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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
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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
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- 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
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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
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Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger Grosse
WHAT TO USE AND WHEN
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● 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
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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
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Learning steps
Err
or
(log)
INFLUENCE OF THE NUMBER OF LAYER
Err
or
(log)
One layer networkTwo layers networkThree layers network
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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
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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
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● 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
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• 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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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
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