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.

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.

In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.

Seismic tube waves, produced by flexure of the well boundary, pressure changes in the fluid in the well, and deformation of the material immediately surrounding the well, are particularly sensitive to variations in the state of the well. We evaluate a direct approach for generating and observing tube waves as a means of detecting well damage. While we find that it can be difficult to reliably excite observable tube waves without a very strong surface source, time-frequency techniques can be employed to increase the detectability of tube wave reflections. New technologies, particularly distributed acoustic sensing, hold great promise for evaluating well integrity by monitoring tube waves, temperature changes, and seismic noise due to well deformation and fluid leakage.