Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework

Abstract

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Efficient Joint 4D Seismic and Electromagnetic History Matching Using an Ensemble-Based Approach

“…In the previous study (Zhang and Hoteit, 2019), a distance-based Kalman gain localization was applied to integrate crosswell EM data, which was shown to be a viable option if the localization matrix was determined appropriately. However, to choose a proper localization matrix is not an easy task that usually involves a joint consideration of prior ensemble covariance, data sensitivity and ensemble size.…”

“…As discussed in Zhang and Hoteit (2019), the way to assimilate geophysical measurements like seismic and EM data is not unique. There are three different levels at which the extracted information from the raw data can be utilized for updating reservoir models.…”

“…There are three different levels at which the extracted information from the raw data can be utilized for updating reservoir models. Zhang and Hoteit (2019) examined the integration of crosswell EM data from the first two levels, which correspond to the conditioning on raw EM observations (after necessary data processing) and interpreted conductivity through geophysical inversion, respectively. They showed that for the feature-based integration approach its performance somehow depends on the quality of inversion results and is susceptible to observation bias and propagated errors introduced by the inversion.…”

Feature-Oriented Joint Time-Lapse Seismic and Electromagnetic History Matching Using Ensemble Methods

“…Secondly, the approach to assimilate geophysical data, such as seismic and EM data measurements, is not unique [50,60]. There are three different levels at which the extracted information from the raw data can be utilized for updating reservoir models, as shown in Fig.…”

“…In general, no matter which level is selected, obtaining a consistent comparison between measured (including interpreted or inverted geophysical attributes) and simulated data is not trivial. There is a growing investigation of incorporating different forms of geophysical data and studying their performances under the ensemble-based history-matching framework [51,15,30,27,60]. Lastly, geophysical data involve large uncertainty that stems from the whole acquisition-processing-interpretation process and is usually difficult to quantify.…”