogeneous database

- Artificially expand the database if possible

● Network design

- Adapt the size of the network to the complexity of the task

SAID / Big Data and Machine Learning applied to Petrophysics

- Adapt the size of the network to the complexity of the task

- Don’t create a network too big compared to the size of the database

- Choose an adapted loss

- Properly constrain the network to avoid over-fitting

- Make small tests to evaluate meta parameters

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WAY FORWARD

● Till now, just a first attempt, but…

● Deep learning algorithms have a huge potential

- Not a perfect solution for everything but can propose alternate senarios

- Specialized networks to generate multiple scenarios and estimate theirprobability

- Some more tests required on :

• Larger data base, more heterogeneous, a larger variety of geological contexts

SAID / Big Data and Machine Learning applied to Petrophysics

• Larger data base, more heterogeneous, a larger variety of geological contexts

• More and heterogeneous logs datasets

● The key limiting factor is the access to the database

- Can only be as good as the ground truth provided

- What if we would train logs directly on core data ? How to proceed ?

● Designing a network can be time consuming… but once it’s done, can be applied efficiently

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DISCLAIMER & COPYRIGHT

The TOTAL GROUP is defined as TOTAL S.A. and its affiliates and shall includethe party making the presentation.

Disclaimer

This presentation may include forward-looking statements within the meaning ofthe Private Securities Litigation Reform Act of 1995 with respect to the financialcondition, results of operations, business, strategy and plans of Total that aresubject to risk factors and uncertainties caused by changes in, withoutlimitation, technological development and innovation, supply sources, legalframework, market conditions, political or economic events.

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Total does not assume any obligation to update publicly any forward-lookingstatement, whether as a result of new information, future events or otherwise.Further information on factors which could affect the company’s financial resultsis provided in documents filed by the Group with the French Autorité desMarchés Financiers and the US Securities and Exchange Commission.

Accordingly, no reliance may be placed on the accuracy or correctness of anysuch statements.

Copyright

All rights are reserved and all material in this presentation may not bereproduced without the express written permission of the Total Group.

SAID / Big Data and Machine Learning applied to Petrophysics