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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
4
➢ 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
6
??
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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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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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15
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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21
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
24
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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
27
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28ML picked Tops – Auto Tops PickerTM
W1
W2
W3
W4W5
W6
28
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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
31
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32Auto Facies using ML
DT (us/ft) DT (us/ft)
GR
(AP
I)
GR
(AP
I)
Kmeans Unsupervised ML Clustering
32
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
33
1
2
3
4
5
NPHI (v/v)
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Summary
34
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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
35
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Disclaimer
The information contained in this presentation is confidential. Any unauthorized use, disclosure, reproduction, or dissemination, in full or in part, in any media or by any means, without the prior written permission of IHS Markit Ltd. or any of its affiliates ("IHS Markit") is strictly prohibited. IHS Markit owns all IHS Markit logos and trade names contained in this presentation that are subject to license. Opinions, statements, estimates, and projections in this presentation (including other media) are solely those of the individual author(s) at the time of writing and do not necessarily reflect the opinions of IHS Markit. Neither IHS Markit nor the author(s) has any obligation to update this presentation in the event that any content, opinion, statement, estimate, or projection (collectively, "information") changes or subsequently becomes inaccurate. IHS Markit makes no warranty, expressed or implied, as to the accuracy, completeness, or timeliness of any information in this presentation, and shall not in any way be liable to any recipient for any inaccuracies or omissions. Without limiting the foregoing, IHS Markit shall have no liability whatsoever to any recipient, whether in contract, in tort (including negligence), under warranty, under statute or otherwise, in respect of any loss or damage suffered by any recipient as a result of or in connection with any information provided, or any course of action determined, by it or any third party, whether or not based on any information provided. The inclusion of a link to an external website by IHS Markit should not be understood to be an endorsement of that website or the site's owners (or their products/services). IHS Markit is not responsible for either the content or output of external websites. Copyright © 2019, IHS Markit®. All rights reserved and all intellectual property rights are retained by IHS Markit.
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Leveraging Machine Learning To Improve Geological and Petrophysical Workflows
Malleswar Yenugu, PhD