EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs
KAUST DepartmentEarth Science and Engineering Program
Physical Sciences and Engineering (PSE) Division
Environmental Science and Engineering Program
Earth Fluid Modeling and Prediction Group
Computational Transport Phenomena Lab
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AbstractNew developments of electromagnetic and seismic techniques have recently revolutionized the oil and gas industry. Time-lapse seismic data is providing engineers with tools to more accurately track the dynamics of multi-phase reservoir fluid flows. With the challenges faced in distinguishing between hydrocarbons and water via seismic methods, the industry has been looking at electromagnetic techniques in order to exploit the strong contrast in conductivity between hydrocarbons and water. Incorporating this information into reservoir simulation is expected to considerably enhance the forecasting of the reservoir, hence optimizing production and reducing costs. Conventional approaches typically invert the seismic and electromagnetic data in order to transform them into production parameters, before incorporating them as constraints in the history matching process and reservoir simulations. This makes automatization difficult and computationally expensive due to the necessity of manual processing, besides the potential artifacts. Here we introduce a new approach to incorporate seismic and electromagnetic data attributes directly into the history matching process. To avoid solving inverse problems and exploit information in the dynamics of the flow, we exploit petrophysical transformations to simultaneously incorporate time lapse seismic and electromagnetic data attributes using different ensemble Kalman-based history matching techniques. Our simulation results show enhanced predictability of the critical reservoir parameters and reduce uncertainties in model simulations, outperforming with only production data or the inclusion of either seismic or electromagnetic data. A statistical test is performed to confirm the significance of the results. © 2014 Elsevier B.V. All rights reserved.
SponsorsWe acknowledge the support of Olwijn Leeuwenburgh and Fabio Ravanelli in stimulating the idea for the integration of the history matching methods into the MRST Sintef framework. The work presented in this paper has been supported in part by the project entitled Simulation of Subsurface Geochemical Transport and Carbon Sequestration, funded by the GRP-AEA Program at King Abdullah University of Science and Technology (KAUST).