Analyzing Hydraulic Fractures Using Time-Lapse Electric Potential Data


Jason Hu







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Hydraulic fracturing is the process of injecting fluids at high pressure to create conductive channels in low permeability reservoirs so that oil and gas can flow through. Hydraulic fracturing will continue to play a key role in the future of oil and gas production, and characterizing the fractures is an important task to improve the understanding and utilization of the process. As an approach to augment and improve on the existing methods, resistivity measurements can be used to characterize subsurface features. The lithology, pore fluid chemistry, and water content affect the spatial distribution of resistive and capacitive characteristics of the subsurface. Fractures created by hydraulic fracturing are saturated with water and show reduced resistivity, which provides the opportunity to extract fracture characteristics by monitoring the change in resistivity distribution in the subsurface as a function of time.

The purpose of this work was to investigate a fracture characterization approach by making use of electrical potential data. We focused mainly on using electric field simulation and inverse analysis to examine subsurface electric field behavior due to fracture creation. We considered a new borehole method designed specifically for hydraulic fracture characterization, which modifies a single-borehole survey method and utilizes a permanent resistivity array. This method can attain higher resolution by implementing electrodes in or near boreholes and monitoring the electric potential distribution near the horizontal fracture zone.

The electric potential distribution in steady-state flow through a porous medium is analogous to the electrical potential distribution in an electrically conductive medium. By solving the flow equation and the Poisson equation for electrical field numerically, we can determine the fluid distribution and electric potential distribution at every time interval. The time-lapse electrical data generated by the simulator was considered as measured data and subsequently used for analyzing fracture characteristics. Inverse analysis was used in this work to find fracture parameters such as fracture length, orientation, and more. By deploying our simulator as the forward model, we used an objective function to capture the difference between measured data and data generated by the forward guess. Then we used a gradient-free nonlinear optimization scheme to minimize the objection function in order to achieve the best guess of targeted parameters.

The preliminary results of this work show that time-lapse electrical data is capable of capturing flow dynamics during fracturing process and has the potential to further assist fracture characterization.

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