HPC and Machine Learning collaboration: an industry view
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By Richard Cooper, Chief Executive Officer, Rock Solid Images This talk was presented at the EPCC-CeeD event 'Advanced technologies for Industry 4.0' in September 2018.
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HPC and Machine Learning collaboration: an industry view
- 1. HPC and machine learning collaboration: an industry view Rock Solid Images 19 September 2018 Advanced technologies for Industry 4.0 EPCC Edinburgh
- 2. © RSI 2018 rocksolidimages.com • Background & Motivation • Challenges • Summary Outline
- 3. © RSI 2018 rocksolidimages.com • Background & Motivation Outline
- 4. © RSI 2018 rocksolidimages.com Access to decision ready data a key survival factor for upstream oil & gas companies Retail real-estate Oil & gas Decision process Success factors Purchase or lease real estate Purchase or lease acreage Invest in infrastructure (buildings, roads) Invest in infrastructure (wells, pipelines) Divest or sell real estate Farm out or relinquish acreage Rapid access to reliable geographic knowledge Rapid access to reliable sub-surface knowledge Proficiency at raising and employing capital Proficiency at raising and employing capital Employ and retain exceptional people: ideas! Employ and retain exceptional people: ideas! Knowledge ready data available Simple 2D: Analytics Raw data available Knowledge ready data unavailable Complex, 3D: AI Rapid access to reliable geographic knowledge Rapid access to reliable sub-surface knowledge
- 5. © RSI 2018 rocksolidimages.com $ 1,000 per m3* $ 0.0001 per m3* Wells Dense, uniform vertical data Sparse, non-uniform lateral data Depth domain ABSOLUTE Seismic Sparse, uniform vertical data Dense, uniform lateral data Time domain RELATIVE Upstream Oil & Gas – Geology and Geophysics
- 6. © RSI 2018 rocksolidimages.com Motivation Typical E&P cash-flow project based upon the Brazil Fiscal System (Suslick, 2005)
- 7. © RSI 2018 rocksolidimages.com • Background & Motivation • Challenges Outline
- 8. © RSI 2018 rocksolidimages.com BP Survey of E&P Companies – early 2000’s Q: If you could ask your G&G supplier to improve just one thing? A: Make it Faster! “…the oil industry puts to use in exploration activities barely 5% of the seismic data it has collected.” Ulrich Spiesshofer, CEO ABB The Economist April 2017 “By the time I get an answer from a geophysicist, I’ve forgotten the question.” Dr. Nansen Saleri, former Head of Reservoir Management,Saudi ARAMCO Today - the status quo “We are drowning in information but starved for knowledge” John Naisbitt
- 9. © RSI 2018 rocksolidimages.com 3D seismic surveys in the Gulf of Mexico (USA) – Offshore Magazine Too much data? - seismic Complex, inconsistent workflow
- 10. © RSI 2018 rocksolidimages.com • LAS curves (image, digital, spliced, concatenated….) • Well parameters (KB, water depth, casing depths and bit sizes as curves, maximum recorded temperature) • Lat & Long and datum • Tools run per hole • Drilling mud properties in depth • Reservoir Fluid properties in depth • Mud log lithology curve is not available in LAS • Pressure & Temperature data in depth from reports and any other tests available • Flags or Marker in depth of operational issues in the main logs (e.g. tools got stuck, severe bore instability, etc.) • Data availability per log type including depth ranges (meaning zones with non-zero or null values) Too much data? - Wells Complex, inconsistent workflow RSI Well Database – Gulf of Mexico (USA)
- 11. © RSI 2018 rocksolidimages.com Challenges for AI/machine-learning in upstream G&G • Data issues: • Multiple data sets available in any given geographic area • Limited or no standard analysis workflows • Human bias in processing • Limited or no standard output format • Link data and meta-data • Convert unstructured meta-data to structured database • “Big Data” IT emphasis • Cloud access to data and software is not a solution! • G&G data more complex than consumer data • Machine Learning Guided learning • Elephant burgers – do the easy things first • Learn from specialists; training data and knowledge capture
- 12. © RSI 2018 rocksolidimages.com • Background & Motivation • Challenges Outline
- 13. © RSI 2018 rocksolidimages.com An example: LITHANN® • Neural network classification of seismic attributes • Developed by Dr. Tury Taner in early 2000’s as part of RSI’s LFP consortium and patented. • Uses a Kohonen self organizing map • Still in use today ! • KSOM often rebranded as “AI” Past Present Future Pseudo 2D color bar LITHANN®
- 14. © RSI 2018 rocksolidimages.com Past Present Future RSI “ERA” Project – Barents Sea • The regression approach provides a good first order indication of anisotropy. • Statistical relationships suggest underlying mechanisms linking anisotropy and the parameters considered, that could be modelled deterministically. • Training data is key !
- 15. © RSI 2018 rocksolidimages.com Determining lithology and fluid properties from seismic How to to this ? Past Present Future
- 16. © RSI 2018 rocksolidimages.com Case study: Prospect evaluation in the Falkland Islands DiscoveryProspect Past Present Future
- 17. © RSI 2018 rocksolidimages.com Calculating rock and fluid properties from seismic • Finds the optimum geometric combination of two or more attributes for sensitivity to a target attribute based on well log calibration. • Chooses from 64 possible combinations of elastic attributes • Builds a function relating attributes to target properties which can then be applied to seismic data. More details can be found in Alvarez et al, 2015, Interpretation, November 2015 Goal: Estimate a new attribute () that shows the highest correlation with the target property Past Present Future
- 18. © RSI 2018 rocksolidimages.com Results of the MARS analysis Past Present Future
- 19. © RSI 2018 rocksolidimages.com Project SWOOP: Streamlined WOrkflows for Optimsed Petrophysics using machine learning A.I. workflow 7 minutes per wellCurrent workflow: 7 days per well Conducted in partnership with EPCC – Edinburgh University’s AI team in the high performance computing centre. Commenced May 2018 This project is enabled by RSI’s well based rock physics database Past Present Future
- 20. © RSI 2018 rocksolidimages.com Petrophysics/rock physics workflow Can we develop machine learning approaches to assist petrophysicists and make the workflow more efficient ? Past Present Future
- 21. © RSI 2018 rocksolidimages.com Training data Barents Sea 124 wells Norwegian Sea 211 wells North Sea 150 wells Gulf of Mexico (US) 1200 wells East Java Sea 50 wells Central Graben 150 wells East Timor Sea 15 wells Alaska N Slope 30 wells Existing data base 2000 wells in the existing multi-client database Expand this to a global database… Estimate about 20,000 key wells should be accessible Training data must be CONSISTENT and COMPLETE Questions and challenges: • How much training data is ‘enough’ ? • How regionally focused should training data be ? • Is the training data biased ? • How do we deal with imperfect, noisy, gappy data ? Past Present Future
- 22. © RSI 2018 rocksolidimages.com Starting point: Can we predict mineralogy ? Training data: 42 wells from mid-Norway Challenges: • Need to predict multiple properties at once (proportions of minerals). • Bias in the training data: There is lots of quartz and clay, much less of everything else. • Non-uniqueness • Which algorithm ? We have tried: • Dirichlet regression (didn’t work very well) • Deep Neural Network (current second favourite) • Multi-layer perceptron • Boosted trees (current favourite) • How to choose ? • Currently looking into a parameter optimisation approach. • How do we characterize uncertainty in the result ? Past Present Future
- 23. © RSI 2018 rocksolidimages.com Training data: 12 training wells Past Present Future
- 24. © RSI 2018 rocksolidimages.com Questions and challenges • Can we use machine learning to streamline workflows ? • Yes – results are promising • …but we need to understand the effect of training data and assess how general a model can be built. • How good a model do we need ? • It doesn’t need to be perfect to be useful. None of our models are perfect ! • We’re comparing against a human interpretation – who is right ? • How good is good enough and how do we characterize that ? • What is the best algorithm to use? • How much training data is required? Does this need to be regionally focused, or can be build global training datasets? • How general will a trained machine learning approach be? For example, whereas you can imagine that results may be good within a single geological basin, will they be equally good on the other side of the planet? • How accurate will a machine generated petrophysical interpretation be? It does not need to be perfect to be useful: something that is right 70-80% of the time could still represent a huge time saving in the workflow. • Can we characterize/understand the uncertainty in the results? Past Present Future
- 25. © RSI 2018 rocksolidimages.com rockAVO: data, workflow and expertise • Provide near instantaneous access to rock physics information for all the world’s key well data • Key features: • Global well database • Replace people/SME intensive workflow with AI Past Present Future
- 26. © RSI 2018 rocksolidimages.com “We have had used the Atlas extensively in the Round application and still using it now for further work. It is very helpful to run scenarios and testing.” Lukoil “Thank you for the rockAVO (project), it is really powerful visualizing the impact of various reservoir properties.” Oryx Petroleum “The work and final deliverable (rockAVO) are very good, and exactly what was needed..” African Global Energy rockAVO: data, workflow and expertise Past Present Future
- 27. © RSI 2018 rocksolidimages.com • Background & Motivation • Challenges • Summary Outline
- 28. © RSI 2018 rocksolidimages.com Digital transformation will become a matter of survival for oil and gas companies Decision Today Digital Transformation Farm-in 2 per year 5 per year Farm-out 3 per year 10 per year Well placement 10 per year 30 per year Quality of decision Low High Portfolio size Limited by decision process Limited by capital Impact of Digital Transformation: • Rapid and efficient block promote • Fast farm-in, farm-out decisions • Reduction in pre-development costs • Pre-development timetable compression Portfolio Impact of Digital Transformation
Editor's Notes
- Darwin East: Shallow marine sandstone – early cretaceous 68m net pay, porosity up to 30% with an average 22% Permeability 337 mD Current assessment has 360 MMBO On 3D seismic the sand extends across Darwin east and West – represented by amplitude conformance to structure and class 3 AVO.
- Notice that in the Sw volume it was possible to identify the presence of fluid contacts in the Darwin East and West structures. In the Vclay volume the good lateral continuity of the shallow marine reservoir rock and the cap rock can be seen. In the porosity volume a decrease of porosity with depth in the reservoir rock can be observed, which can be produced by a compaction trend.
- DNN – better accuracy but can’t deal with missing values Boosted trees – just about as good, but can deal with gappy data – which for real geoscience data is important.