Applications of reservoir simulation and machine learning in subsurface energy systems for decarbonization

Aug 5, 2026

Uploaded by Theodora

Transition to a low-carbon future drives the need for advanced carbon management and hydrogen storage solutions, especially for enhanced oil recovery (EOR) from hydrocarbon reservoirs. The thesis uses computational reservoir simulation and machine learning to optimize carbon management strategies, including evaluating flue gas and CO2 in WAG injection with Eclipse (E300). Results indicate strong recovery and storage performance, supported by sensitivity analyses and data-driven site screening methods for offshore CO2 storage. A separate modeling effort predicts water–hydrogen–rock contact angles using multiple ML models to improve hydrogen underground storage understanding.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages447

Reading Time18 h 46 min

AccessFree to download

Abstract

Carbon management and EOR simulation (flue gas, CO2, WAG)

CO2 storage optimization in fractured carbonate reservoirs

Machine-learning framework for offshore CO2 storage site screening

Hydrogen storage modeling via contact-angle prediction

Edith Cowan University Research Online Theses: Doctorates and Masters Theses 2025 Applications of reservoir simulation and machine learning in subsurface energy systems for decarbonization Seyedmohammadmehdi Nassabeh Edith Cowan University Follow this and additional works at: [https://ro.ecu.edu.au/theses](https://ro.ecu.edu.au/theses) Part of the Engineering Science and Materials Commons, and the Geological Engineering Commons Author also known as Mehdi Nassabeh Recommended Citation Nassabeh, S. (2025) . Applications of reservoir simulation and machine learning in subsurface energy systems for decarbonization. Edith Cowan University. [https://doi.org/10.25958/xpn3-6336](https://doi.org/10.25958/xpn3-6336) This Thesis is posted at Research Online. [https://ro.ecu.edu.au/theses/2939](https://ro.ecu.edu.au/theses/2939) Applications of Reservoir Simulation and Machine Learning in Subsurface Energy Systems for Decarbonization Seyedmohammadmehdi Nassabeh Thesis for the Degree of Doctor of Philosophy in Petroleum Engineering School of Engineering Edith Cowan University ABSTRACT The transition to a low-carbon future necessitates innovative approaches to carbon management and hydrogen storage, particularly in the context of enhanced oil recovery (EOR) from hydrocarbon reservoirs. This study employs advanced analytics and machine learning techniques to optimize carbon management strategies. One key focus of this research is to evaluate the effectiveness of fluegas and CO2 in Water Alternating Gas (WAG) injection within a homogeneous fractured carbonate reservoir characterized by low porosity and permeability. A computational model was developed to depict the flow regime in the reservoir and simulate reservoir fluid behavior using Eclipse (E300) software, various hybrid EOR methods were evaluated, revealing that natural production accounted for only 29% of the total output. The optimized Hybrid EOR method achieved an impressive oil recovery factor of approximately 85%, demonstrating the critical need for EOR techniques to enhance overall production. In parallel, to mitigate greenhouse gas emissions caused by reliance on hydrocarbon resources, the integration simulation study of CO2 storage with EOR in fractured carbonate reservoirs is implemented to meet this pressing requirement. Utilizing the Eclipse simulator, various gas injection scenarios were modeled to assess the effectiveness of CO2 and fluegas geo-sequestration and EOR. Key findings revealed that flue gas demonstrated superior storage capacity (150 MMSCF) compared to CO2 (85 MMSCF) and maintained better reservoir pressure, while CO2 injection resulted in a higher oil recovery factor of 52% versus 36% for flue gas. Sensitivity analyses indicated that increased reservoir porosity, permeability, and injection rates enhanced gas storage capacity, although CO2 showed a normal distribution trend in permeability. In addition, further reservoir simulation study explores the synergistic relationship between flue gas compositions, reservoir characteristics, and injection rates, highlighting that flue gases with higher concentrations of CO2 and O2 significantly improve recovery factors. Key findings indicate that reservoir temperature, porosity, and permeability are vital factors influencing oil recovery, with CO2 injection consistently yielding the highest recovery rates. Notably, the study established that flue gas injection demonstrated greater sensitivity to increased injection rates, with specific flue gas compositions enhancing recovery efficiency. On another facet of advanced analytical techniques, a data-driven framework for site screening of offshore CO2 storage is introduced, which integrates diverse geospatial data with expert-weighted criteria to identify optimal locations for Carbon Capture, Utilization, and Storage (CCUS) projects. Machine learning algorithms, particularly Deep Neural Networks (DNN), were employed to enhance predic

FAQs

How does the thesis apply reservoir simulation to carbon management and EOR?

It develops a computational reservoir model and uses Eclipse (E300) to simulate reservoir flow and fluid behavior, evaluating hybrid EOR methods and WAG injection scenarios for flue gas and CO2.

What comparative results are reported for flue gas versus CO2 injection?

The study finds flue gas provides higher storage capacity and maintains reservoir pressure better, while CO2 injection yields higher oil recovery factor under the modeled scenarios. Sensitivity analyses link storage and recovery to porosity, permeability, and injection rate.

How is machine learning used beyond reservoir simulation in the thesis?

A data-driven framework integrates geospatial data with expert-weighted criteria to screen offshore CO2 storage sites, using Deep Neural Networks to improve predictive accuracy. Separately, ML models predict water–hydrogen–rock contact angles to support hydrogen storage in geological formations.

Aug 5, 2026

Uploaded by Theodora