Petro-electric modeling for CSEM reservoir characterization and monitoring

Abstract

The controlled-source electromagnetic (CSEM) method has been successfully applied to petroleum exploration; however, less effort has been made to highlight the applicability of this technique for reservoir monitoring. This work appraises the ability of time-lapse CSEM data to detect the changes in fluid saturation during water flooding into an oil reservoir. We simulated a poorly consolidated shaly sandstone reservoir based on a prograding near-shore depositional environment. Starting with an effective porosity model simulated by Gaussian geostatistics, dispersed clay and dual water models were efficiently combined with other well-known theoretical and experimental petrophysical correlations to consistently simulate reservoir properties. The constructed reservoir model was subjected to numerical simulation of multiphase fluid flow to predict the spatial distributions of fluid pressure and saturation. A geologically consistent rock physics model and a modified Archie's equation for shaly sandstones were then used to simulate the electrical resistivity, showing up to 60% decreases in electrical resistivity due to changes in water saturation during 10 years of production. Time-lapse CSEM data were simulated at three production time steps (zero, five, and ten years) using a 2.5D parallel adaptive finite element algorithm. Analysis of the time-lapse signal in the simulated multicomponent and multifrequency data set demonstrates that a detectable time-lapse signal after five years and a strong time-lapse signal after ten years of water flooding are attainable using current CSEM technology.

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

“…Controlled source electromagnetic (CSEM) data offer a reliable alternative to seismic monitoring (e.g. Constable, 2010; Orange et al., 2009; Shahin et al., 2012). Like any geophysical method, reservoir monitoring with CSEM data is also confronted as a unique set of practical obstacles.…”

Geophysics-Based Fluid-Facies Predictions Using Ensemble Updating of Binary State Vectors

“…Probabilistic and deterministic approaches have been proposed with different model parameterizations, in terms of resistivity or saturation. Time-lapse electrical resistivity inversions have been proposed in several applications (Berre et al 2011;Shahin et al 2012;Lien et al 2014;Tveit et al 2015;Commer et al 2016;Bergmann et al 2017).…”

Time-lapse CSEM: how important is survey repeatability?

“…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.…”