DEPARTMENT OF PETROLEUM ENGINEERING

Extracting oil from tight reservoir formations is notoriously difficult. These rocks have tiny, poorly connected pores and properties that vary wildly across the formation—all of which make conventional waterflooding ineffective. Water channels through easier paths, leaving most of the oil trapped. Smart Water Injection offers a different approach by adjusting the chemistry of injected water—tweaking salt content and ionic composition—to change how oil and rock interact at the molecular level. This wettability shift helps release trapped oil. I used CMG software to simulate Smart Water performance in two low-permeability reservoirs: one moderately heterogeneous (0.45 mD) and one ultra-tight and highly variable (0.28 mD). I adjusted relative permeability curves and capillary pressure functions to represent the wettability changes Smart Water causes. The results were striking. Smart Water boosted recovery by 37% in the moderate-heterogeneity case and 66% in the ultra-tight reservoir compared to conventional waterflooding. These numbers prove Smart Water can unlock significant oil volumes even in reservoirs considered extremely challenging. This study shows Smart Water is both technically sound and economically viable for tight formations. The simulation workflow developed here provides a practical screening tool for identifying good candidates without expensive upfront lab work

Asphaltene deposition is a significant flow assurance challenge in oil production, particularly in the Niger Delta fields, where variations in pressure, temperature, and crude composition exacerbate the issue. This project focuses on deploying a comprehensive risk assessment framework for predicting and mitigating asphaltene deposition using Monte Carlo simulation and enhanced oil recovery (BOR) techniques, including CO2 huff-and-puff injection. The study integrates probabilistic risk analysis, and experimental data to assess asphaltene precipitation risks under varying reservoir conditions. Monte Carlo simulation is employed to quantify the uncertainties associated with key parameters such as pressure depletion, CO2injection effects, and compositional changes. The effectiveness of CO2 huff-and-puff injection as a potential remediation technique is evaluated, considering its impact on asphaltene solubility and mobility. Additionally, the framework incorporates various mitigation strategies, including chemical inhibitors, operational adjustments, and reservoir management techniques, By developing a robust risk assessment framework, this project provides a decision-making tool for petroleum engineers and field operators to optimize production strategies, reduce downtime, and enhance oil recovery in the Niger Delta region. The results will contribute to improved flow assurance practices, ensuring more sustainable and efficient
hydrocarbon extraction. This study investigates the risk of asphaltene deposition in Niger Delta oil fields, which can impede production and increase operational costs. A comprehensive risk assessment
framework was developed by integrating laboratory analysis, field data, and predictive modeling. Key factors influencing deposition include pressure, temperature, and oil composition. The proposed framework identifies high-risk conditions, enabling proactive management to minimize flow assurance issues and enhance production efficiency.

Tracking the Forensic Analysis of BOP & Considering Key Factors Affecting Performance during Blowouts; This research delves into the crucial field of blowout preventer (BOP)forensic analysis, examining the complex interplay of factors influencing their performance during well control emergencies. The study meticulously tracks the evolution of BOP technology, regulatory frameworks, and prevalent failure mechanisms. It unveils the intricacies of forensic analysis, encompassing data collection, analysis, and reconstruction of events leading to BOP failures.

By scrutinizing various case studies, the research identifies key factors impacting BOP performance, including operational procedures, equipment design and maintenance, environmental conditions, and human error. It analyzes the implications of inadequate drilling practices, improper well control protocols, design flaws, manufacturing defects, and the influence of extreme pressure, temperature, and sea state on BOP function. Additionally, the study emphasizes the significant role of human factors, including operator training, communication, and decision-making, in contributing to or mitigating BOP failures.

Drawing upon this comprehensive analysis, the research culminates in a series of practical recommendations for improving BOP performance and safety in the oil and gas industry. These recommendations encompass enhancing operational procedures, strengthening equipment design and maintenance practices, mitigating the impact of environmental conditions, and minimizing human error. The study advocates for the adoption of industry best practices, cutting-edge technologies, and robust training programs to bolster BOP system effectiveness and safeguard against catastrophic blowouts. This research provides invaluable insights into the complexities of BOP performance during blowouts, contributing to the development of a safer and more sustainable in oil and gas industry.

Traditional reservoir forecasting methods provide single deterministic predictions that fail to capture subsurface uncertainty. This research develops a Python-based simulation framework integrating material balance physics with Monte Carlo uncertainty quantification for probabilistic reservoir performance forecasting.

The framework generates P10/P50/P90 forecasts through efficient tank modeling, completing 5,000-realization uncertainty analysis in under one hour—a 50-100x speedup versus commercial simulators. Application to a representative sandstone reservoir yields 18.5 MMstb cumulative recovery (P50) with ±24% uncertainty (P90: 14.2 MMstb, P10: 23.1 MMstb). Sensitivity analysis identifies permeability as the dominant uncertainty driver (correlation +0.68), directly informing data acquisition priorities.
Validation confirms material balance closure within 0.5% and recovery factors consistent with published analogs. By eliminating software licensing costs and computational barriers, the framework democratizes probabilistic forecasting, transforming reservoir engineering from deterministic prediction to accessible, risk-aware decision support.

Reservoir heterogeneity, characterized by spatial variation in rock properties such as porosity and permeability, is a defining feature of petroleum reservoirs that influences fluid flow behavior and hydrocarbon recovery. Traditional methods to quantify heterogeneity rely mainly on static petrophysical measurements and indices such as the Dykstra-Parsons coefficient, which provide numerical expressions of permeability variation but often lack dynamic insights into fluid connectivity and migration pathways. Recent advances have demonstrated the value of integrating geochemical techniques, notably Strontium Residual Salt Analysis (Sr-RSA), with permeability data to reveal subtle compartmentalization, fluid discontinuities, and internal flow barriers that directly impact sweep efficiency during secondary and tertiary recovery processes. This integrated approach facilitates a multidimensional characterization of reservoir architecture, enabling better prediction of fluid displacement patterns and optimization of injection and production strategies. Such insights are critical for improving hydrocarbon recovery efficiency and maximizing asset value. This study assesses the extent and impact of reservoir heterogeneity on sweep efficiency, employing both traditional petrophysical metrics and advanced geochemical fingerprinting to provide a comprehensive understanding of reservoir behavior. The findings aim to support enhanced reservoir characterization and guide strategic decisions in field development and enhanced oil recovery operations.

Accurate estimation of Stock Tank Oil Initially in Place (STOIIP) is essential for effective reservoir management and production planning. This study validates geological STOIIP estimates through the Material Balance (MBAL) method, comparing it with the volumetric approach to evaluate their reliability. The research focuses on Reservoir Q, which spans 1,000 acres, has a thickness of 75 feet, and an estimated oil volume of 105 million stock tank barrels (MMSTB). A comparative analysis of both methods showed no significant differences, with STOIIP estimates of 102 MMSTB and 105 MMSTB, respectively, being nearly identical. This consistency enhances confidence in the accuracy of geological reserve assessments and supports improved reservoir performance optimization and hydrocarbon recovery strategies. The study underscores the importance of MBAL as a validation tool to reduce uncertainties and enhance resource estimation. These findings contribute to the advancement of petroleum engineering methodologies by demonstrating the effectiveness of MBAL in verifying geological STOIIP estimates. The research also highlights the necessity for ongoing reservoir monitoring and data integration to enhance decision-making in field development and maximize hydrocarbon recovery.

The accurate prediction of the Well roductivity Index (PI) is critical for reservoir management, production optimization, and forecasting. Traditional methods, such as analytical correlations and decline curve analysis, are often limited by simplifying assumptions that fail to capture the complexities of heterogeneous reservoirs like those in the Niger Delta. This research addresses
this limitation by developing and evaluating a data-driven framework for PI prediction using machine learning (ML) on historical production data. The study implements and compares three advanced ensemble regression algorithms—Random Forest, GBoost, and CatBoost—to predict PI from daily records of oil, gas, and water production rates and downhole pressures. A dataset approximately 7,000 daily records from five Niger Delta wells was utilized, with the PI target variable calculated using a proxy for reservoir pressure drawdown. A clear performance hierarchy was established among the models. Random Forest yielded the weakest performance (R² = 0.18, MAE = 65.62), while XGBoost showed substantial improvement (R² = 0.78, MAE = 34.14). CatBoost erged as the superior model, achieving exceptional predictive accuracy with an R² of 0.95, a Mean Absolute Error (MAE) of 18.96, and a Root Mean Squared Error (RMSE) of 21.02. Residual and temporal analyses confirmed that CatBoost produced unbiased, homoscedastic errors and effectively tracked the dynamic PI trends of individual wells over time. Interpretability analyses revealed that production rates (oil, gas, and water) were the most influential predictors, a finding consistent with reservoir engineering principles. However, this also highlights a methodological caveat regarding the mathematical coupling between the model's inputs and the PI target. The study concludes that CatBoost provides a robust and highly accurate model for PI prediction from routine field data, offering a significant advantage over traditional methods for well performance monitoring and screening in the Niger Delta context

Refinery operations play a crucial role in converting crude oil and natural gas into usable
petroleum products; however, these processes generate significant quantities of waste that pose serious environmental concerns. This study examines the estimation of refinery wastes and evaluates their impact on the environment during oil and gas refining activities. The research focuses on identifying major categories of refinery waste such as gaseous emissions, wastewater effluents, solid sludge, spent catalysts, and particulate matter and assessing their sources, composition, and disposal techniques. Data was collected through operational records, regulatory reports, and existing environmental studies. Findings indicate that improper waste management contributes to soil degradation, water pollution, air contamination, and adverse health effects on nearby communities. The study emphasizes the importance of adopting environmentally sustainable waste handling practices, advanced treatment technologies, and strict compliance with environmental regulations to minimize ecological damage. It concludes that effective waste
estimation and management strategies are essential to ensuring cleaner refinery operations, protecting ecosystems, and promoting public health.

This research examines how sodium hydroxide (NaOH) influences the flow characteristics of purified gum Arabic-based drilling mud formulations, positioning them as eco-friendly substitutes for conventional synthetic additives. The experiment involved developing seven initial formulations combining bentonite with different polymer systems: xanthan gum, gum Arabic, and mixtures of gum Arabic with either cocoyam starch or ginger extract in proportions of 50/50 and 75/25. Subsequently, selected formulations underwent alkaline modification using NaOH at measurements of 3.0g, 7.5g, and 15.0g to replicate varying pH environments.
Flow behavior parameters encompassing plastic viscosity (PV), yield point (YP), gel strength, and mud weight were determined through Fann viscometer measurements and evaluated against three mathematical model frameworks: Bingham Plastic, Power Law, and Herschel-Bulkley models.
Experimental findings demonstrated that 50g of gum Arabic delivered comparable rheological characteristics to 1g of xanthan gum under neutral conditions. The introduction of alkaline treatment produced substantial modifications in fluid behavior, with response patterns dependent
on both the specific polymer-starch pairing and alkalinity level. The most remarkable transformation occurred in the gum Arabic-cocoyam (50/50) formulation treated with 7.5g NaOH, which demonstrated PV of 65 cp and YP of 180 lb/100ft² corresponding to increases of 261% and 1025% respectively relative to the 3.0g NaOH variant. The gum Arabicginger combination displayed considerable viscosity enhancement (PV = 108 cp with 7.5g NaOH) yet revealed temporal degradation of gel structure at elevated alkalinity levels. Every alkalinetreated system manifested pseudoplastic (shear-thinning) characteristics with flow behavior indices (n) spanning 0.3 to 0.948, validating their appropriateness for drilling fluid applications. Comparative model analysis indicated that the Herschel Bulkley model most accurately characterized the behavior of alkaline modified natural polymer systems, whereas both Bingham Plastic and Power Law models exhibited substantial prediction errors, especially under highalkalinity conditions. These results established that purified gum Arabic, when strategically combined with indigenous starches, (cocoyam & ginger) and subjected to pH optimization, represents a viable, environmentally degradable, and economically advantageous alternative to synthetic drilling fluid components, delivering ecological advantages while preserving operational performance standards required for petroleum drilling activities.

This study investigates how optimising injection parameters can improve oil recovery performance in black oil reservoirs using enhanced oil recovery (EOR) techniques. Using CMG's IMEX and STARS simulators, this research examined the influence of injection rate and pressure variations on reservoir recovery efficiency. Three EOR techniques were examined: waterflooding, polymer flooding, and steam injection. Multiple simulation scenarios were conducted on a three-dimensional reservoir model to determine the optimal parameter combinations for maximising oil production. Analysis revealed that injection rate and pressure significantly influenced overall recovery efficiency. While waterflooding outperformed primary depletion methods, polymer flooding yielded the best results in terms of recovery factor and total oil produced, primarily by enhancing sweep efficiency and minimising water production. Steam injection improved recovery by reducing oil viscosity via heat transfer, though it ranked second to polymer flooding under the modeled conditions. Based on the simulation results, polymer flooding emerged as the most effective method for the studied reservoir conditions, indicating strong applicability for Niger Delta black oil reservoirs.