A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs

Adelina Lv, Lei Cheng, Mohammad Ali Aghighi, Hossein Masoumi, Hamid Roshan

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Permeability is arguably the most important parameter in gas pre-drainage and production practices in coal mining and coal seam gas industries. In fractured coal seams, permeability can significantly vary both laterally and vertically. Knowing the vertical variation of permeability in relatively thick coal seams is important in optimizing inseam pre-drainage design in underground coal mining and improving reservoir management and completion design efficiency in the coal seam gas industry. Existing empirical/statistical coal permeability models often yield poor results in estimating permeability profiles where there is significant variation in the characteristics of coal fracture systems. This study presents a novel workflow for evaluating the permeability profile of naturally fractured thick coal seams using downhole geophysical logging data. The workflow combines physics-based simulation, laboratory experiments, and a data-driven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses. Numerical simulation of the Navier-Stokes fluid flow and elastic wave propagation was then performed on a range of constructed coal block geometries with various fracture densities. In the next step, a physics-informed, neural network-based model (PINN) was trained using the data obtained from both laboratory experiments and numerical simulation. The model inputs included density, as well as compressional and shear wave velocities of the coal seam and its output is the trend of permeability variation across the coal seam interval. In-situ permeability measurement at a certain depth (e.g., from well pressure testing) was then used to convert the permeability trend to the entire permeability profile of the coal seam along its interval. The performance of the proposed workflow was finally evaluated using a suite of downhole geophysical logging data from a borehole intersecting two coal seams in an eastern Australian basin. This study demonstrates that the proposed workflow is relatively simple to implement and yet accurate for evaluating the permeability profile of a coal seam across its interval.

Original languageEnglish
Article number105171
Number of pages14
JournalMarine and Petroleum Geology
Volume131
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Coal seam permeability
  • Physics-informed machine learning
  • Trend prediction
  • Well logging

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