PARAMETER ESTIMATION FOR THE REDUCED FRACTURE MODEL VIA A DIRECT FILTER METHOD

In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse problem is formulated by first constructing a forward formulation based on the reduced fracture model of the diffusion equation. A parameter estimation problem is then performed online by utilizing a direct filter method. Numerical experiments are carried out to demonstrate the accuracy of our method in approximating the target parameters.

Authors

Institutions

Publication Details

Journal
Journal of Machine Learning for Modeling and Computing
Published
2024-11-12
DOI
https://doi.org/10.1615/jmachlearnmodelcomput.2024056400
Citations
3
Primary Topic
Hydraulic Fracturing and Reservoir Analysis
Type
article
Field-Weighted Citation Impact
0.64
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PARAMETER ESTIMATION FOR THE REDUCED FRACTURE MODEL VIA A DIRECT FILTER METHOD

Thi‐Thao‐Phuong Hoang, Toan Luu Duc Huynh, Feng Bao
3 citations
Journal of Machine Learning for Modeling and Computing
Hydraulic Fracturing and Reservoir Analysis
0.64
article

PARAMETER ESTIMATION FOR THE REDUCED FRACTURE MODEL VIA A DIRECT FILTER METHOD

Thi‐Thao‐Phuong Hoang, Toan Luu Duc Huynh, Feng Bao
article en
3 citations

Abstract

In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse problem is formulated by first constructing a forward formulation based on the reduced fracture model of the diffusion equation. A parameter estimation problem is then performed online by utilizing a direct filter method. Numerical experiments are carried out to demonstrate the accuracy of our method in approximating the target parameters.

Journal of Machine Learning for Modeling and ComputingVol. 6(1)
Florida State University (US), Auburn University (US)
Openalex Percentile: Top 35%
Hydraulic Fracturing and Reservoir Analysis
0.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.