PGD Project

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 examined the influence of sound office and record management on school administration in public secondary schools in Ovia North-East Local Government Area of Edo State. The purpose of the study was to explore the significance and role of effective office and record management in the administration of secondary schools, with particular attention to teachers in the Ovia North East Local Government Area of Edo State. Four research questions were raised. A descriptive survey research design was adopted for the study. The population of the study was 400 teachers and a sample of 80 teachers was drawn using a multi-stage sampling technique. Data were collected using a researcher-developed instrument titled Office and Record Management Practices Questionnaire (ORMPQ), which was validated by experts in Educational Administration and Measurement and Evaluation. The findings revealed that sound office management positively influences administrative effectiveness through improved communication, information flow, and coordination of activities. School location was also found to influence record management practices, with urban schools demonstrating relatively better practices than rural schools. Major challenges identified included poor filing systems, inadequate office facilities, insufficient record-keeping materials, and inadequate staff training. Based on the findings of the analysis, the following recommendation were made: among other things, that government should provide adequate funding for record management facilities, that school administrators should enforce clear record management policies, and that regular training programmes should be organised for teachers on modern office and record management techniques.

Flooding has been a recurring natural disaster in Nigeria, with the 2022 flood being one of the most devastating in the nation’s history. This study investigated the linguistic and discursive strategies employed in media representations of the 2022 Nigerian flood discourse, focusing on their role in shaping public perception, framing narratives, and revealing underlying ideologies. Utilising a qualitative research approach, 90 purposively selected media reports, out of which 50 excerpts were extracted and analysed, with a specific focus on those covering Delta, Edo, Kogi, Ebonyi, Anambra, Benue, Rivers, Akwa Ibom, Bayelsa and Abuja—the most severely affected regions. The study adopted Teun van Dijk’s socio-cognitive theory as its framework, emphasising the interplay between language, cognition, and societal structures. The study revealed that the media employed linguistic tools such as actor description, argumentation, and evaluation to construct narratives that project dominant ideologies, shape social identities, and highlight power dynamics. The media assigned blame, constructed authority, evoked empathy and amplified the urgency of the flood crisis through language use. These strategies influenced public perception, reinforced systemic inequalities, urged collective responsibility, and advocated proactive governance. By analysing the linguistic elements and socio-cognitive dimensions of the flood discourse, this study contributed to the field of critical discourse analysis by demonstrating how language was used to frame societal crises and influence public discourse. It underscored the importance of language as a tool for critiquing, shaping, and mobilising responses to disasters in a way that aligned with Nigeria's complex sociopolitical landscape.

Due to industrialization, population growth and urbanization in Yenagoa and environs, anthropogenic activities are increasing daily and could introduce contaminants into soil and water resources.This study evaluated the causes, distribution, and environmental effects of heavy metal pollution in groundwater, surface water, and soil in Yenagoa and environs, Niger Delta, Southern Nigeria. The study sought to compare concentrations with control values for soil, surface and ground water, and regulatory standards (WHO and NSDWQ) for groundwater, as well as assess the degree and variability of heavy metal pollution across various anthropogenic sites, including waste disposal sites, mechanic shops, cassava mills, and areas affected by crude oil spills. A total of one hundred and twelve (112) samples were randomly collected for each season, including 77 soil samples depth 30cm and 35 surface and ground water samples. These samples were then evaluated using standard laboratory technique, Atomic Absorption Spectrophotometer (AAS) during both the wet and dry seasons. Soil contamination levels were measured using pollution indicators such the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI), and groundwater quality was assessed in relation to allowable limits. Results showed that all site types exhibited elevated concentrations of key heavy metals including iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), and vanadium (V). Crude oil spill sites recorded the highest soil contamination, with lead reaching 15.354 mg/kg and

Due to industrialization, population growth and urbanization in Yenagoa and environs, anthropogenic activities are increasing daily and could introduce contaminants into soil and water resources.This study evaluated the causes, distribution, and environmental effects of heavy metal pollution in groundwater, surface water, and soil in Yenagoa and environs, Niger Delta, Southern Nigeria. The study sought to compare concentrations with control values for soil, surface and ground water, and regulatory standards (WHO and NSDWQ) for groundwater, as well as assess the degree and variability of heavy metal pollution across various anthropogenic sites, including waste disposal sites, mechanic shops, cassava mills, and areas affected by crude oil spills. A total of one hundred and twelve (112) samples were randomly collected for each season, including 77 soil samples depth 30cm and 35 surface and ground water samples. These samples were then evaluated using standard laboratory technique, Atomic Absorption Spectrophotometer (AAS) during both the wet and dry seasons. Soil contamination levels were measured using pollution indicators such the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI), and groundwater quality was assessed in relation to allowable limits. Results showed that all site types exhibited elevated concentrations of key heavy metals including iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), and vanadium (V). Crude oil spill sites recorded the highest soil contamination, with lead reaching 15.354 mg/kg and