1 / 37

How Can Machine Learning Add Value to Making Inferences from Real Reservoir Data?

How Can Machine Learning Add Value to Making Inferences from Real Reservoir Data?. Vasily Demyanov Nathan Amaral , Julie Halotel , Tom Buckle, Rhona Hutton. Reservoir Challenges. Discover. Describe. Predict. Decide. Variability and heterogeneity in subsurface models.

basil
Download Presentation

How Can Machine Learning Add Value to Making Inferences from Real Reservoir Data?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How Can Machine Learning Add Value to Making Inferences from Real Reservoir Data? Vasily Demyanov Nathan Amaral, Julie Halotel, Tom Buckle, Rhona Hutton

  2. Reservoir Challenges Discover Describe Predict Decide Variability and heterogeneity in subsurface models On resource development with confidence under uncertainty Patterns in geological & reservoir data subject to uncertainty Outcomes of subsurface resources development Interpretable AI

  3. Reservoir Challenges Discover Describe Predict Decide Variability and heterogeneity in subsurface models On resource development with confidence under uncertainty Patterns in geological & reservoir data subject to uncertainty Outcomes of subsurface resources development Interpretable AI

  4. Discover Patterns in geological & reservoir data

  5. Facies classification challenge Bias Uncertainty Petrophysiycal interpretation Sedimentological interpretation Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  6. Real field application – North Falkland Hybrid Event bed (HEB) Scenario B Flow partitioning Flow transformation Turbulent flow Mixed flow: debris flow and turbidity current Bipartite laminar/turbulent flow Turbidite Scenario A Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  7. Alternative depositional scenarios • Thick package of sandstones: • Good porosities • High NTG • No barrier to vertical flow Scenario B • Fine grain layers: • Greater range of porosity with low outliers • Lower NTG • Potential additional barrier/baffle to flow Scenario A Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  8. What are the right input features? INPUT: wireline logs + engineered features OUTPUTPredicted True GR SP NPHI GR Resdiff RHO Random Forest(RF) Classification  Leo Breiman, 2001 Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  9. Feature importance • RF classifier gave more weight to the features commonly used by an expert • OWC becomes unimportant if data has no information about its impact► Leads to poor classification below OWC 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 No well with OWC in the training set well with OWC in the training set Relative importance Relative importance GR ResdiffPordiffSP RT NPHI RHO OWC RXO Resdiff GR PordiffRHO NPHI RXO RT OWC SP Feature Feature Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  10. Identify alternative scenarios Scenario A: prediction identical to true facies, similar to the ones by Dodd et al (2018) Scenario B: 4 combinations predict an alternative depositional scenario Halotel, McCarty, Gardiner, Demyanov, 2019, IAMG conference

  11. How certain is the classifier? Entropya measure of diversity Sandstone with mudstone interbeds Hybrid Event Bed High 1.0 0.8 0.6 2460 0.4 0.2 Normalized entropy True facies Prediction scenario A Prediction scenario B 0.0 From Dodd et al (2018) From Dodd et al (2018) Low

  12. Describe Variability and heterogeneity in subsurface models

  13. The SOA Problem • Seismic imaging is limited by complex geological structures • State-of-the-art techniques are unable to produce a good image. ? Nathan Amaral, Heriot-Watt MSc thesis, 2019; Courtesy – Conoco Phillips

  14. The Proposed Solution • PS images bring more information at SOAs.  • PP images can be inferred from PS information with physics • Machine learning would be able to bypass physics limitations in the PP – PS problem Farfour and Yoon, 2016 Nathan Amaral, Heriot-Watt MSc thesis, 2019; Reidar

  15. Generative Adversarial Networks (GANs) • Generator creates and discriminator classifies it • Adversarial training: similar to cop and counterfeiter https://skymind.ai/wiki/generative-adversarial-network-gan Goodfellow et al (2014)

  16. A real mature field application • Seismic volume (length: 717, width: 1129) Nathan Amaral, Heriot-Watt MSc thesis, 2019; Courtesy ConocoPhillips

  17. Flow Generator PS Boundary conditions Discriminator Encoder Bottleneck PP PS Decoder Classifies (real or fake) PP Backpropagate Real / Fake Nathan Amaral, Heriot-Watt MSc thesis, 2019

  18. Optimization and training • Adversarial training: discriminator and generator loss fluctuates. Nathan Amaral, Heriot-Watt MSc thesis, 2019

  19. Optimization and training • Competitive nature: the higher the discriminator loss, – the lower the generator loss (and vice-versa). Nathan Amaral, Heriot-Watt MSc thesis, 2019

  20. Optimization and training • No network completely wins the competition, both get better at what they do (create or classify) Nathan Amaral, Heriot-Watt MSc thesis, 2019

  21. Boundary conditions: results (test set) • Translates PS condition. • Respects boundaries. • Does not recover amplitudes. • Struggles with noise and complex geology (faults, strong dips, etc.). Nathan Amaral, Heriot-Watt MSc thesis, 2019

  22. Blind test: SOA Nathan Amaral, Heriot-Watt MSc thesis, 2019

  23. GAN PP SOA prediction Nathan Amaral, Heriot-Watt MSc thesis, 2019

  24. What does GAN see? • Visualize layers to understand the process behind feature generation • Increases interpretability • May lead to an informed optimization of the network Nathan Amaral, Heriot-Watt MSc thesis, 2019

  25. Outcomes Limitations • GAN is able to produce good quality PP images respecting the PS and boundary conditions on a real data • Main features and reflectors are preserved in the test samples, although an exact reproduction is not achieved • GAN model struggles in areas of more complex geology (faults, discontinuities, strong dips, etc.) • Unrealistic amplitudes as a result of upscaling • Blind test on SOAs had poor quality polluted with artifacts  Nathan Amaral, Heriot-Watt MSc thesis, 2019

  26. Predict Outcomes of subsurface resources development

  27. Where are the remaining reserves? A real mature field challenge • 40 years of production with active aquifer support • Good reservoir quality • High heterogeneity • Densely drilled: hundreds of wells Relatively poorly understood geology • Poor quality data • Large geological uncertainty and heterogeneity Gas Oil Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations Water Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations

  28. ML to guide the model update • Multiple models obtained from AI equipped history matching Large Remaining Reserves Small Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations

  29. Correlation of causation? • Is the discovered zonation correlated with some meaningful geological feature? • What is possibly the cause? Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations

  30. Correlation of causation? NTG in rock type 3 Depth to Carbonate Layer Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations

  31. Predict remaining reserves with confidence Reserves confidence map Large Large oil accumulationgood match Large oil accumulationpoor match Remaining Reserves Small oil accumulationpoor match Small oil accumulationgood match Small Buckle, et al, ThPG06:, EAGE Petroleum Geostatistics, 2019Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations

  32. Summary: added value from ML • Able to identify possible alternatives that impact reservoir decisions from the bulk of data • Tackle generative problem of obscure pattern reconstruction to improve interpretation • Help to introduce reservoir features that have been overlooked in the model to make better predictions

  33. AI needs more explanatory power to become more interpretative to predict causation rather than correlation

  34. Way Forward Current: Solution = ML expertise + data + computation Domain expert + Future: Solution = data + computation + auto search for best AI architecture after Jeff Dean, head of AI, Google

  35. Vasily Demyanov v.demyanov@hw.ac.uk Thank you

  36. A way to interpretable AI Introduce cognitive reasoning to AI DH Park, LA Hendricks, Z Akata, A Rohrbach, B Schiele, T Darrell, M Rohrbach, (2018) UC Berkley, U Amsterdam, Facebook AI

  37. Acknowledgements North Falkland study Ekofisk study Vasily Demyanov (v.demyanov@hw.ac.uk)

More Related