A new stochastic inversion workflow for time-lapse data: hybrid starting model and double-difference inversion

SUMMARY Time-lapse resistivity tomography bring valuable information on the physical changes occurring inside a geological reservoir. In this study, resistivity monitoring from controlled source electromagnetics (CSEM) data is investigated through synthetic and real data. We present three different schemes currently used to perform time-lapse inversions and compare these three methods: parallel, sequential and double difference. We demonstrate on synthetic tests that double difference scheme is the best way to perform time-lapse inversion when the survey parameters are fixed between the different time-lapse acquisitions. We show that double difference inversion allows to remove the imprint of correlated noise distortions, static shifts and most of the non-linearity of the inversion process including numerical noise and acquisition footprint. It also appears that this approach is robust against the baseline resistivity model quality, and even a rough starting resistivity model built from borehole logs or basic geological knowledge can be sufficient to map the time-lapse changes at their right positions. We perform these comparisons with real land time-lapse CSEM data acquired one year apart over the Reykjanes geothermal field.

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A new stochastic inversion workflow for time-lapse data: hybrid starting model and double-difference inversion

Cited by 10 publications

References 40 publications

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Time-lapse resistivity imaging: CSEM-data 3-D double-difference inversion and application to the Reykjanes geothermal field

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