Semester

Spring

Date of Graduation

2022

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Petroleum and Natural Gas Engineering

Committee Chair

Shahab Mohaghegh

Committee Co-Chair

Shahab Mohaghegh

Committee Member

Shahab Mohaghegh

Committee Member

Samuel Ameri

Committee Member

Kashy Aminian

Committee Member

Mehrdad Zamirian

Committee Member

Hassan Amini

Abstract

In recent years, artificial intelligence (AI) and machine learning (ML) technology have grown in popularity. Smart Proxy Models (SPM) are AI/ML based data-driven models which have proven to be quite crucial in petroleum engineering domain with abundant data, or operations in which large surface/ subsurface volume of data is generated. Climate change mitigation is one application of such technology to simulate and monitor CO2 injection into underground formations.

The goal of the SPM developed in this study is to replicate the results (in terms of pressure and saturation outputs) of the numerical reservoir simulation model (CMG) for CO2 injection into saline aquifers. In so doing, the artificial intelligence model was used to particularly predict the pressure distribution as well as carbon dioxide plume at any time-step throughout the period of injection and post-injection. There are four injectors injecting approximately two million metric tons of CO2 per year for a period of ten years. The project seeks to unravel what happens to CO2 and pressure during and after the injection process, commonly referred to as injection and post-injection periods. This process was monitored for 10 years of injection and 190 years of post-injection.

There are 46 geologic realizations of the porosity and permeability distributions which along with some 300 static and dynamic data and features extracted from the model are used as the main input to the artificial neuron network for training, calibration and validation. The dataset produced is then distributed into three major parts; the training dataset, which is majorly aimed at training smart proxy model, the calibration dataset which is majorly a watchdog, and a blind validation which is used to perform the final evaluation on the model after it achieves the desired training accuracy. The results show that the developed SPM can successfully mimic the pressure and CO2 behavior of the CMG outputs which are determining factors of the amount and safety of CO2 sequestration. When implemented on a large scale, this technology has the potential to be very competitive with existing numerical reservoir simulators, providing an additional toolbox for petroleum engineers and CO2 sequestration specialists to monitor the pressure and CO2 plume, as well as perform uncertainty quantification and optimization.

Comments

Corrected version as requested.

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