DEPARTMENT OF PETROLEUM ENGINEERING

Condensate banking is a critical flow assurance challenge in gas condensate reservoirs that can reduce well productivity by up to 60% due to near-wellbore liquid accumulation when reservoir pressure falls below the dew point. Accurate prediction of this phenomenon is essential for optimizing field development strategies, well design, and production forecasting. However, conventional cubic equations of state, particularly the widely used Peng-Robinson (PR) equation, systematically underpredict the severity of condensate banking due to fundamental limitations in their mean-field thermodynamic assumptions. This research presents a novel modification to the Peng-Robinson equation of state that incorporates density-dependent attractive forces to better capture the molecular correlations and beyond-mean-field effects that dominate liquid phase behavior in gas condensate systems. The proposed PR-DD (Peng-Robinson with DensityDependent attraction) modification introduces a densitycorrection function, f(ρᵣ) = 1 + c₁ρᵣ² + c₂ρᵣ⁴, to the attractive parameter, where ρᵣ is the reduced density and c₁, c₂ are empirically determined coefficients. This modification addresses the critical deficiency of standard cubic equations in representing the enhanced intermolecular attractions that occur at liquid densities, particularly relevant for accurately predicting retrograde condensation and liquid dropout volumes. The methodology encompasses three major components: (1) development and validation of the PR-DD equation of state against experimental PVT data, including constant volume depletion (CVD) tests showing liquid dropout curves; (2) implementation of PR-DD within a fully compositional reservoir simulator using local grid refinement to capture near-wellbore gradients; and (3) comprehensive comparison with standard PR predictions through parallel simulations of a representative offshore gas condensate reservoir

This research focuses on determining the effect of divalent salts on the rheological properties of beneficiated gum Arabic, with the aim of assessing its suitability as a vicosifying and filtration control agent in drilling fluids. Gum Arabic, a natural biopolymer, was beneficiated to enhance its purity and performance before being exposed to varying concentrations of divalent salts such as calcium chloride (CaCl₂) and magnesium chloride (MgCl₂). Rheological analyses were conducted to evaluate parameters including plastic viscosity, yield point, and gel strength under different shear rates and aging conditions. The results showed that the presence of divalent salts significantly altered the flow behavior of the gum Arabic-based fluids. Increasing salt concentration led to ionic cross-linking between the salt cations and the polymer’s functional
groups, improving viscosity and gel strength at lower concentrations but causing flocculation and viscosity reduction at higher concentrations. The interaction also affected the thixotropic behavior and structural stability of the fluid. Overall, the study demonstrates that divalent salts play a crucial role in modifying the rheological characteristics of gum Arabic solutions. The beneficiated gum Arabic exhibits potential as an eco-friendly, biodegradable, and cost-effective alternative to synthetic polymers for drilling fluid formulation, contributing to sustainable petroleum engineering operations.

Conventional decline curve analysis (DCA) methods often fail to provide reliable short-term production forecasts in Niger Delta wells due to significant reservoir heterogeneity—permeability variations of up to 300%—as well as frequent operational disruptions, such as pipeline vandalism that can cause annual losses of around 15%. In addition, non‐stationary decline behavior further complicates forecasting efforts. Previous machine learning approaches have not been specifically validated for the unique conditions of the Niger Delta, leaving a gap in
accurate, regionally tailored predictive tools. To address these challenges, we assembled a daily time-series dataset spanning 2010 to 2023 from 10–15 mature wells in Niger Delta fields. Input variables included oil, gas, and water production rates; downhole and tubing pressures; and choke size. We engineered additional features, such as seven‐day rolling means of production rates and time‐since‐last‐workover, to capture temporal dynamics more effectively. Data preprocessing involved robust scaling, linear interpolation to fill missing entries, and removal of outliers beyond three standard deviations. Our predictive model is a feedforward artificial neural network (ANN) with three hidden layers—256, 128, and 64 neurons respectively—using ReLU activations, 20% dropout for regularization, and batch normalization at each hidden layer. We employed a sliding 30‐day input window, optimized the network using the Adam algorithm on mean squared error loss, and implemented early stopping to prevent overfitting. Model performance was evaluated using a chronological split of 70% training, 15% validation, and 15% test data, with metrics including RMSE, MAPE, and R². Results indicate that the ANN consistently outperforms a Random Forest (RF) benchmark across all targets. For oil production, the ANN achieved an R² of 0.92 compared to RF’s 0.87, with an RMSE of 0.34 versus 0.45. The
most pronounced improvement was observed in water rate predictions, where the ANN attained an R² of 0.93 and MAPE of 0.24, while RF achieved 0.83 and 0.35, respectively. Feature‐importance analysis revealed wellhead pressure (correlation r≈0.85 with oil rates) and choke size as key drivers. Furthermore, residual analysis showed that the ANN’s errors maintain uniform variance across the production range, whereas the RF tends to underpredict at higher production
rates. The ANN’s superior performance stems from its ability to capture nonlinear interactions—such as complex choke‐pressure‐fluid relationships—that are critical for modeling sudden surges in water cut. By leveraging engineered features like rolling means and workover timing, we effectively captured temporal dependencies without resorting to recurrent architectures. In practical terms, the high accuracy (R² > 0.90) achieved by the ANN supports proactive choke management and maintenance scheduling, helping operators mitigate downtime in a region notorious for operational interruptions. However, the study has limitations. The dataset is confined to Niger Delta fields, which may limit generalizability across other operators or geological settings. Additionally, the feed forward ANN functions as a “black box,” and further work is required to incorporate explainability methods (e.g., SHAP or layer‐wise relevance propagation) before full field deployment. In conclusion, this research presents the first validated feed forward ANN tailored to Niger Delta wells, enabling reliable 180‐day production forecasts and marking a shift from reactive to predictive production management in this challenging environment.

The study focuses on the formulation and comparing efficiency of plant-based demulsifiers as sustainable substitutes of disintegrating water-in-oil emulsions during crude oil processing. The objective of the study is to determine the efficiency of the selected natural materials in the demulsification process like clove extract, coconut oil and orange and banana peels combined, besides evaluating the effect of external forces on the work of the demulsifier like diesel dilution and magnetic fields. Fourier Transform Infrared Spectroscopy (FTIR) was used to determine chemical composition of each bio-extract and functional groups to develop a relationship between the structure and activity in terms of emulsion destabilization. The quantitative information about the performance trends was gained with the help of experimental data during a
70-minute period of treatment, which was analyzed using the standard deviation statistics (which
are tabulated in the appendix). Findings showed that all the natural demulsifiers were highly
interfacially active due to the presence of surface-active compounds, including phenolics, flavonoids, terpenes, and fatty acids, which influence the interfacial activity of the natural demulsifiers through their ability to destabilize the interfacial films and induce droplet coalescence. The clove extract recorded the greatest demulsification efficiency among all samples that can be explained by its high phenolic concentration and good amphiphilicity. It was found that diesel dilution and magnetic treatment can affect, but not change much of the demulsification behavior, which proves that intrinsic chemical composition is a stronger factor, compared to extrinsic factors. All in all, the research proves that the natural plant demulsifiers have a promising potential to substitute the traditional chemical demulsifiers in the process of crude oil treatment and to provide similar or better results at less environmental and economic expenses. The results highlight the promise of green demulsification technologies as an important measure to achieve sustainable production and processing of petroleum.

Permeability is one of the most important properties in reservoir engineering because it controls how fluids move through rocks and strongly influences production forecasting, recovery efficiency, and field development planning. Conventional methods for estimating permeability depend on core measurements and empirical correlations with porosity and water saturation. While core analysis provides accurate results, it is expensive, time-consuming, and limited to specific depths. Empirical models such as those proposed by Timur, Coates and Dumanoir, and Tixier often fail to capture the complexity of heterogeneous formations like the Niger Delta. This
study develops an integrated framework that combines core

Wax deposition remains a major flow assurance challenge in crude oil pipeline systems and is particularly severe in regions such as the Niger Delta where waxy crude oils and fluctuating operating conditions promote rapid cooling and crystallisation of paraffinic hydrocarbons. This study provides an assessment of wax deposition prevention and mitigation technologies through a systematic review of peer-reviewed literature published from 2022 to date. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework was adopted to identify, screen and analyse relevant publications. The review examines chemical, thermal and mechanical approaches with emphasis on their mechanisms, effectiveness, limitations and suitability for wax-prone crude oils. Across the collected studies, chemical methods emerged as the most extensively researched and adaptable strategy for prevention. Polymeric pour point depressants, crystal modifiers, solvent blends and plant-based inhibitors demonstrated strong capabilities in reducing Wax Appearance Temperature, altering crystal morphology and improving crude oil flowability. Several authors reported that natural inhibitors derived from jatropha oil, palm kernel oil, palm oil and other agricultural sources produced inhibition efficiencies comparable to synthetic formulations while offering environmental and economic advantages. Nanoparticle enhanced additives also showed improved thermal stability and dispersive behaviour. Thermal methods such as insulation, active heating and temperature maintenance remained effective for keeping crude oil above its crystallisation point although their energy requirements limit continuous use. Mechanical techniques such as pigging continue to dominate remedial operations whenever deposition has already occurred despite operational challenges such as pig sticking and unpredictable wax breaking forces. The findings show that no single method provides a universal solution. Effective management requires combining preventive strategies with periodic remediation while accounting for the crude oil composition, operating conditions and pipeline characteristics. The review highlights the growing potential of plant based additives, nano enhanced inhibitors as environmentally responsible and economically viable alternatives. The study therefore contributes to improving flow assurance practices and supports the development of sustainable wax management strategies within the Niger Delta and other regions producing waxy crude oils.

One of the most significant threat to petroleum production is that of sand production resulting from the migration of formation sand caused by the flow of reservoir fluids. Conventional well Completions in soft formations commonly produce formation sands or fines with fluids. Today’s operators need access to complete production system, delivering intelligent real time information and operation alongside existing instrumentation. Hence, Acoustic Sand Monitors and Intrusive Erosion Probes are invaluable tools in detecting the presence and effect of solids production. Accurate monitoring coupled with analysis and interpretation of the real time data can guarantee improved longevity of the asset and greatly reduce cost JK of repairs, replacement and downtime. Detailed, real-time information can also help to optimise production, and in many cases adjustments can be made to individual wells to increase oil production where sand is not an issue. This study presents an analytical assessment of sand production monitoring from an offshore field in the Niger Delta region from real time oil field production data. The data used in this study have been obtained from sand signals generated from acoustic sound detectors installed along flow paths in the oil production facility. In this study, sound signals generated by solids particles (sands/fines) along the flow paths of the facility are analysed to monitor sand production from the wells in the field over a period of nine years of production. These wells were consequently categorized as high sand producers or low sand producers following the percentage deviation of the sand signal averages for the wells from the corresponding baselines for each of the wells in comparison to the established percentage deviation threshold. At the end of this study, four wells were categorized to be high sand producers while nine were categorized as low sand producers.

Flow assurance remains a critical challenge in the oil and gas industry, where complex interactions among temperature, pressure, corrosion, and flow dynamics can lead to operational inefficiencies, production losses, or complete pipeline blockage. Traditional rule-based and thermodynamic models often fall short in capturing the nonlinear, multi-parameter dependencies underlying these challenges. In this study, a data-driven framework was developed for the prediction and early detection of flow assurance challenges in oil and gas pipelines using ensemble machine learning models.
A dataset comprising twenty-four operational and material parameters including temperature, pressure, pipe size, flow rate, corrosion impact, and energy consumption was analyzed to classify pipeline states into normal, moderate, and critical risk categories. Three algorithms Random Forest, Support Vector Machine (SVM), and Gradient Boosting—were implemented, optimized through grid-based hyper parameter tuning, and evaluated using cross validation and standard performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices.
The results indicated that all three models successfully identified key operational relationships influencing flow assurance risks. The Random Forest model achieved a high training accuracy of 98.71% but showed overfitting with a test accuracy of 40.33%. The SVM model achieved a test accuracy of 45.00% with a recall of 70.6% for critical conditions. The Gradient Boosting model outperformed both, achieving a cross-validation score of 47.71%, test accuracy of 49.33%, and recall of 97.2% for critical flow states, with minimal overfitting (accuracy gap of 4.10%).
The study concludes that ensemble machine learning methods, particularly Gradient Boosting, offer a reliable and interpretable approach for predicting and classifying flow assurance challenges. By enabling early detection and proactive intervention, the proposed framework can support predictive maintenance, reduce pipeline downtime, and enhance operational safety and efficiency in oil and gas transportation systems.

In most drilling operations, it is evident that a considerable amount of money is spent when faced with drilling related problems such as stuck pipe, lost circulation and excessive mud cost which in turn leads to an increase in non-productive time especially in deep-water environment where the pore pressure and fracture pressure gradient is very close (narrow drilling window). Managed pressure drilling (MPD) has been found to be effective when compared with conventional drilling method to eliminate or reduce these hole problems. The MPD technique is of two types: Reactive MPD (where MPD equipment is rigged up in cases of drilling hazards) and Proactive MPD (MPD equipment is rigged up from the onset). There are four variations of MPD and each variation used must suite the drilling hazard to be mitigated.In this review, three case studies in the deep-water environment (Bolontiku Field in the Gulf of Mexico, Tarim Basin in China and Kristin Field in the Norwegian sea in the haltenbanken area) were taken, to comparatively analyze the challenges, methods used and the drilling hazards encountered when drilling conventionally and with MPD technique, using indices of comparison such as: Cost of drilling, target depth attained, number of drilling days and mud losses seen. In all the three cases considered, the drilling hazards were encountered while drilling conventionally, which were not able to be mitigated. However, with the application of the constant bottom hole managed pressure drilling technique, in the Bolontiku and Tarim Basins, the wells were drilled safely, reduced the number of drilling days by 50% when compared with the conventional drilling technique. Application of the pressurized mud cap drilling was done on the Kristin field and it shows that MPD enabled drilling in highly depleted reservoirs with the application of a static drilling fluid weight below the original pore pressure.

This report explores the challenges associated with gas hydrates in the Niger Delta oil and gas fields, focusing on their formation, inhibition techniques, and effective management strategies. It begins with a review of the thermodynamic and kinetic conditions that favor hydrate formation, emphasizing the influence of pressure, temperature, and gas composition. Utilizing machine learning models, including linear regression and random forest, the study predicts hydrate volume fractions, with the random forest model demonstrating superior accuracy. The effectiveness of traditional inhibitors such as methanol and monoethylene glycol (MEG) is evaluated, highlighting their roles in mitigating hydrate formation. The report identifies critical research gaps, including the need for field testing of inhibitors and comprehensive modeling of hydrate behavior in real-world conditions. Recommendations for future work include enhancing collaboration among industry stakeholders, conducting economic analyses of inhibition techniques, and investigating innovative materials like nanoparticles. By implementing these strategies, the oil and gas industry can improve operational efficiency and ensure sustainable production in the Niger Delta region.