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Data-driven estimates of reservoir properties from 3D/4D seismic A brown field study

Explore data-driven estimates of reservoir properties using innovative Machine Learning techniques applied to seismic and well logs in a 3-month project.

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Data-driven estimates of reservoir properties from 3D/4D seismic A brown field study

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

  2. Project: Properties from SeismicMotivation 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

  3. DigitizationofSubsurface: seismic and wells data Survey 2016 Survey 2010 A09 A18 A27 ElasticImpedance Poro well log Swewell log RFT pressure Voxels Table

  4. Illustrationofresolution and sampling effects Sample bias Swe • 100 ftaverage • - Observed Well log vs. Seismic Pressure Swat > 0.5 - Observed - Polynom - Linear - Swe Fly by Pluto with the New Horizons probe | New Scientist

  5. Data available: poro, swe, pressure, A09, A18,A27 and ratios EI A09 EI A09 Poro Swe Swe EI A09 Poro EI A09/A27

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

  7. CausationvsCorrelation Swe EI A09 Color is Pressure Poro EI A09/A27

  8. CausationvsCorrelation Swe EI A09 Color is Pressure LowSwe High Poro Poro EI A09/A27

  9. CausationvsCorrelation Swe EI A09 High Swe Low Poro Color is Pressure Poro EI A09/A27

  10. CausationvsCorrelation: NEXT LEVEL, divisionintogroups or clusters Color is Formation Pore intervals • Scope: • Try alternative clustering methods: • DensityDbscan • K-means • Normal Mixture • Hierarchical • Dimesionalityreduction: • Multi-DimensionalScaling • Principal Component Analysis • Try alternative ML methods: • Multi-Adaptive Regression • Neural Networks • DecisionTrees • Support Vector Machine • Random Forest • etc. PressureIntervals A09 Swat 0-1

  11. Conceptillustration: clustering and prediction SeismicType 1 Cluster 1 Category H RockType 1 Cluster 1 Category H RockType 1 SeismicType 1 RockType 2 Onlyseismic Use ML model from wells A09, A18, A27 Cluster 2 Category L RockType 2 SeismicType 2 SeismicType 2 Category H Cluster 2 Category L Cluster 1 Cluster 2 Category L RockType 1 Poro, Press, Swe RockType 2

  12. Software architecture SAS JMP / R-scripts Select Transform Filter Interpolate CARET PredictedFactor: Category Well Master table K-means Clustering 3 Seismic 3 props per formation • Random Forest • Predict Cluster • With Factor: Water and Factor: Category • PredictSwe, 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

  13. Agenda • Introduction • Methodologyreview • Results • Validation • Summary

  14. Resultsofnew Clustering at welllevel Swe Clusters Poro Pressure Seismic A09

  15. Resultsofnew Clustering: zoom Swe Clusters Poro ed ed ed ed Pressure Seismic A09

  16. BLIND TEST Prediction stageValidation at well level: future wells drilled in 2017-2018 Well 1 Well 4 Well 2 Well 5 Well 3 Well 6

  17. Effect of faults and fault shadows: Well 4 example (prediction validation) Well 4 water saturation Well 4 Faultedzone Well 4 water saturation Faultedzone

  18. Summaryofprediction at welllevel: wellsdrilled in 2017-2018 • What data do we compare to: • «Blind test» or validation wells drilled in 2017-2018 • Observed properties from well logs (lumped into «voxels») • What data do we have: • ML models trained on wells drilled in 2010-2016 • Reservoir properties scored in 3D using 2016 seismic

  19. 3D Comparison with simulation model: prediction of 3D Water Saturation, formation average Water saturation 0-1 Polygons are manual interpetations of water fronts based on well and production data

  20. Agenda • Introduction • Methodologyreview • Results • Validation • Summary

  21. 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

  22. Acknowledgements

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