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 9: History Matching the Top-Down Model
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Published:2017
"History Matching the Top-Down Model", Data-Driven Reservoir Modeling, Shahab D. Mohaghegh
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When it comes to reservoir models, the logic goes like this: “Would you believe a model’s forecast of the reservoir’s behavior in the future if it fails to match the production history of the reservoir in the past?” Answering this question should be quite easy: “No.” If a reservoir model fails to match the production history, it will enjoy little or no credibility in forecasting production into the future. However, the opposite is not necessarily a “Yes.” That is, if the question is posed as “Would you believe a model’s forecast of the reservoir’s behavior in the future if it is able to match the behavior of the reservoir in the past?,” then the answer should be “It depends” or “Yes, but only to a certain degree.”
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