*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Enhanced Reservoir Characterization and Uncertainty Quantification: A Multi-Data History Matching Approach
Speaker: Professor Ibrahim Hoteit, Earth Science and Engineering Physical Sciences and Engineering Division (PSE) King Abdullah University of Science and Technology (KAUST)
Abstract:
Reservoir characterization and prediction assume an essential role in reservoir management, assisting in determining production strategies for efficient recovery and future planning. History matching is at the heart of calibrating the reservoir simulators to best emulate and forecast the true reservoir formation. This process traditionally involved the fine-tuning of critical reservoir parameters based almost entirely on production data that are only observed at some wells. While adequate matches could be realized, these data are spatially rather sparse to accurately characterize the reservoir. Consequently, various 4D monitoring techniques have been developed for better exploration of the reservoir formation in space and time. These include time-lapse seismic and electromagnetic surveys, and gravity and satellite INSAR data, which provide different, but complimentary in information about the reservoir parameters. Up-to-date, very little has been undertaken to exploit the synergy from combining these different datasets, often separately interpreted and analyzed before they are provided as inputs to the reservoir simulator. In this talk I will present and discuss the development of an efficient multi-data reservoir characterization framework for simultaneously and directly incorporating all these datasets into the history matching process for best possible reconstruction, prediction, and uncertainty quantification of the reservoir formation. I will also outline how the resulting information could be used for planning and decision making.
