EMSE: Synergizing EM and seismic data attributes for enhanced forecasts of reservoirs

Abstract

New 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. • Synergizing EM and seismic data improve reservoir forecasts. • Efficient integration of seismic and EM data attributes. • EnKF and iterative EnKF outperform EnRML. • Inclusion of seismic and EM reduces ensemble spreads of Ensemble Filter. • Better prediction of permeability and porosity spatial profiles.