Abstract
Abstract Recently, a new methodology has been developed to model hydraulic fracturing induced fracture network and simulate a shale reservoir as a dual porosity system. The reservoir geological and petrophysical data are integrated to build both structure and property models. Microseismic (MS) data are used to delineate stimulated reservoir volumes; event intensities are employed to estimate hydraulic fracture intensity. The fracture intensity is calibrated by using hydraulic fracturing job data and geomechanical properties through the fracture propagation mechanism. As part one of this title in previous study, a theoretical framework generalization for the new methodology was introduced. Modeling and calibration of fracture network by stage, by well, and by well pad are discussed. A superpositioning technique is used to process the overlapping of MS mapping responses from different stages. Different fracture network geometry and average fracture width are evaluated. Various stimulated volume expansion models are investigated for proppant placement estimations and fracture conductivity distributions. A procedure is also introduced to build a simulation model when MS data is lacking by utilizing treatment data, geomechanical parameters, and user experience on estimated stimulation volume configurations. In this paper, that framework generalization is updated with new analysis. We focus on details for implementation of the methodology, modeling workflow, and stepwise procedure. The process is comprehensive, giving full consideration to shale reservoir integrated modeling, data input, fracture network geometry configuration, intensity calibration, and calculation of dual-porosity model parameters for reservoir simulation and other fracture network properties. This extended methodology and workflow can be applied to single well and multiple-well modeling when the well drainage volumes are interconnected. It provides an effective means for modeling and simulation study as well as history-matching calibration of stimulated shale gas reservoirs. The new modeling methodology was applied to a Barnett shale field case, and sensitivity and history matching analyses were carried out.
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