FACULTY OF ENGINEERING

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.

This project focuses on the upgrade of an electric bicycle to enhance its performance and range. The objective is to improve the distance travelled and charging power of the bicycle through technical enhancements and component upgrades. Methods include the integration of advanced battery technology, indication upgrade, and enhancements to the control systems. Results demonstrate significant improvements in speed, range, and overall user experience. The findings of this project contribute to the advancement of electric bicycle technology, offering insights into potential upgrades for future models

Worldwide efforts to reduce carbon emissions require technologies that can substantially decrease human-caused CO₂ releases while preserving energy reliability. Carbon Capture and Storage (CCS) presents an interim solution by capturing carbon dioxide from industrial facilities and storing it permanently underground in geological structures. This study examines the viability of storing CO₂ in a typical depleted oil field in Nigeria's Niger Delta Basin using sophisticated compositional modeling through Computer Modelling Group (CMG) software. A detailed three-dimensional reservoir model was constructed using geological, rock property, and production data to simulate extended-term CO₂ injection, plume movement, and entrapment patterns. The research assesses actual storage volumes, injection limitations, pressure changes, and the roles of various trapping methods—including structural containment, residual entrapment, dissolution, and mineralization—across a century-long timeframe. Findings show storage capacity ranging from 5.5 to 11.2 million tonnes of CO₂ with pressure remaining safely under fracture thresholds, confirming reliable containment with negligible escape potential. Parameter studies demonstrate that variations in rock permeability, remaining oil content, and injection speeds significantly affect storage performance and CO₂ distribution patterns. The results validate that Nigeria's depleted petroleum reservoirs offer suitable geological and technical conditions for secure, effective carbon sequestration. This research provides a simulation-driven methodology for nationwide CCS implementation, advancing Nigeria's progression toward reduced-carbon energy systems and adherence to global climate commitments.

This project aimed to conduct a comprehensive geotechnical analysis of laterite soil from the Obviogie Borrow Pit, with the goal of assessing its suitability for use in road construction and developing a data bank for future reference. Laterite soils were commonly used in road construction, particularly as sub-base and sub-grade materials, due to their availability and cost-effectiveness. However, it was crucial to evaluate the physical properties of the soil to ensure it met the engineering requirements for long-lasting and stable roads. For this study, a soil sample was collected from the borrow pit and subjected to various laboratory tests to determine its key physical characteristics.
The analysis focused on fundamental properties such as moisture content, specific gravity, and particle size distribution. These factors played a significant role in understanding the behavior of the soil under load and during compaction. These parameters were essential for establishing the soil’s ability to support heavy loads when used as a sub-base or sub-grade material in road construction. The data gathered from the physical and compaction tests were compiled into a detailed data bank. This data bank served as a valuable resource for engineers and road construction professionals, providing them with critical information to guide the selection, preparation, and compaction of the soil for use in road building projects. By offering a clear understanding of the soil's load-bearing capacity and compaction behavior, this project helped ensure that roads constructed in the Obviogie region were built on a solid foundation, enhancing their durability and reducing the need for costly repairs in the future.
The overall goal of this project was to support the use of local materials in road construction while ensuring that they met the necessary engineering standards. This study contributed to the efficient use of laterite soils, which were widely available in the Obviogie area, and helped reduce construction costs by minimizing the need for imported materials. In doing so, the project provided valuable insights that promoted more sustainable and cost-effective infrastructure development in the region.

This study investigates a plant-based surfactant alternative as a result of the growing need for Surfactants that are both economical and ecologically friendly for enhanced oil recovery. These research project comprises the study of enhance oil recovery with it crucial application of Natural surfactant from bitter leaf extract , while using alkaline from calcined corn cob ash to form basic medium . Corn cobs were calcined for 3 hours at 750°C to produce the alkali, which was then extracted using distilled water to produce the alkaline for the medium, the extraction process for the bitter leaf to extract the saponin content to produce the surfactant The extraction process was carried out according to the experimental design with 19 runs with independent variables of extraction time (30-300 minutes) ,mass of bitterleaf (1-10grams) and temperature
(50-90 degree census),and a constant volume of 100 ml of methanol with a single response yield (%), Alkaline surfactant was also produced using alkaline hydrolysis of the saponin was carried out to form the surfactant and75ml of the saponin was mixed with 25ml of the ash solution. Various Physical characteristics was carried out in the process such as forth test where 2.0 gms portion of the powdered sample was boiled into 20ml of distilled water in a test tube in boiling water bath and filtered , 10ml of the filtrate was mixed with 5ml of distilled water and shaken vigorously to foam, Total Saponin Content Quantitative Analysis where a quantity ,1.0 gms of the powdered sample was weighed using electric weighing balance into 25ml beaker and soaked with 100 ml of 20% Methanol for 3 minutes and heated for 3 hours at 55 degree
census for proper extraction then filtered and lastly . The volume and stability of the emulsion was observed and the emulsion index was calculated.

The increasing reliance on sachet water as a primary source of drinking water among students in
Ekosodin underscores the need for rigorous quality assessment. This study investigates the physicochemical and microbiological characteristics of various sachet water brands consumed in the region. Parameters such as pH, turbidity, total dissolved solids (TDS), and the presence of microbial contaminants were analyzed using standard laboratory techniques. The study aims to determine compliance with regulatory standards and assess potential health
risks associated with these products.

With Nigeria generating over 42 million tonnes of waste annually, improper disposal poses significant risks to soil health, groundwater, and public health. This study examines the contamination levels of heavy metals and the physicochemical properties of soil at a solid waste disposal site in Ovia Northeast, Edo State, Nigeria. Soil samples were collected at varying depths (10, 20, 30, and 40 cm) from a dumpsite and a control site, focusing on lead (Pb), iron (Fe), copper (Cu), and manganese (Mn), alongside properties such as pH, bulk density, porosity, organic matter, and electrical conductivity (EC). Results revealed elevated levels of heavy metals at the dumpsite compared to the control site, particularly in the top 10 cm of soil. For example, Pb concentrations reached 12.31 mg/kg at the dumpsite, nearly three times higher than the 4.24 mg/kg observed at the control. Similarly, copper (Cu) levels at the dumpsite peaked at 74.22 mg/kg, significantly higher than the control site’s 57.47 mg/kg. Physicochemical properties demonstrated a strong influence on metal mobility: soil pH at the dumpsite ranged from 7.12 to 7.62, slightly higher than the control’s 6.86 to 6.12. Organic matter content decreased with depth, from 8.74% at the surface to 3.15% at 40 cm in the dumpsite, compared to 9.07% to 2.54% in the control. EC values were markedly higher at the dumpsite (252–290 µS/cm) compared to the control (144–168 µS/cm), reflecting leachate infiltration and ion enrichment. The findings underscore the environmental risks posed by heavy metal contamination, including soil degradation, reduced fertility, and potential bioaccumulation in the food chain. Elevated metal concentrations exceeded WHO permissible limits, necessitating immediate remediation actions. Recommendations include the implementation of sustainable waste management practices, soil remediation techniques such as phytoremediation, and ongoing monitoring to mitigate long-term environmental impacts.

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.

Precision agriculture has emerged as a vital approach for improving agricultural efficiency and sustainability by leveraging advanced technologies. This project focuses on developing a machine learning model that utilizes historical data for precision agriculture applications. The system aims to assist farmers in making data-driven decisions by predicting suitable crops, forecasting crop yields, and detecting potential crop diseases. The proposed solution integrates wireless sensor networks to collect environmental parameters such as rainfall, temperature, humidity, Potassium, Nitrogen and soil pH, which are combined with historical agricultural data. Machine learning models, including Voting Classifier (Support Vector Machines, Gaussian Naïve Bayes, Random Forest) and Linear regression model, are trained and deployed to analyze the data for actionable insights. The Voting classifier model achieved a 99.3% accuracy in predicting suitable crops, while the Linear regression model provided yield forecasts with an R² score of 0.91. The Convolutional Neural Network (CNN) detected crop diseases with an accuracy of 93.8%. The system’s implementation demonstrates the effectiveness of combining historical data with machine learning techniques to enhance precision agriculture practices. By providing accurate and timely information, this solution helps optimize crop selection, improve resource allocation, and mitigate the risk of crop diseases. Recommendations for integration with mobile applications, weather data integration and farmer education and training are proposed to further enhance the system’s usability and impact. This project offers a promising step toward sustainable and smart farming practices, contributing to food security and agricultural productivity.