Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs. (November 2022)

• Record Type: Journal Article
• Authors: Fang, Junling; Gong, Bin; Caers, Jef
• Is Part Of: Engineering. Volume 18(2022)
• Page Start: 116
• Page End: 128
• Publication Date: 2022-11
• Subjects: Bayesian evidential learning -- Falsification -- Fractured reservoir -- Random forest -- Approximate Bayesian computation; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2022.04.015
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: Many properties of natural fractures are uncertain, such as their spatial distribution, petrophysical properties, and fluid flow performance. Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulation, and hence to support reservoir performance predictions. The application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic cases. In field applications, however, one of the main problems is that the Bayesian prior is falsified, because it fails to predict past reservoir production data. In this paper, we show how a global sensitivity analysis (GSA) can be used to identify why the prior is falsified. We then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate model to match the production history. We apply these two approaches to a complex fractured oil and gas reservoir where all uncertainties are jointly considered, including the petrophysical properties, rock physics properties, fluid properties, discrete fracture parameters, and dynamics of pressure and transmissibility. We successfully identify several reasons for the falsification. The results show that the methods we propose are effective in quantifying uncertainty in the modeling and flow simulation of a fractured reservoir. The uncertainties of key parameters, such as fracture aperture and fault conductivity, are reduced.