The future is here; a machine can learn games and beat the world’s best players. What a fascinating time we are living in—Industrial Revolution 4.0.
In December 2017, news broke that AlphaZero decisively beat the world’s best players in chess after its older sibling, AlphaGo, defeated the world champions in the ancient game of go a couple of years earlier. These games have been won by machine learning and artificial intelligence.
What effects will Industrial Revolution 4.0 have on the upstream oil and gas business in general and petrophysics in particular? We should see more intelligence and automation in measurements, processes, work flows, and operations, which should result in more-consistent results, better-quality answer products, and less nonproductive time (i.e., improved quality, efficiency, and productivity with less cost).
Historically, petrophysics is based on physical principles or empirical relationships, as illustrated in paper SPE 191296 on predicting crude-oil viscosity. With Industrial Revolution 4.0, a new era in petrophysics has begun. The logging tools are smarter (as demonstrated in paper SPE 190062 dealing with environmental corrections to some of the deliverables of pulsed-neutron logs that can be performed automatically). And the operational and data-interpretation work flows are more automated (as shown in paper SPE 187040, which details formation testing and sampling jobs that can be done semiautomatically through standardizing terminologies, measurement uncertainties, and data quality-control criteria).
In playing games such as go and chess, machines learn on the basis of man-made game rules. In petrophysics, rules are often data-driven, so data quality becomes critically important. It is always true that having bad data is worse than having no data.
Density, representativeness, and coverage are other parameters of the data besides data quality that are required for data-driven petrophysics.
This Month's Technical Papers
Recommended Additional Reading
SPE 189807 Characterization of Reservoir Quality in Tight Rocks Using Drill Cuttings: Examples From the Montney Formation, Alberta, Canada by A. Ghanizadeh, University of Calgary, et al.
SPE 188804 Low-Resistivity Pay Identification in Lower Cretaceous Carbonates, Onshore UAE by J.L. Ruiz, ADCO, et al.
SPE 187371 Saturation Mapping in the Interwell Reservoir Volume: A New Technology Breakthrough by Alberto F. Marsala, Saudi Aramco, et al.
|S. Shouxiang (Mark) Ma, SPE, is a senior consultant overseeing research, technology, and professional development with the Reservoir Description Division of Saudi Aramco. He serves on the Petroleum Engineering Special Core Analysis Council and Technologist Development Program Committee and champions the Logging Excellence Professional Network. Ma was supervisor of the Petrophysical Support and Study Unit, petrophysics adviser at the Upstream Professional Development Center, and petrophysical lead for logging operations. Before joining Saudi Aramco in 2000, he worked at the Exxon Production Research Company, Wyoming Western Research Institute, New Mexico Petroleum Recovery Research Center, and China Yangtze University. Ma has authored or coauthored more than 60 technical papers and holds several patents. He serves on the JPT Editorial Committee, is chairperson of the 2018 SPE Annual Technical Conference and Exhibition Formation Evaluation Committee and the 2019 International Petroleum Technology Conference Education Week Committee, and was chairperson of the 2012–13 SPE Formation Evaluation Award Committee. Ma holds a BS degree from China Petroleum University and MS and PhD degrees from New Mexico Tech, all in petroleum engineering. He can be reached at firstname.lastname@example.org.|
Shouxiang (Mark) Ma, SPE, Senior Consultant, Saudi Aramco
01 August 2018
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