A new-generation Embedded Discrete Fracture Model calibration workflow applied to the characterization of complex naturally fracture reservoir
作者机构：Beijing Karst S&T Limited Sim Tech LLC RIPED University of Texas at Austin
出 版 物：《Petroleum Research》 (石油研究(英文))
年 卷 期：2022年第01期
页 面：1-12页
学科分类：0820[工学-石油与天然气工程] 08[工学] 082002[工学-油气田开发工程]
基 金：funded by “CNPC Science and technology project: Fine evaluation and prediction technology for complex reservoirs in overseas natural gas reservoirs grant number 2018D-4305”
摘 要：Characterizing natural fractures has a decisive effect on production forecasts in fractured oil and gas reservoirs. Discrete Fracture Networks(DFN) constitutes the main modeling framework for fractured geosystems. However, myriads of uncertainties are enclosed prior modeling representative stochastic or deterministic DFN ensembles. This paper presents a novel methodology for DFN calibration and an efficacious field application, which incorporates Well-testing interpretation, Embedded Discrete Fracture Model(EDFM) framework, and numerical reservoir simulation. The proposed workflow starts with the DFN generation from seismic data, imaging logging data and core data. After multiple DFNs are modeled,well-test analysis is employed to calibrate the intrinsic properties of fractures at different locations. Then,these fracture networks are characterized dynamically by EDFM, which promotes capturing the optimal fracture model quickly screened. Finally, pressure and production history match are reached for the DFN realization that honors the optimal fracture model.
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