Data-driven estimates of reservoir properties from 3D/4D seismic A brown field study Evgeny Tolstukhin*, Reidar Midtun, Pål Navestad, ConocoPhillips Norway Tetyana Kholodna, CapGemini Norway Evgeny.Tolstukhin@conocophillips.com October 13, 2019

Project: Properties from Seismic Motivation and Business Case Idea / Innovation: Use Machine Learning to predict reservoir properties directly from 3D seismic and well logs data Motivation: Increase value of G&G data through data-driven approach Build a Machine Learning and AI platform for Subsurface Domain Project scope: Duration: 3 months Prove the concept Evaluate business impact Business impact: Drill less water-wet wells and side-tracks Better understanding of reservoir properties and mechanisms October 13, 2019

Digitization of Subsurface: seismic and wells data Survey 2010 Survey 2016 A09 A18 A27 Elastic Impedance Poro well log Swe well log RFT pressure Voxels Table October 13, 2019

Illustration of resolution and sampling effects Sample bias Swe 100 ft average - Observed Well log vs. Seismic Pressure Swat > 0.5 - Observed - Polynom - Linear - Swe Fly by Pluto with the New Horizons probe | New Scientist October 13, 2019

Data available: poro, swe, pressure, A09, A18,A27 and ratios EI A09 EI A09 Poro Swe Swe EI A09 Poro EI A09/A27 October 13, 2019

Causation vs Correlation: poro, swe, pressure, A09, A18,A27 and ratios Properties, available only from well logs EI A09 Seismic, available in 3D volume Poro EI A09/A27 October 13, 2019

Causation vs Correlation Swe EI A09 Color is Pressure Poro EI A09/A27 October 13, 2019

Causation vs Correlation Swe EI A09 Color is Pressure Low Swe High Poro Poro EI A09/A27 October 13, 2019

Causation vs Correlation Swe EI A09 High Swe Low Poro Color is Pressure Poro EI A09/A27 October 13, 2019

Causation vs Correlation: NEXT LEVEL, division into groups or clusters Pore intervals Color is Formation Scope: Try alternative clustering methods: Density Dbscan K-means Normal Mixture Hierarchical Dimesionality reduction: Multi-Dimensional Scaling Principal Component Analysis Try alternative ML methods: Multi-Adaptive Regression Neural Networks Decision Trees Support Vector Machine Random Forest etc. Pressure Intervals A09 Swat 0-1 October 13, 2019

Concept illustration: clustering and prediction SeismicType 1 RockType 1 Cluster 1 Category H Cluster 1 Category H RockType 1 SeismicType 1 RockType 2 Only seismic Use ML model from wells A09, A18, A27 Cluster 2 Category L RockType 2 SeismicType 2 SeismicType 2 Category H Cluster 2 Cluster 2 Category L Category L Cluster 1 RockType 1 Poro, Press, Swe RockType 2 October 13, 2019

Software architecture SAS JMP / R-scripts Select Transform Filter Interpolate CARET Predicted Factor: Category Well Master table K-means Clustering 3 Seismic 3 props per formation Random Forest Predict Cluster With Factor: Water and Factor: Category Predict Swe, Poro, Press within Cluster using Random Forest Clusters Software used in the project: • SAS Enterprise Guide 7.1 • JMP 14.2.0 • JMP.R version 14.0 Distributions Within Clusters Factor: Category High, Medium, Low Scoring in 3D October 13, 2019

Agenda Introduction Methodology review Results Validation Summary October 13, 2019

Results of new Clustering at well level Clusters Swe Poro Pressure Seismic A09 October 13, 2019

Results of new Clustering: zoom Clusters Swe Poro ed ed ed ed Pressure Seismic A09 October 13, 2019

BLIND TEST Prediction stage Validation at well level: future wells drilled in 2017-2018 October 13, 2019

Effect of faults and fault shadows: Well 4 example (prediction validation) Well 4 water saturation Faulted zone Well 4 water saturation Faulted zone October 13, 2019

Summary of prediction at well level: wells drilled in 2017-2018 Property Abs. misfit Quality of prediction, percent of correct voxels Poro, unit < 0.03 82% Swe, unit < 0.1 84% Pressure, psi < 1000 psi 81% What data do we have: ML models trained on wells drilled in 2010-2016 Reservoir properties scored in 3D using 2016 seismic What data do we compare to: «Blind test» or validation wells drilled in 2017-2018 Observed properties from well logs (lumped into «voxels») October 13, 2019

3D Comparison with simulation model: prediction of 3D Water Saturation, formation average Polygons are manual interpetations of water fronts based on well and production data October 13, 2019

Agenda Introduction Methodology review Results Validation Summary October 13, 2019

Summary The methodology allowed to predict 3D volumes of porosity, water saturation and pressure: Predictions show good results at wells and in 3D This data-driven model can be further utilized for: Further quantitative analysis Reservoir characterization Multi-disciplinary communication Key learnings from the project: Agile project management Collaboration between data and geoscientists «Test fast, fail fast, adjust fast» Quick feed-in of more data from Subsurface Data Lake: New wells, new seismic, new simulation models, other observations Consider addition of tracers, pressure, temperature and other data Strength of the methodology: Quick to run and update Overall 10 min from training at well level to scoring in 3D October 13, 2019

Acknowledgements October 13, 2019