DEPARTMENT OF PETROLEUM ENGINEERING

The formation of mineral scale in petroleum reservoir is one of the most severe oil field problems that inflict water injection process primarily when two incompatible waters are involved. Typical examples are sea water, with high concentration of sulphate ions and formation water, with high concentrations of calcium, barium, and strontium ions. Mixing of these waters, therefore, could cause precipitation of calcium sulphate, barium sulphate and/or strontium sulphate. Different mathematical models currently exist in the literatures that predict the tendency of scale formation during water injection technique but not the amount of scale mineral that would be precipitated. This research work was conducted to develop a mathematical model that will be able to predict the amount of scale minerals that would be formed at different locations few inches from the injection well toward the petroleum reservoir. The relationship that exists among the saturation index, solubility product and concentration of scale precipitated was used as the basic principle in deriving the mathematical model. The results of the research work show that in Siri – C oil field, at saturation index of -3.00, the mass of Gypsum precipitated was 3986.455mg but when the saturation index increased to 0.4, the mass reduced to 3985.001mg. In Siri – D oil field, at saturation index of -1.20, the mass of Gypsum precipitated was 4792.014mg, but when the saturation index increased to 0.1, the mass reduced to 4792.001mg. In Siri – E oil field, at saturation index of -3.50, the mass of Gypsum precipitated was 9184.572mg, but when the saturation index increased to -0.4, the mass also reduced to 9184.000mg. This trend was similar for Barite and Celestite. It was inferred from the analysis that the amount of scale precipitated is dependent on the saturation index of the scale and the ionic composition of the system. v vi TABLE OF CONTENTS TITLE PAGE CERTIFICATION i LETTER OF TRANSMITTAL ii DEDICATION iii ACKNOWLEDGEMENT iv ABSTRACT v LIST OF FIGURES viii LIST OF TABLES ix LIST OF SYMBOLS x CHAPTER ONE 1.0 INTRODUCTION 1 1.1 OBJECTIVE OF THE WORK 3 1.2 SCOPE OF THE WORK 3 1.3 TECHNICAL CHALLENGES 3 1.4 JUSTIFICATION OF THE RESEARCH WORK 3 CHAPTER TWO 2.0 LITERATURE REVIEW 5 2.1 THEORETICAL PRINCIPLES 11 2.1.1 Water Injection Technique 11 2.1.2 Causes of Scale Formation 12 2.1.3 Factors affecting Scale Formation 13 2.1.3.1 Supersaturation 13 2.1.3.2 Reaction Kinetics 14 2.1.3.3 Changes in Temperature and Pressure 14 2.1.3.4 Mixing of Incompatible waters 14 2.1.3.5 Effects of other compounds and impurities 14 2.1.4 Types of Inorganic Scales 15 2.1.5 Nature of common Oil Field Scales 15 2.1.5.1 Calcium sulphate scales 15 2.1.5.2 Calcium Carbonate Scales 17 2.1.6 Mechanisms of Scale Formation 19 2.1.6.1 Attainment of Supersaturation/Induction Period 19

This project work has attempted to determine the extent to which shale (clays) and silt or siltstones affect porosity, formation water saturation and some other petrophysical properties obtained from data collected from five wells situated in the Brown fields along the coastal swamps in the western onshore region of the Niger -Delta (Brown field). It is established that porosity and permeability of sandstones depends on grain size, sorting, cementation and compaction and these parameters are significantly affected by significant presence of shale (clay) and silt in the formation. Shale formations are believed to be fine-grained, loose and flexible mixtures of clay-sized or colloidal particles. These fine-grained particles affect porosity, which is the percentage of void spaces in the overall rock volume. The work evaluated relevant well and reservoir parameters of interest with respect to the various depths of reservoir sands and correlated wells, using its findings to confirm trends conformable to Niger - Delta basin oil and gas reservoirs. The porosity of the different units of reservoir sands showed variation laterally with porosity values decreasing with a combination of increasing reservoir depth and average shale volume. Consequently, permeability was observed to decrease with increasing depth, though sand Z has high permeability values than sands X and Y lying several feet above it. The reservoir sands X, Y and Z have slightly reduced porosity and permeability due to high volume of clays (shales) and silts (siltstones) often associated with the environment. The porosities of the reservoir sands are good to very good; their permeability moderate to good with high and widespread oil and gas accumulation throughout the Brown field. The hydrocarbon resources are considered exploitable for profit.

Gas lift remains one of the most widely applied artificial lift methods for sustaining oil production, particularly in mature wells with declining reservoir pressure. However, its efficiency strongly depends on the accurate selection of operating parameters such as injection gas rate, valve depth, and tubing size. Conventional approaches often rely on trial-and-error procedures, which are time-consuming and may lead to suboptimal performance. This study presents an integrated approach that combines artificial neural networks (ANN) and sensitivity analysis within a nodal analysis framework to optimize gas lift performance in a Niger Delta field.
A dataset of well test and production parameters was pre-processed, normalized, and used to train ANN models for predicting oil production rates under varying gas injection conditions. The model achieved a high prediction accuracy (R² > 0.95), demonstrating its robustness in capturing the nonlinear relationship between gas injection and liquid production. Sensitivity analysis was performed to evaluate the relative influence of injection gas rate, tubing head pressure, and valve depth on production performance. Results indicated that injection gas rate was the most significant parameter, accounting for over 60% of the observed production variability, followed by tubing head pressure.
The integration of ANN with sensitivity analysis provided clear guidelines for selecting optimal gas injection strategies that maximize production while minimizing energy costs. The study concludes that data-driven approaches can significantly improve the efficiency of gas lift optimization, offering a reliable alternative to conventional empirical and simulation-based techniques. The findings contribute to extending the economic life of mature wells in the Niger Delta and demonstrate the potential of artificial intelligence in enhancing petroleum production systems. 

Drilling fluids play a critical role in the success of oil and gas drilling operations, yet the Nigerian petroleum industry relies heavily on imported bentonite and additives for drilling mud formulation, leading to high operational costs and foreign exchange losses. This study evaluates the suitability of selected locally sourced clays from Iyi-Ogene, Okuaghe, and Ikpoba deposits in Delta and Edo States, Nigeria, for use in water-based drilling mud formulation. Laboratory-scale drilling muds were prepared using raw and chemically activated clays, with sodium carbonate employed as the activating agent and carboxymethyl cellulose (CMC) used as a viscosifier and fluid-loss control additive. Standard American Petroleum Institute (API) procedures were adopted to determine key drilling mud properties, including density, rheological parameters (plastic viscosity, apparent viscosity, yield point, and gel strength), filtrate loss, and pH. The results show that untreated local clays exhibited poor rheological behavior and excessive fluid loss, making them unsuitable for direct drilling applications. However, significant improvements were observed after chemical activation and polymer treatment, with several formulated mud samples meeting acceptable API performance criteria. The study demonstrates that locally sourced clays, when properly activated and enhanced with suitable additives, can serve as viable alternatives to imported bentonite for water-based drilling mud formulation. The findings highlight the potential for cost reduction, improved local resource utilization, and increased sustainability in Nigeria’s drilling operations.

This study presents an integrated production-optimization assessment of a mature, high-water-cut reservoir in the Niger Delta. The aim was to evaluate and compare artificial-lift strategies and system-level interventions to restore economic oil production and extend field life. A validated modeling workflow coupling MBAL (material-balance), PROSPER (well performance and artificial-lift design) and GAP (surface-network optimization) was developed and history-matched to 1987–2004 field data, revealing strong aquifer support and persistent water cuts (~80%). Scenarios examined natural flow, gas lift, and electric submersible pump (ESP) implementations with nodal and sensitivity analyses (IPR/VLP matching, PVT calibration, and network constraints). Results show natural flow is insufficient under present conditions; gas lift yields modest gains (≈720 STB/day total liquid, ~144–177 bbls oil/day) with moderate gas requirements, while ESPs can deliver substantially higher liquid and oil rates (pump-design rates up to ~3,800–4,300 RB/day and oil production improvements), albeit with higher power, gas-handling, and reliability considerations. Integrated GAP analysis highlights surface constraints and water-handling costs as decisive economic drivers. Recommendations include staged pilot deployment (ESP where power and conditions permit; gas lift where gas is abundant), targeted diagnostics and zonal shut-off, surface-network upgrades, and ongoing model re-calibration. The study demonstrates the value of coupled reservoir–well–surface workflows for optimizing mature, high-water-cut assets.

The accurate measurement of crude oil during custody transfer is a critical determinant of revenue assurance and operational efficiency in the oil and gas industry. In the Niger Delta, where fiscal losses from measurement inaccuracies and operational inefficiencies are acute, optimizing Lease Automatic Custody Transfer (LACT) systems becomes a strategic imperative. This study aims to optimize LACT system performance by enhancing measurement accuracy and operational efficiency, with specific focus on the Nigerian context. Employing a novel dual-methodology approach, the research first conducts a meter drift simulation to model the financial impact of calibration decay on a turbine meter, evaluating four proving strategies: annual, quarterly, monthly, and predictive. Second, it utilizes machine learning techniques, specifically Random Forest regression, on a dataset from a Port Harcourt LACT unit to model the relationship between operational parameters (pressure, temperature, API gravity) and flow rate, identifying optimal conditions for throughput maximization. The findings reveal profound financial implications. The current industry practice of annual proving results in an estimated annual loss of $10.48 million per LACT unit. A shift to predictive proving could reduce this loss by 99.5%, saving approximately $10.43 million annually. The machine learning model (R² = 0.716) identified operating pressure as the most significant parameter influencing flow rate and pinpointed an optimal operational window (Pressure: 75.3 psi, Temperature: 92.2°F) that can improve flow rates by over 10.5% compared to sub-optimal conditions. The study concludes that significant optimization potential exists through data-driven strategies. It provides a clear, actionable roadmap for operators and regulators, recommending an immediate transition to quarterly proving and the adoption of setpoint optimization, with a long-term view towards predictive maintenance and digital integration. This research demonstrates that leveraging operational data and modern analytical techniques is key to safeguarding national revenue, enhancing transparency, and achieving operational excellence in Nigeria's oil and gas sector.

This study explores Optimal Injection Strategies for Enhanced Oil Recovery (EOR) by employing computational modeling and optimization methods to maximize oil recovery, reduce operational expenses, and improve environmental sustainability. The research evaluates various injection techniques, such as water injection, CO₂ injection, and chemical flooding, through reservoir simulation models. Critical reservoir properties, including porosity (0.18), permeability (200 mD), and oil saturation (70%), were analyzed to determine their influence on recovery efficiency. Optimization approaches like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) were applied to refine injection parameters. CO₂ injection emerged as the most effective approach, delivering 85% oil recovery over a 10-year timeframe, while reducing operational costs by 12% compared to waterflooding. Sensitivity analysis revealed that higher permeability enhances CO₂ injection efficiency, although increased injection rates, despite their benefits, led to premature water breakthrough and elevated costs.Furthermore, CO₂ injection supported greenhouse gas sequestration, contributing to environmental sustainability. The findings underscore the importance of computational optimization in EOR strategies. Future research should focus on real-time monitoring and adaptive optimization methods to improve field application and optimize reservoir performance further.

The growing need for sustainable petroleum production and greenhouse gas reduction has driven interest in carbon dioxide (CO₂)-based Enhanced Oil Recovery (EOR) as a dual-purpose technology for increasing oil recovery while enabling carbon sequestration. This study investigates the potential of CO₂ injection for enhanced oil recovery and geo-storage in tight sandstone reservoirs within the Niger Delta Basin, Nigeria. Laboratory-based simulations and analytical modeling were used to evaluate the influence of injection rate, permeability, wettability, and fracture dip angle on oil displacement efficiency and CO₂ storage performance. Results show that at lower injection rates (0.2 mL/min), delayed breakthrough (0.85 PV) was observed with a recovery factor of 88.2%, while moderate rates (0.5 mL/min) achieved optimal recovery (91.4%) and balanced CO₂ retention (58.1%). Higher rates (1.0 mL/min) led to early breakthrough (0.55 PV) and reduced storage efficiency (54.9%). Overall, findings demonstrate that CO₂ injection enhances oil recovery by 85–93% and achieves up to 65% storage efficiency, depending on reservoir conditions. The study confirms that CO₂-EOR is technically feasible for the Niger Delta Basin, offering both economic and environmental benefits by improving hydrocarbon recovery and reducing carbon emissions.

Offshore drilling operations constitute one of the most technically demanding and hazardous activities in the global petroleum industry. The complexity of these operations, combined with the challenges of deep-water environments, necessitates the implementation of rigorous safety protocols to protect personnel, assets, and the environment. This study provides a comprehensive overview of safety protocols in offshore drilling operations, emphasizing their effectiveness, challenges, and prospects for improvement. The research adopts a qualitative methodology that integrates literature reviews, regulatory reports, and case analyses from national and international offshore operations. Key focus areas include risk assessment modeling, safety management systems, regulatory compliance, human and organizational factors, and the application of modern technologies such as artificial intelligence, predictive analytics, and digital twin systems in enhancing offshore safety performance. Findings reveal that although substantial progress has been made in safety management and regulatory enforcement, gaps still exist in the consistent application of safety standards, particularly in developing regions where limited resources and weak regulatory oversight hinder full compliance. The study further highlights that most offshore incidents result from a combination of technical failures and human factors, underscoring the need for stronger safety cultures, continuous training, and advanced monitoring technologies. It concludes that a holistic, technology-driven, and human-centered approach is essential for achieving sustainable offshore safety. The research therefore recommends the adoption of integrated safety management frameworks, proactive risk mitigation strategies, and harmonization of global safety regulations to ensure safer, more resilient, and environmentally responsible offshore drilling operations.

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