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 7: Top-Down Modeling
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Published:2017
"Top-Down Modeling", Data-Driven Reservoir Modeling, Shahab D. Mohaghegh
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To efficiently develop and operate a petroleum reservoir, it is important to have a model. Currently, numerical reservoir simulation is the accepted and widely used technology for this purpose. Data-driven reservoir modeling, also known as top-down modeling (TDM), is an alternative (or a complement) to numerical simulation. TDM uses the Big Data solution (artificial intelligence, machine learning, and data mining) in order to develop (train, calibrate, and validate) full-field reservoir models that are based on field measurements (facts) rather than mathematical formulation of our current understanding of the physics of the fluid flow through porous media.
There are empirical technologies that forecast production, such as decline curve analysis and capacitance/resistance modeling (CRM), that were briefly discussed in Chapter 6. The main problem with these technologies is that they do not make use of a large amount of data that are usually available in mature fields.
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