Inverse analysis of hydraulic fracturing tests based on artificial intelligence techniques

In the last decades, the study of fluid flow through discontinuities such as fractures has received significant attention from the petroleum industry owing to the discovery of naturally fractured reservoirs. Predicting the behavior of this type of reservoir is challenging and includes, as a crucial...

Full description

Saved in:
Bibliographic Details
Published in:International journal for numerical and analytical methods in geomechanics 2022-09, Vol.46 (13), p.2582-2602
Main Authors: Abreu, Rafael, Mejia, Cristian, Roehl, Deane
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Boreholes
Computer applications
Computing time
Crack propagation
Empirical analysis
Field tests
Fluid dynamics
Fluid flow
Fracture mechanics
Fractured reservoirs
genetic algorithm
Genetic algorithms
Geomechanics
Hydraulic fracturing
In situ tests
inverse analysis
Methodology
Methods
Neural networks
Parameter identification
Parameters
Petroleum
Petroleum industry
Pressure
Reservoirs
Tests
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the last decades, the study of fluid flow through discontinuities such as fractures has received significant attention from the petroleum industry owing to the discovery of naturally fractured reservoirs. Predicting the behavior of this type of reservoir is challenging and includes, as a crucial step, determining its geomechanical parameters. Several in situ tests and empirical relationships have been established to estimate these parameters. Nevertheless, these procedures have important limitations. This work proposes an artificial intelligence‐based methodology to identify geomechanical parameters from borehole injection pressure curves obtained during hydraulic fracturing tests. This methodology applies a genetic algorithm to minimize the sum of squared differences between the observed and predicted borehole pressure curves. Furthermore, an artificial neural network is adopted to build a proxy model that substitutes hydraulic fracturing numerical simulations, substantially reducing computational time. A proper recursive strategy is adopted to predict the borehole pressure curves. Additionally, a hyperparameter tuning technique selects appropriate neural network hyperparameters based on the multistep‐ahead prediction error. Finally, the framework is applied in a KGD problem that models hydraulic fracture propagation. The benchmark confirms the capability of the proposed methodology to identify geomechanical parameters from fracturing tests.
ISSN:0363-9061
1096-9853
DOI:10.1002/nag.3419