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

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8 Scopus citations


We propose a feature-oriented ensemble history-matching workflow with a focus on the integration of time-lapse seismic and electromagnetic (EM) data. The developed workflow consists of two main steps. First, the rock cross-properties, such as water saturation and porosity relating to both seismic and EM attributes, are estimated by joint inversion of seismic and EM data using an iterative ensemble smoother (ES). Second, the remaining model parameters of interest, such as permeability, are calibrated using the updated crossproperties. To assimilate the inverted-saturation information efficiently, we take a feature-oriented integration approach in which front positions are identified from the inverted-saturation field. Related model parameters are then conditioned to the interpreted fronts using the iterative ES with a distance parameterization. The novelty of the proposed approach consists of combining the feature-oriented history matching with ensemble-based geophysical inversion to achieve an efficient joint integration of multiple sources of geophysical data. The performance of the proposed history-matching workflow is examined using a 2D channelized reservoir model and a morerealistic 3D reservoir model with a crosswell configuration for seismic and EM surveys. It is demonstrated that the developed workflow provides a novel and effective way to calibrate reservoir models with multiple sources of geophysical data. The experimental results show a positive synergy effect on the characterization of model variables by jointly assimilating seismic and EM data.
Original languageEnglish (US)
JournalSPE Journal
StatePublished - Oct 5 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-02-23
Acknowledgements: Support for authors is provided by the research project “Efficient Integration of Electromagnetic Tomography into Reservoir History
Matching” which is funded by Saudi Aramco. The authors also thank Femke C. Vossepoel, Wim Mulder, and Marwan Wirianto for providing the multigrid EM forward solver.


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