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International Journal for Uncertainty Quantification
IF: 4.911 5-Year IF: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2020033602
Forthcoming Article

Data-Driven Calibration of P3D Hydraulic Fracturing Models

Souleymane Zio
Ecole Polytechnique de Ouagadougou
Fernando A. Rochinha
COPPE, Universidade Federaldo Rio de Janeiro Brazil


Modeling the propagation of a hydraulic fracture is challenging due to the complex nonlinear equations and the presence of multiple scales behavior at the fracture tips. These complexities have motivated researchers to propose simplified models relying on constrained fracture geometry patterns. Amongst them, in the oil and gas industry domain, the Pseudo-3D (P3D), that computes fracture evolution in reservoirs confined by symmetric stress barriers, is frequently employed. The different assumptions made to obtain the P3D model lead to inaccurate predictions, like the overestimation of fracture height for important operating conditions. To correct such drawbacks, we propose a model discrepancy term in a multi-fidelity approach. The main difficulty associated is to construct a mapping between the inputs and outputs that faithfully represents the error between high and low fidelity models. In this paper, we investigated the efficiency modeling such discrepancy by using Gaussian processes and artificial neural networks. In this analysis, we employ three data sets with different input-output dimensions. The best model obtained is then used for carrying out an Uncertainty Quantification analysis aiming at identifying the impact of parametric uncertainty in fracture propagation computation.