Petroleum Engineering Commons^™

Utilizing Coal For Mesophase Pitch-Based Carbon Fiber Production: Precursors, Processes, And Progress, Christina M. Thompson

Theses and Dissertations--Chemistry

Graphitic materials possess unique properties due to the unique combination of layered crystalline structure and carbon’s low atomic weight. High performance carbon fiber is one such example, displaying exceptional strength-to-weight ratios, stiffness, and thermal and chemical resistance. These properties render high performance carbon fiber a critical structural reinforcement material in the manufacture of composites across various industries, such as for automotive and aerospace applications. However, balancing fiber performance with precursor and processing costs remains a challenge. As alternative carbonaceous feedstocks are explored, coal has gained interest for utilization in graphitic products as a relatively abundant and low-cost source of aromatic …

Thermo-Chemical Recycling Of Waste Plastics Containing Halogenated Polymers (Br−, Cl−, F−) Using Potential Additives, Mohamed Shafi

Thesis/ Dissertation Defenses

Plastic waste handling is a daunting task due to the presence of halogenated polymers, specifically those cluttered with bromine (Br), chlorine (Cl), and fluorine (F). Halogenated plastics can be mixed with other polymers in the general plastic waste, particularly in the recycling stream. Brominated flame retardants (BFRs) are mainly present in e-waste and from the use of flame retardants in household items, textiles, and furniture. Plastics from discarded electronics often contain significant levels of bromine and chlorine, predominantly from flame retardants and plasticizers. Being the most deployed thermoplastic worldwide, waste polyvinyl chloride (PVC) generation is expected to increase by about …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Core-Scale Study Of Miscible Co2 Foam–Oil Interactions, Kuvonchbek Abdirakhmonov

Theses

Foam is currently the most effective means for gas mobility control in a variety of geo-energy applications, including enhanced oil recovery (EOR), carbon capture, utilization and storage (CCUS), and aquifer/soil remediation. This study investigates the mobility control of miscible CO₂ foam in the presence of oil.

The primary objective is to quantify the impact of oil on CO₂ foam behavior under miscible conditions, specifically examining foam stability, strength, and flow regimes as influenced by oil composition and reservoir permeability. While most oils destabilize foam, few studies explore the coarsening mechanisms of CO₂ foam in the presence of miscible oils, which …

Laboratory Investigation Of High-Temperature Preformed Particle Gels For Fluid Control In Granite Cores For Geothermal Applications, K. Caleb Darko, Yanbo Liu, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

To understand the applicability of high-temperature preformed particle gel (HT-PPG) for control of short-circuiting in enhanced geothermal systems (EGSs), core flooding experiments were conducted on fractured granite cores under varying fracture widths, gel particle sizes and swelling ratios. Key parameters such as injection pressure, water breakthrough pressure, and residual resistance factor were measured to evaluate HT-PPG performance. The gel exhibited strong injectability, entering granite fractures at pressure gradients as low as 0.656 MPa/m; HT-PPG yields a superior sealing performance by significantly reducing the permeability; and dehydration occurs during HT-PPG propagation, with a dehydration ratio ranging from 4.71% to 11.36%. This …

Effect Of Clays On The Spontaneous Imbibition Behavior Of Surfactant Solutions In Shale Oil Reservoirs, Ning Xu, Yanling Wang, Baojun Bai, Chuanbao Zhang, Shizhang Cui, Yu Zhang, Di Li, Wenjing Shi, Wenhui Ding

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surfactant-enhanced spontaneous imbibition is a proven method of enhancing oil recovery from shale reservoirs. However, a significant knowledge gap concerning the impact of clay minerals on surfactant-enhanced imbibition in shale reservoirs remains. Therefore, this study first analyzed the mineral composition and pore structure of the shale reservoirs. Subsequently, three surfactants were selected for spontaneous imbibition experiments. The influence of various clay minerals on the surfactant imbibition was assessed by considering various properties of the imbibition system. The results showed that the shale pores primarily consisted of macropores (44.03%) and mesopores (43.94%). Surfactants enhance the imbibition recovery by altering the wettability …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Predicting Groundwater Withdrawals Using Machine Learning With Limited Metering Data: Assessment Of Training Data Requirements, Dawit Asfaw, Ryan G. Smith, Sayantan Majumdar, Katherine Grote, Bin Fang, B. B. Wilson, V. Lakshmi, J. J. Butler

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The future of major aquifer systems supporting irrigated agriculture is threatened due to unsustainable groundwater pumping. Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008–2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point …

Upcycling Waste To Wealth: Cuo-Sio₂/Reduced Graphene Nanocomposite From Pomegranate Peels For One-Pot Low-Temperature Conversion Of Waste Oils Into Valuable Fatty Acid Monomers, Attia Attia, Shahenda Mahran Dr., Maria Centeno Prof., Basudeb Saha

Petroleum Engineering and Gas

and hence reduce the price of the fatty acid monomers. This study reports for the first time a novel, environmentally benign, highly active copper oxide-silica oxide/reduced graphene oxide (CuO-SiO2/RGO), heterogeneous nano-catalyst derived from waste pomegranate peels, for the one-pot, low-temperature synthesis of fatty acid monomers from high-acid-value waste vegetable oil (WVO). The synthesised nano-catalyst was extensively characterised using XRD, FT-IR, TEM, SEM, EDX and TGA-DTA. Further, it was utilised to synthesise fatty acidrich oleic phenoxypropyl acrylate (OPA) monomer from high acid value WVO via a single-step reaction.

Investigation Of Co-Current And Counter-Current Multiphase Flow In Upstream Drilling And Production Applications, Hazem Fleifel

LSU Doctoral Dissertations

In the domain of upstream Petroleum Engineering, the dynamics of co-current and counter-current multiphase flows present huge challenges, significantly impacting the efficiency of associated processes. This study investigates two specific applications concerning these flow behaviors, involving both laboratory experiments and transient computer simulations. This work investigates two distinct well control and production enhancement processes, involving multiphase flow dynamics: the bullheading kill procedure and liquid-assisted gas lift (LAGL).

The first topic (Topic 1) explores the bullheading process, a type of well control methods, by challenging the traditional understanding that gas removal efficiency increases monotonically with injection pump flow rate. Through 1D …

Dissolution Mechanism Of Nonionic Polyether Surfactants In Supercritical Co2, Ning Xu, Yanling Wang, Baojun Bai, Shizhang Cui, Zan Gao, Yu Zhang, Di Li, Wenjing Shi, Wenhui Ding, Peixu Ma

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polyoxyethylene ether is an efficient CO₂-philic compound with significant potential applications in CO₂ flooding, which requires CO₂-philic surfactants with high solubility in supercritical CO₂ (scCO₂). This study elucidates the molecular and atomic-level dissolution mechanism of the fatty alcohol polyoxyethylene ether (AEO) in scCO₂ by combining phase-behavior experiments and molecular dynamics simulations. The solubility of AEO in scCO₂ is measured using a semiconductor laser. AEO containing three polyoxyethylene (EO) groups exhibited the highest solubility in scCO₂.The solubility of AEO in scCO₂ decreased with an increase in the number …

Mantle Flow And Crustal Deformation Revealed By Seismic Anisotropy In The Subduction Zone Beneath Myanmar, Enbo Fan, Mingming Jiang, Yinshuang Ai, Stephen S. Gao, Kelly H. Liu, Yumei He, Yiming Bai, Guangbing Hou, Yuan Ling, Chit Thet Mon, Myo Thant, Kyaing Sein

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The oblique convergence of the Indian and Eurasian plates drives significant deformation in the crust and mantle beneath Myanmar. However, the crustal deformation in the Indo-Burman Ranges (IBR) and mantle flow beneath Myanmar and adjacent areas remain unclear due to limited studies. We utilized data from the newly deployed second phase of the China-Myanmar Geophysical Survey of the Myanmar Orogen array to perform shear wave splitting analysis of PKS, SKKS, and SKS (XKS) phases. Our results, together with previous findings, provide new insights into mantle flow and crustal deformation across central Myanmar. In the IBR, we observed a transition from …

Detection Of Oil Pollution On The Tigris River In Iraq Using Fluorescence And Reflecting Techniques, Ahmad K. Ahmad, Thamer M. Mohammed

Karbala International Journal of Modern Science

In this study, a broadband light source (visible and ultraviolet) was used to name minerals via fluorescence spectra and estimate slick oil thickness, ranging from sub-millimetres, using the reflecting spectrum technique. Using a fluorescent technique, we obtained fluorescence spectra of the samples, converting and processing the signals to produce high-resolution data, which enabled the identification of the types of dissolved elements and oil slicks in the Tigris River, highlighting pollution sources and potential control measures. This system aims to show the faint signal in fluorescence samples collected from the cooling system outlet water throughout the refining process from AL-Dora (Baghdad) …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofrendly Systems, Noora Al Mansoori

Thesis/ Dissertation Defenses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …

Engineering Bio-Based Poly (Ethylene Furanoate) Blends And Nanocomposites, Safa Fadl Eldin Mohamed

Thesis/ Dissertation Defenses

The growing demand for sustainable materials has directed attention toward bio-based polymers such as poly(ethylene furanoate) (PEF), a 100% bio-derived alternative to PET. While PEF offers excellent gas barrier and thermal properties, its limited mechanical strength restricts its use. This study explores polymer blending and nanocomposite strategies to enhance PEF’s mechanical and electrical performance.
Blending PEF with linear low-density polyethylene (PE) using reactive compatibilizers (SEBS-g-MA, PE-g-MA) significantly improved ductility and tensile toughness. Interfacial reactions between PEF and compatibilizers transformed the blend morphology, resulting in improved flexibility suitable for packaging applications.
Electrically conductive composites were developed by localizing graphene nanoplatelets (GNP) …

Quantifying Wellbore Effects During Co2 Sequestration In A Saline Aquifer, Uzoma Johnpaul Ajugwe

USF Tampa Graduate Theses and Dissertations

Carbon capture and storage (CCS) is a strategy for mitigating climate change by reducing greenhouse gas emissions from large stationary sources like fossil-fuel-fired power stations. One method of CCS involves carbon dioxide (CO2) sequestration in deep saline aquifers, where supercritical CO2 is injected into an aquifer. The success of such projects depends on accurate predictions of CO2 behavior within the subsurface, which are obtained through numerical simulations.

TOUGHREACT, a renowned simulation tool, models subsurface fluid flow, geochemical interactions, and solute transport, providing insights into the feasibility of CO2 sequestration projects. A critical aspect of these simulations is the apportioning of …

Investigation Of Retention And Dehydration Of Preformed Particle Gel For Conformance Control In Fractured Carbonates, Abdulaziz A. Almakimi, Abdullah O. Almansour, Junchen Liu, Baojun Bai, Ibnelwaleed A. Hussein

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Preformed particle gel (PPG) treatment has proved to be among the most effective methods to reduce excessive water production in fractured reservoirs. The blocking ability of PPGs to fractures or fracture-like features is highly dependent on injected gel properties and volume. Gel dehydration during extrusion through fracture is common and has a considerable effect on gel properties, transport, and plugging efficiency. As the gel particles propagate through the fracture, continuous dehydration is often associated and causes the gel to concentrate and require high-pressure gradients to move. Dehydration might be desirable to a certain limit because it can help to form …

Investigation And Prediction Of Excessive Water Production In Bottom Water-Drive Naturally Fractured Reservoirs Using Machine And Deep Learning, Sami Abderraouf Belkhir

Theses

Naturally Fractured Reservoirs (NFRs) are characterized by dual-porosity and dual-permeability systems, posing significant challenges in managing water production due to highly conductive fracture networks that facilitate rapid water migration from bottom aquifers, often bypassing oil stored in the matrix, thus resulting in early water breakthrough and water channeling phenomena. The main objective of this thesis is to develop and validate deep learning and machine learning models to predict water production, water breakthrough time (tbt), and ultimate water cut (WCult) in NFRs, thereby enabling more effective reservoir management strategies. This work also aims to evaluate the sensitivity of water behavior to …

Experimental Evaluation Of Novel Nano Polymer Hybrid Enhanced Oil Recovery In Carbonate Reservoir, Altamish Ahmed Pakeer

Theses

Innovative enhanced oil recovery (EOR) strategies are needed to unlock additional reserves in heterogeneous, oil-wet carbonate reservoirs. This study evaluates hybrid nano-polymer flooding by integrating silica nanoparticles (SiO₂) and single-walled carbon nanotubes (CNT) with novel HPAM polymers (Sav10 and Sav10 VHM) to optimize wettability alteration, rheological parameters, and recovery mechanisms.

A comprehensive experimental approach was employed, where rheological tests assessed viscosity enhancement under varying shear conditions, and wettability alteration was quantified via contact angle measurements using HPAM-SiO₂, HPAM-CNT, Sav10 VHM-SiO₂, and Sav10 VHM-CNT systems. Core floods were performed in three consecutive phases; waterflooding (baseline), standalone nanoparticle flooding, and hybrid nano-polymer …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofriendly Systems, Noora Saleh Al Mansoori

Theses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …