We analyse a strategy to optimize the computational effort of large-scale electromagnetic (EM) modeling and particularly inversion, the latter usually requiring a large number of forward modeling solutions. While we are interested in finely gridded earth models to capture realistic structures, the forward modeling operator may act upon a coarser simulation grid, or a subsection of the model grid, thus providing significant potential for computational speed-up. After briefly outlining the methodology of the grid transfers and its implications on the inversion scheme, we demonstrate the method on a marine CSEM survey example.

The growing use of the controlled-source electromagnetic method (CSEM) for exploration applications has been driving the technical development of data acquisition, as well as three-dimensional (3D) modeling and imaging techniques. However, targeting increasingly complex geological environments also further enhances the problems inherent in large-scale inversion, such as non-uniqueness and resolution issues. In this paper, we report on two techniques to mitigate these problems. We use 3D joint CSEM and MT inversion to improve the model resolution. Further, a hybrid model parameterization approach is presented, where traditional cell-based model parameters are used simultaneously within a parametric inversion.

The process of going from course-scale seismic reservoir parameters produced from AVA inversion to a fine-scaled reservoir permeability model that fits production data usually results in a permeability and associated seismic parameter model that fits production data but not the original input seismic data. Rarely an iterative process is employed that attempts to find a model that fits both the seismic and production data, but even when successful this is a very expensive. We develop and demonstrate a process that incorporates AVA stochastic inversion with machine-learning to produce fine-scale permeability (and associated seismic parameter) models that fit both the observed seismic AVA and the production data. The process involves training a cGAN on synthetic flow-AVA models to generate a conditional probability function for find-scaled permeability given course-scaled seismic parameters and applying this to the stochastic ensemble of course-scaled AVA inversion models. We show that the resulting MAP permeability model fits production data significantly better than permeability derived from the original AVA models. To further improve production data fit the ensemble of permeability models can be flow-simulated and the closest match to production data chosen to provide the ultimate solution that fits both seismic and production data.

© 81st EAGE Conference and Exhibition 2019. All rights reserved. The process of going from course-scale seismic reservoir parameters produced from AVA inversion to a fine-scaled reservoir permeability model that fits production data usually results in a permeability and associated seismic parameter model that fits production data but not the original input seismic data. Rarely an iterative process is employed that attempts to find a model that fits both the seismic and production data, but even when successful this is a very expensive. We develop and demonstrate a process that incorporates AVA stochastic inversion with machine-learning to produce fine-scale permeability (and associated seismic parameter) models that fit both the observed seismic AVA and the production data. The process involves training a cGAN on synthetic flow-AVA models to generate a conditional probability function for find-scaled permeability given course-scaled seismic parameters and applying this to the stochastic ensemble of course-scaled AVA inversion models. We show that the resulting MAP permeability model fits production data significantly better than permeability derived from the original AVA models. To further improve production data fit the ensemble of permeability models can be flow-simulated and the closest match to production data chosen to provide the ultimate solution that fits both seismic and production data.

© 81st EAGE Conference and Exhibition 2019. All rights reserved. The process of going from course-scale seismic reservoir parameters produced from AVA inversion to a fine-scaled reservoir permeability model that fits production data usually results in a permeability and associated seismic parameter model that fits production data but not the original input seismic data. Rarely an iterative process is employed that attempts to find a model that fits both the seismic and production data, but even when successful this is a very expensive. We develop and demonstrate a process that incorporates AVA stochastic inversion with machine-learning to produce fine-scale permeability (and associated seismic parameter) models that fit both the observed seismic AVA and the production data. The process involves training a cGAN on synthetic flow-AVA models to generate a conditional probability function for find-scaled permeability given course-scaled seismic parameters and applying this to the stochastic ensemble of course-scaled AVA inversion models. We show that the resulting MAP permeability model fits production data significantly better than permeability derived from the original AVA models. To further improve production data fit the ensemble of permeability models can be flow-simulated and the closest match to production data chosen to provide the ultimate solution that fits both seismic and production data.

In this paper we consider the use of, time-domain electromagnetic, DC electrical and injection-production data in isolation and in combinations in order to investigate their potential for monitoring spatial fluid saturation changes within reservoirs undergoing enhanced oil recovery. We specifically consider two scenarios, a CO2 EOR within a relatively shallow reservoir, and a water flood within a deep carbonate reservoir. The recognition of the signal-enhancing role that electrically high conductivity steel well casings play makes the use of EM data possible in both these scenarios. The work has demonstrated that reservoir fluid saturation changes from EOR processes produce observable changes in surface electric fields when surface-to-borehole (deep reservoirs), and surface-tosurface (shallow reservoirs) configurations are used and the steel well casings are accurately modeled. Coupled flow and TDEM data inversion can significantly improve estimate of fluid saturation levels and location compared to inversion of flow data only. The inversion of surface time-domain electric fields, including DC fields can resolve volumetric and resistivity differences that can distinguish between various water flood scenarios. Coupled flow and DC data can resolve the size and orientation of elongated fracture zones within limits that are considered a significant improvement over estimates made with traditional data.