Machine learning approaches for enhanced analyses of rock properties in geologic CO2 storage
Barteneva, Ekaterina
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Permalink
https://hdl.handle.net/2142/121385
Description
Title
Machine learning approaches for enhanced analyses of rock properties in geologic CO2 storage
Author(s)
Barteneva, Ekaterina
Issue Date
2023-07-21
Director of Research (if dissertation) or Advisor (if thesis)
Makhnenko, Roman
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Carbon storage
machine learning
caprock
permeability
breakthrough pressure
wellbore logs
acoustic emission.
Abstract
Geological carbon storage (GCS) has emerged as a promising approach for mitigating the accumulation of carbon dioxide (CO2) in the atmosphere. This research investigates the application of machine learning (ML) techniques to enhance the analysis and optimization of CO2 storage operations. The initial part of the study focuses on predicting permeability and breakthrough pressure in the caprock, the upper layer of GCS. By conducting a thorough literature review and developing robust ML algorithms, accurate predictions are achieved using input variables derived from the comprehensive datasets. Prediction of the CO2 breakthrough pressure is performed using three ML methods: the non-linear neural network, Bayesian framework, and the classification model are developed and evaluated for their accuracy and reliability. The analysis includes direct and indirect test results with two (porosity and permeability) and five (porosity, permeability, specific surface area, pore radius, and clay content) input parameter configurations. The results reveal strong correlations between the predicted and measured breakthrough pressure values. The non-linear neural network model demonstrates a superior performance when utilizing five input parameters, while the Bayesian framework yields identical predictions for both two-parameter and five-parameter configurations. The classification model successfully captures the correct range of values in its predictions for both configurations.
The machine learning techniques are also utilized to analyze wellbore data and acoustic emission waveforms. Fractured wellbore intervals are predicted based on petrophysical and mineralogical parameters, underscoring the importance of considering multiple parameters for accurate fracture characterization. The analysis of acoustic emission waveforms demonstrates the effectiveness of deep learning models in denoising seismic data and accurately predicting the first arrival time. The findings of this study contribute to a better understanding of the correlation between the rock properties that are crucial for the analysis of the geologic CO2 storage. This work highlights the potential of ML approaches in robust assessment of caprock sealing capacity, enhancement of the quality of the seismic data interpretation, and efficient detection of the fractured intervals based on the wellbore log data.
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