He has authored more than 170 technical papers and carried out more than 60 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. He is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to machine learning and data mining. He has been honored by the US Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of US Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage (2014-2016).
Chapter 5: Pitfalls of Using Machine Learning in Reservoir Modeling
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Published:2017
"Pitfalls of Using Machine Learning in Reservoir Modeling", Data-Driven Reservoir Modeling, Shahab D. Mohaghegh
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Rushing to evaluate the capabilities of a new technology as it starts to find its foothold in a new industry seems to be a common issue. Such efforts usually cut both ways. On the one hand, there are those that intentionally or unintentionally try to undermine the new technology and portray it as another fad that will fizzle away sooner or later, so they try to contribute to its quicker demise. On the other hand, there are those who with good intentions try to promote the technology, but present a superficial implementation of it and in some cases (not all) prepare the groundwork for those who will attack the technology for its lack of depth and minimal enhancements in the face of big promises.
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