Realizing 4D CSEM Value on Deep Water Reservoirs – The Jubarte Case Study

Zerilli, Andrea; Labruzzo, Tiziano; Andreasi, F. Golfré; Menezes, Paulo T. L.; Crepaldi, João Lucas; Alvim, L.D.M.

doi:10.3997/2214-4609.201800749

Cited by 4 publications

(9 citation statements)

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“…This improvement is caused by effectively eliminating the nonuniqueness problem-a major inversions challenge. Similar findings have been reported by Zerilli et al (2018) where "localized" inversions performed much better than "mesh-based" inversions. One can argue therefore that 4-D CSEM should in general have a better resolution than 3-D CSEM thanks to the fact that resistivity changes are localized within a small volume where production (or injections) takes place.…”

Section: Discussionsupporting

confidence: 88%

“…Numerous synthetic studies have shown that CSEM sensitivity can be sufficiently high to resolve small resistivity changes in various monitoring scenarios. Some of these studies are based on synthetic models of existing producing hydrocarbon fields (Ziolkowski et al 2010;Black et al 2011;Colombo & McNeice 2013;Zerilli et al 2018). Some studies use reservoir simulations to demonstrate that CSEM data can be directly linked to monitoring changes in the fluid saturation and motion of the flooding front (Ziolkowski et al 2010;Liang et al 2012;Shahin et al 2012;Zerilli et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these studies are based on synthetic models of existing producing hydrocarbon fields (Ziolkowski et al 2010;Black et al 2011;Colombo & McNeice 2013;Zerilli et al 2018). Some studies use reservoir simulations to demonstrate that CSEM data can be directly linked to monitoring changes in the fluid saturation and motion of the flooding front (Ziolkowski et al 2010;Liang et al 2012;Shahin et al 2012;Zerilli et al 2018). Other papers (Lien & Mannseth 2008;Wirianto et al 2010) show how one can deal with typical challenges of 4-D CSEM analysis such as noise, measurement and modelling errors, by means of for example optimizing acquisition geometry.…”

Section: Introductionmentioning

confidence: 99%

“…However, none of them investigated the effect of acquisition non-repeatability on the quality of the inverted 4-D response. Zerilli et al (2018) state that 'strong' non-repeatability was mitigated in their inversions but gives no quantitative measure of non-repeatability 'strength' or its effect on inversion results. The aim of the present work is to fill the existing gap and establish new repeatability requirements for 4-D CSEM, applicable when 4-D effects are analysed in the model domain.…”

Section: Introductionmentioning

confidence: 99%

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Time-lapse CSEM: how important is survey repeatability?

Shantsev

Nerland

Gelius

2020

Geophysical Journal International

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Summary An important concern for time-lapse studies using the controlled-source electromagnetic (CSEM) method is repeatability of acquisition parameters for the base and monitoring surveys. We consider a challenging case when variations in source and receiver positions, conductivity of seawater etc. will lead to differences in the recorded EM fields that exceed EM response due to production-induced changes in the reservoir resistivity. We show that even in that case, 4D response can often be clearly resolved if the base and monitor EM datasets are inverted to produce 3D resistivity distributions. More precisely, for a synthetic model of the Snøhvit gas field, changes in the inverted resistivity maps caused by such non-repeatability will be at least 10 times smaller than the time-lapse differences in the reservoir resistivity. By contrast, measurement errors and poor knowledge of background resistivity may reduce the resolution of 4D CSEM to a much stronger degree. Analysis of field CSEM data from the Wisting oil field supports our conclusion about strongly relaxed repeatability requirements when 4D effects are established by examining inverted resistivity volumes rather than by looking at raw EM data.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-lapse CSEM: how important is survey repeatability?

Shantsev

Nerland

Gelius

2020

Geophysical Journal International

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“…Most 4D studies are limited to ranges from pre-production to periods of up to 28 years of injection and monitoring [18]. An exception is provided by the long-term study of [15], where they investigated 110 years of CO 2 injection in a deep saline aquifer for storage purposes.…”

Section: Introductionmentioning

confidence: 99%

Time-Lapse CSEM Monitoring: Correlating the Anomalous Transverse Resistance with SoPhiH Maps

Menezes

Correa²,

Alvim³

et al. 2021

Energies

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The CSEM method, which is frequently used as a risk-reduction tool in hydrocarbon exploration, is finally moving to a new frontier: reservoir monitoring and surveillance. In the present work, we present a CSEM time-lapse interpretation workflow. One essential aspect of our workflow is the demonstration of the linear relationship between the anomalous transverse resistance, an attribute extracted from CSEM data inversion, and the SoPhiH attribute, which is estimated from fluid-flow simulators. Consequently, it is possible to reliably estimate SoPhiH maps from CSEM time-lapse surveys using such a relationship. We demonstrate our workflow’s effectiveness in the mature Marlim oilfield by simulating the CSEM time-lapse response after 30 and 40 years of seawater injection and detecting the remaining sweet spots in the reservoir. The Marlim reservoirs are analogous to several turbidite reservoirs worldwide, which can also be appraised with the proposed workflow. The prediction of SoPhiH maps by using CSEM data inversion can significantly improve reservoir time-lapse characterization.

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Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

Crepaldi,

de Figueiredo,

Zerilli

et al. 2024

GEOPHYSICS

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A Bayesian approach is proposed to estimate litho-fluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Since computing this probability for all possible facies configurations may be impractical, a Markov Chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geological scenario and a well from a selected marine oil field. Two other wells from the same reservoir were used to gather prior distributions. Data from the well, calibration of the rock physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward modeling adaptations on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical datasets are then inverted for litho-fluid facies models under different prior inputs. Two challenging geoelectric scenarios were also tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating both geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also show potential for quantitative reservoir interpretations.

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Realizing 4D CSEM Value on Deep Water Reservoirs – The Jubarte Case Study

Cited by 4 publications

References 0 publications

Time-lapse CSEM: how important is survey repeatability?

Time-lapse CSEM: how important is survey repeatability?

Time-Lapse CSEM Monitoring: Correlating the Anomalous Transverse Resistance with SoPhiH Maps

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

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