Applied Sciences (Apr 2022)

Gridding Optimization for Hydraulic Fractured Well in Reservoir Simulation Using Well Test Analysis for Long Term Prediction

  • Jang Hyun Lee,
  • Berihun Mamo Negash

DOI
https://doi.org/10.3390/app12094551
Journal volume & issue
Vol. 12, no. 9
p. 4551

Abstract

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Analytic models, complex simulations, and simple models are being used to predict the production performance of hydraulically fractured shale. Analytical models such as decline curve analysis and rate transient analysis are used for a quick evaluation of reservoir performance. However, they have considerable limitations. For instance, decline curve analysis cannot honor the physical phenomena in shale wells that are related to hydraulic fracture, reservoir characteristics, and fluid flow. On the other hand, even though explicit hydraulic fracture modeling is the most comprehensive approach when compared with other traditional techniques, it cannot guarantee to model enough hydraulic fracture effects. Hence, calibration of the model, which commonly is referred to in the oil and gas industry as history matching, becomes a must. However, history matching of an explicit hydraulic fracture model with limited information is time-consuming and cumbersome. Especially history matching of a full field shale gas/oil model with many wells is a daunting task. In this study, we propose a workflow to integrate numerical reservoir simulation and well test analysis. In the workflow, information such as fracture half-length and enhanced effective permeability are obtained from pressure transient analysis and are used to calibrate grid properties in the vicinity of the plane covered by the fracture length and width. Finally, the simulation model is calibrated using pressure and flow rate data, and it is used for the long-term performance prediction of a hydraulic fractured well. The workflow was evaluated by using a synthetic reservoir model whose permeability mimicked that of a shale formation. As a result, the workflow thus enabled the use of coarse grid blocks, which, in turn, reduced the simulation time to just 1.5% of the simulation runtime consumed by a reference fine grid model.

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