SPE OKC July Tech Talk

When:  Jul 9, 2020 from 10:00 AM to 11:30 AM (CT)
Associated with  Oklahoma City Section
Analyzing Hydraulic Fractures Using Time-Lapse Electric Potential Data Jason Hu, Independent Data Science Consultant

About this Event

Analyzing Hydraulic Fractures Using Time-Lapse Electric Potential Data

Speaker Bio: Jason Hu is currently an independent data science consultant. For the past two years Jason worked at Arundo Analytics and focused on using computer vision techniques and time-series analysis to solve industrial challenges. Jason has delivered data-driven solutions and improved digital practices for industrial clients in the oil & gas, renewable energy, and utilities sector. Applications include oil and gas production monitoring and forecast, power plant emission forecast, utility load forecast, heavy industry equipment monitoring, and diagram digitization. Jason has a BS degree in Petroleum Engineering from University of Houston and MS degree in Energy Resources Engineering from Stanford University.

Abstract: Characterizing the fractures is an important task to improve the understanding and utilization of hydraulic fracturing. As an approach to augment and improve on the existing methods, time-lapse electric potential measurements can be used to characterize subsurface features. In this study we investigate the characterization of fracture length and fracture density by using time-lapse electric potential data.

A new borehole ERT (electric resistivity tomography) method designed specifically for hydraulic fracture characterization is purposed to better capture reservoir dynamics during hydraulic fracturing. This method can attain high resolution electric potential data by implementing electrodes in or near boreholes and monitor electric potential distribution near the horizontal fracture zone. The reservoir pressure distribution in a porous medium is analogous to the electrical potential distribution in an electrically conductive medium. We build a fracture model using discrete fracture modeling and generate time-lapse electric potential signals by running electric simulation in parallel with flow simulation. The time-lapse electric potential data generated by the simulations is considered as measured data and subsequently used to analyze fracture characteristics.

The results of this work show time-lapse electric potential data is capable of capturing flow dynamics during fracturing process. Using the proposed borehole ERT method we successfully estimate the true fracture length and true fracture density of a constructed fracture model. We are able to determine the best locations of the constructed reservoir to place the electrodes, and we find the maximum noise level of the electric potential data under which allow the purposed framework to make robust fracture length and fracture density estimate through sensitivity analysis. Our purposed method offers a new approach to make robust estimate of fracture length and fracture density in a fractured reservoir. Electric potential data are mostly used for well logging in petroleum exploration to date. This study demonstrates a novel way of using electric potential data in unconventional settings and opens possibilities for more applications such as production monitoring.

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