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Leveraging Machine Learning To Improve Geological and Petrophysical Workflows
Malleswar Yenugu, PhDAssociate Director in Data AnalyticsIHS Markit, Houston, USA
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AI and ML 2
Machine Learning
~
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AI for Upstream – E&P 3
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Outline 4
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➢ The Challenges for Geoscientists
➢ Log Predictor for Missing Log Generation
➢ Geology based ML Log Prediction
➢ Auto Top Picker for Formation Tops
➢ Auto Facies Predictor
➢ Summary
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The Challenges for Geoscientists
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The Challenges: Missing Logs or No Logs 6
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??
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The Challenges: Basin wide Formation Tops Picking 7
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The Challenges: Facies Identification 8
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Neutron Porosity (v/v) PEF (Barns/Electron)
DT
(us/
ft)
RH
OB
(g/c
c)
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Log PredictorTM using ML
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Linear Regression and Random Forest ML Algorithms 10
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.. .. ...
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. ... .
...... .... . .. .. ..... ... . ..... . ..
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.. .. . .. . .. ... .. . ..
. ... .
Logs used in the Training Log to Predict Linear Regression Predicted Log
Logs used in the Training Log to Predict Random Forest Predicted Log
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XGBoost Supervised Learning Algorithm 11
XGBoost…..
Errors Errors
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Log PredictorTM : Powder River Basin
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Leveraging ML: Powder River Basin 13
➢ A total of 273 wells used in the ML training
➢ Few key logs are missing in the well selected for ML prediction
➢ Core data is also used in the ML training
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Log Prediction – Comparison of ML Algorithms 14
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Derived from DeltalogR technique
TOC Log Prediction using ML
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Missing Log Generation using ML 16
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Actual vs. ML Predicted Log 17
TOC (wt%)
ML
Pre
dict
ed T
OC
(wt%
)
ILD (Ohm-m)
ML
Pre
dict
ed IL
D (O
hm-m
)17
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Log PredictorTM : Powder River Basin –Reservoir (Geology) Based Prediction
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What Logs We want to use for ML Training and Prediction?
19Geology (Reservoir/Physics) Based Log Prediction
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20Geology Based Log Prediction
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Actual vs. ML Predicted (based on Geology) Log
ILD (Ohm-m)
ML
Pre
dict
ed (b
ased
on
Geo
logy
) ILD
(Ohm
-m)
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Guideline Chart for Geology based Log Prediction 22
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Auto Top PickerTM using ML
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24Autotop PickerTM - CNN
CNN
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Auto Top PickerTM – Appalachian Basin
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26Auto Top PickerTM - Marcellus
GR: 1624 wells
GR&RHOB: 667 wells
Total No. of wells used in the Training (picked manually): 164
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27Tops from a Geologist
W1
W2W3
W4
W5 W6 W7
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28ML picked Tops – Auto Tops PickerTM
W1
W2
W3
W4W5
W6
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29Human-ML Tops Picks Comparison
Human picked Tops
Machine picked Tops 29
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Auto Facies using ML – Work in Progress
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31Permian basin well
BS
_LM
BS
1_S
DB
S2_
SD
BS3_
SDW
olfc
amp
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32Auto Facies using ML
DT (us/ft) DT (us/ft)
GR
(AP
I)
GR
(AP
I)
Kmeans Unsupervised ML Clustering
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1
2
3
4
5
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33Auto Facies using ML
NPHI (v/v)R
HO
B (g
/cc)
RH
OB
(g/c
c)
Kmeans Unsupervised ML Clustering
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1
2
3
4
5
NPHI (v/v)
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Summary
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Summary 35
➢ML is an additional tool to help Geoscientists
➢Both ML and Geology based Approach
➢Supervision from Geoscientists is necessary to reduce the Uncertainty
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Leveraging Machine Learning To Improve Geological and Petrophysical Workflows
Malleswar Yenugu, PhD