FACULTY OF ENGINEERING

This study examines the performance of the ZigBee Received Signal Strength Indicator (RSSI) in indoor environments, with a focus on understanding how distance and environmental obstacles influence wireless signal propagation. The research was conducted at the Faculty of Engineering, University of Benin, utilizing two ZigBee Pro S2B modules configured through XCTU software. Measurements were taken at distances ranging from 10 feet to 50 feet, under various conditions involving obstacles such as furniture, walls, and human presence. The findings indicate that RSSI values exhibit a progressive decline with increased distance and greater obstacle density. Specifically, the signal strength diminished by approximately 4 to 6 dB for every 10-foot increment, with an additional decrease of 3 to 5 dB for every two additional obstacles encountered. It was determined that walls and human presence are the most significant factors contributing to signal attenuation, due to effects related to reflection, absorption, and scattering. These results are consistent with theoretical path loss models and corroborate prior empirical studies, reinforcing the notion that the performance of ZigBee technology is significantly influenced by environmental conditions. The study concludes that ZigBee is well-suited for short-range, low-power Internet of Things (IoT) and sensor applications; however, optimal node placement and the implementation of mesh networking are critical for ensuring reliable communication in complex indoor environments. The insights derived from this research hold valuable implications for enhancing the design and deployment of wireless sensor networks in academic and smart-building contexts.

Traditional classroom lighting systems often lack adaptability and responsiveness, hindering the creation of optimal learning environments. This report presents the development and evaluation of a smart classroom lighting system designed to address these limitations. The system utilizes motion sensors and passive Infrared sensors] to detect the motion of people within the classroom and to check the current status of the classroom lights based on pre-programmed schedules. A mobile app interface allows for user interaction and customization. The project methodology encompassed system design, control algorithm development, user interface creation, prototyping, and performance evaluation. The system was evaluated through energy consumption monitoring, user surveys, and lighting environment measurements. Findings revealed that the smart lighting system led to a reduction in energy consumption, improved user satisfaction with the learning environment, and potential for enhanced student learning outcomes. This report concludes by discussing the system's potential for implementation in educational settings and future research directions.

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.

Flooding along the River Niger remains a persistent hydrological and humanitarian challenge, exacerbated by data scarcity, climate intensification, and rapid land-cover transformation. This study advances a sustainable framework for flood prediction and risk mapping by integrating geospatial technologies with deep learning in data-limited riparian environments. The study focused on the Agenebode–Idah corridor, delineated by a 20 km buffer on either side of the River Niger. The research aimed to design, train, and validate a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN– LSTM) model capable of representing both the spatial complexity and temporal evolution of flood dynamics. The overarching purpose was to enhance anticipatory flood governance and inform climate-resilient development strategies across vulnerable communities of the lower Niger Basin. Methodologically, the study employed a multi-source geospatial architecture integrating CHIRPS rainfall, ERA5 soil moisture and temperature, SRTM topography, Sentinel-1 SAR inundation data, and Landsat-derived land-use/land-cover information. The CNN component extracted geomorphological and hydrological features, including elevation, slope, vegetation, and proximity to rivers, while the LSTM component learned the sequential dependencies of hydroclimatic variables over time. Model calibration and validation employed rigorous statistical and spatial metrics, including ROC–AUC, F1- score, RMSE, and confusion matrix analysis, alongside geospatial overlays between predicted and observed flood masks. The framework simulated both historical flood dynamics (2000–2024) and flood probabilities (2026–2035), allowing for a comprehensive spatiotemporal risk assessment. Results revealed that the hybrid CNN–LSTM achieved high predictive fidelity (ROC– AUC = 0.92; F1 = 0.86; RMSE = 0.081), outperforming baseline machine-learning and hydrodynamic models. Spatial analyses identified chronic flood hotspots in Agenebode, Anegbete, Ifeku Island, Ogurugu, and Idah. Forward projections indicated a 34% spatial expansion of high-risk zones by 2035, primarily across cropland and peri-urban settlements. The study concludes that coupling AI-driven deep learning with geospatial intelligence enhances flood forecasting capacity in data-scarce regions, providing a replicable, operational blueprint for early warning systems, spatial planning, and adaptive water governance in the face of accelerating climate variability.

This project proposes the design and implementation of a smart meter management system. The system will focus on electricity metering and leverage smart meter technology to collect realtime consumption data. Improvements in metering infrastructure are required due to the growing need for better customer service and energy efficiency. The “Design and Implementation of Smart Meter Management System” project aims to create an efficient and intelligent system for managing energy consumption using smart meters. Smart meters, equipped with advanced sensors and communication modules, enable real-time data collection and bidirectional communication between utility providers and consumers. This smart meter management system has the potential to revolutionize energy management by providing valuable insights into consumption patterns, enabling optimized billing practices, and empowering consumers to make informed choices about their energy use. The system will gather, analyze, and manage smart meter data to improve energy usage monitoring, billing accuracy, and resource optimization.