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Novel Forward Model Quantifies Uncertainty of Fracture Networks

A major portion of the uncertainty found in shale reservoirs is the result of the distribution and properties of the fracture network. However, explicit fracture models are rarely used in uncertainty quantification because of their high computational cost. This paper presents a work flow to match the history of reservoirs featuring complex fracture networks with a novel forward model. The efficiency of the model allows fractures to be characterized explicitly, and the corresponding uncertainty about the distribution and properties of fractures can be evaluated.

Introduction

For low-permeability unconventional shale reservoirs, the fracture network greatly determines the performance of the wells. Because of the large uncertainty of the fracture distribution, especially for natural fractures, the production behavior might differ dramatically. A possible way to characterize fractures is by use of core data, well-logging data, or seismic data. However, these data are either sparse in nature, and thus cannot be used to determine the exact location of fractures, or low in accuracy because of limited quality. An alternative approach is to use production data and characterize the fractures through history matching.

Various approaches have been proposed for automatic history matching. Most of these were evaluated with conventional reservoir models through upscaling (partially the result of the cost of generating explicit fracture models and performing simulations). For fractured reservoir models, especially when explicit fracture models are used, nonlinearity is even more significant and challenges the applicability of the existing methods. In this work, the authors use the improved compartmental embedded discrete fracture model (cEDFM), combining the level-set approach and the ensemble Kalman filter (EnKF) for history matching. The cEDFM has a much better accuracy compared with the original embedded discrete fracture model (EDFM) in handling flux across the direction of fractures, which is a common case for interwell flow.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191395, “Uncertainty Quantification of the Fracture Network With a Novel Fractured-Reservoir Forward Model,” by Zhi Chai, SPE, Hewei Tang, SPE, Youwei He, and John Killough, SPE, Texas A&M University, and Yuhe Wang, Texas A&M University at Qatar, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed.
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Novel Forward Model Quantifies Uncertainty of Fracture Networks

01 April 2019

Volume: 71 | Issue: 4

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