bluuebunny/crossref_metadata_2025_split huggingface dataset:
Field-Scale Reactive Transport Modeling of Bio-Methanation During Underground Hydrogen Storage in Saline Aquifers
Authors: AL homoud Rana , Machado Marcos Vitor Barbosa , Daigle Hugh 
Published: August 2024

Abstract
In the context of underground hydrogen storage in saline aquifers, hydrogen is in direct contact with the formation brine, which rises various concerns about the dissolution of hydrogen in water, and the microbial/abiotic geochemical reactions that may occur consequently. In this study, we expand on the knowledge of the microbial reactivity during underground hydrogen storage in saline aquifers, in particular, the methanogenesis reaction that consume the hydrogen and carbon dioxide to generate methane. This study delves into the impurity-induced geochemical reactions on a field scale level. Through utilization of kinetic parameters from the experimental data, and advance computational modeling, we elucidate the mechanisms governing this reaction and their consequences for storage efficiency, safety, and environmental impact. On the other hand, the potential of methane formation in the presence of microorganisms and CO2 poses a critical challenge to hydrogen storage. The presence of microbes under appropriate conditions can lead to a considerable loss of hydrogen, with 10-50% of hydrogen being consumed in the methanation reaction. This phenomenon is considered a significant hurdle in the practical implementation of hydrogen storage technologies, and a carful microbial analysis of the existing brine is a crucial step to be considered during the initial stage of screening process. Here, we present a reactive transport model on a field-scale level to enhance the comprehensive characterization of hydrogen behavior and its ultimate fate within reservoir systems. Our investigation addresses the extent of hydrogen loss attributable to biochemical reactions.
Gas Reserve Estimation and Performance Prediction of a Fracture-Dominated Unconventional Reservoir Using Weibull Model
Authors: Mohammed Shaibu , Boakye-Ansah Yaw Akyampon 
Published: August 2024

Abstract
Gas-in-place estimation and fractured gas-well performance prediction are crucial for determining the effectiveness of hydraulic fracture treatment and making technical and economic decisions. Existing empirical models have been used to analyze both oil and gas wells regardless of whether they are exponential decline-based or stretched exponential decline-based. Furthermore, estimation of the parameters of Weibull model is currently carried out on a trial-and-error basis.
In this paper, the Weibull model is used to analyze and predict the performance of a fractured gas well depleting an unconventional reservoir. We demonstrate that the Weibull model can be linearized, with cumulative production as the independent variable and thus allowing the determination of estimated ultimate recovery. Furthermore, probabilistic estimates (P10, P50, and P90) of Barnett shale gas wells are presented. The proposed analysis method uses performance data (rate-cumulative-time data) and circumvents any iteration schemes. Numerical and field data have been used to validate and demonstrate the applicability of the analysis method.
Transient-decline exponent is related to the shape parameter of the Weibull model; consequently, the shape parameter can be estimated during the transient state. However, the scale parameter cannot be estimated until boundary-dominated flow is reached. Although the scale parameter gives an indication of the time at which 63.2% of the gas-in-place has been recovered, it does not suggest the onset of boundary-dominated flow.
The Weibull model is suitable for analyzing a fractured gas-well performance, but not oil-well performance. While oil-well performance is amenable to an exponential decline, not all gas-well performances exhibit an exponential decline. This paper contributes to the existing techniques for estimating gas reserve and predicting the performance of a fracture-dominated unconventional gas reservoir.
An Automated Machine Learning and Analytics Framework for Data-Driven Optimization of Multi-Source Regional Energy Systems
Authors: Ajibade A. A. , Okai M. I. , Okon P. E. , Golagha O. G. 
Published: August 2024

Abstract
With rising energy demands and sustainability goals, optimizing a region's energy mix is critical but challenged by data complexities. Existing solutions face limitations in siloed data sources, inaccurate forecasting, static visualizations, and manual modelling workflows. This pioneering research develops ENERLIZER, the first open-source solution to integrate real-time data analytics and machine learning for end-to-end automated insights into robust multi-energy optimization. This solution is developed on the Streamlit framework and leverages integration with real-time databases to enable continuous operationalization and rapid adaptation. It provides an intuitive interface that transforms raw dataset inputs through customized pipelines. Automated descriptive analytics and aggregations are performed using Pandas. Custom XGBoost Models enable precise energy generation and demand forecasting tailored to the data. Interactive location-based visualizations are generated using Plotly. Validated using data from Spain's energy portfolio from 2015-2018, ENERLIZER delivers more than 600% reduction in analysis time versus conventional manual methods, while improving forecast accuracy. Comprehensive analytics and visualizations provide holistic insights for robust optimization. By pioneering real-time integrated data analytics, machine learning and automation, ENERLIZER breakthroughs key limitations in current energy optimization approaches. This enables more rapid, accurate and holistic data-driven decision-making for strategic energy mix planning and management.
Influence of Surface Modification on the Oil Recovery of Iron Oxide Nanoparticles Under Reservoir Conditions
Authors: Yakasai F. , Jaafar M. Z. , Sidek M. A. , Nwaichi P I. , Ridzuan N. , Mahat S. Q. A. , Abbas A. H. , Ngouangna E. , Gbadamosi A. , Oseh J. O. , Gbonhinbor J. , Agi A. 
Published: August 2024

Abstract
Iron oxide nanoparticles (IONPs) show promise as advanced materials for enhanced oil recovery (EOR) due to their distinct physicochemical properties. However, their tendency to quickly clump together in reservoir brine, driven by the need to reduce high surface energy, poses a significant challenge. This aggregation results in reduced stability and the loss of desirable properties, ultimately impeding effective oil recovery. This study addresses these challenges by examining how functionalizing IONPs with 3-aminopropyltriethyloxysilane (AIONPs), tetraethyl orthosilicate (SIONPs), and a combination of both (ASIONPs) enhances their physicochemical characteristics to facilitate oil recovery in reservoir conditions. The nanoparticles were synthesized and modified in the laboratory, with confirmation of synthesis and functionalization achieved through analyses using high-resolution transmission electron microscopy (HRTEM), energy dispersive X-ray analysis (EDX), X-ray diffraction (XRD) and Thermogravimetric analysis (TGA). To accurately simulate reservoir conditions, actual reservoir cores and high-temperature, high-pressure (HTHP) core flooding equipment were used to evaluate the oil recovery of the resulting nanofluids (NFs). Visual sedimentation investigations of NPs in distilled water, low and high electrolyte concentrations revealed that APTES functionalization of IONPs and SIONPs reduced sedimentation of IONPs in the reservoir, especially in high electrolyte conditions. Under reservoir conditions (25,000 ppm, 2400Psi, 120°C), the respective oil recovery percentages for ASIONFs, SIONFs, AIONFs, and IONFs were found to be 14.6%, 13.8%, 18.3%, and 4.8%. These comprehensive findings significantly contribute to our understanding of how functionalization influences the oil recovery efficiency of IONPs in reservoir settings.
Predictive Modeling of Daily Oil Production Volume Using Pressure-Dependent Parameters: A Machine Learning Approach and Web App Implementation
Authors: Ajibade A. A. , Okafor I. S , Ogolo O. , Golagha O. G. 
Published: August 2024

Abstract
This paper presents the development of a machine learning model for predicting daily oil production volume in barrels (bbl) based on pressure-related data. The Volve field production data from 2008 to 2016, operated by Equinor, was used for training and testing the model. An exploratory data analysis was conducted to identify outliers and anomalies in the dataset, and the most relevant features for the prediction task were selected based on statistical methods and domain knowledge. The selected features included pressure-related parameters such as average downhole pressure, average production tubing size, average annulus pressure, average choke size in percentage, average wellhead pressure, production choke size, and onstream hours. To prevent overfitting and bias, features with high correlation were identified and dropped, and the remaining features were evaluated using Mutual Information (MI) scores. Various algorithms, including Convolutional Neural Networks (CNN), Feed-Forward Neural Network (FNN), Linear Regression (LR), XGBOOST, and Long Short-Term Memory (LSTM) Networks, were tested for training and evaluating the predictive performance of the model. The LSTM model outperformed the other models with an average metric score of 104, demonstrating superior ability to generalize trends and learn minimal signals. The model was then deployed on a web app for the prediction of oil production volume based on pressure-related data. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used as metrics to evaluate the performance of each model. The results indicate that the developed machine learning model is highly effective for predicting daily oil production volume using pressure-related data. This approach has the potential to significantly improve production optimization in the oil and gas industry, offering a robust and reliable tool for operational decision-making.
Optimization of Polymeric Nanofluid Oil Recovery Mechanisms at Reservoir Condition
Authors: Al_Ani M. , Panis N. I. , Manikumar Y. , Sidek M. A. , Jaafar M. Z. , Ridzuan N. , Gbonhinbor J. , Umunnawuike C. , Money B. , Agi A. 
Published: August 2024

Abstract
Nanoparticles have been used to improve the properties of oilfield polymers however, at higher and prolonged temperature, dihydroxylation of the bonds occurs decreasing the affinity of the oilfield polymers towards the surface of the nanoparticles. Hence, polymeric nanoparticles (LPNP) with rigid structures have been sought after to circumvent this problem. Therefore, in this study, LPNP was synthesized from sago palm (Metroxylon sagu) bark and used as a rheological agent to improve the viscosity of displacement fluids. Thereafter, the synthesized LPNP was characterized via transmission electron microscopy, particle size analysis, zeta potential, Fourier transform infrared spectroscopy and thermogravimetric analysis. Then, the rheological flow behaviour of lignin polymeric nanofluid (LPNF) was investigated at low and high shear rates utilizing a 350 RST Brookfield rheometer. Box-Behnken design was used to simulate the effect of salinity, shear rate, concentration, and temperature on the viscosity of LPNF. Statistical analysis of variance was used to analyse various parameters of the model. Finally, any parameter combination that resulted in the maximum viscosity was recorded and optimized using a multi-response surface model. The synthesis method was efficacious in producing LPNP with a size range of 10–23 nm. Besides, LPNF exhibited shear thinning and pseudoplastic behaviours even at high salinity and showed good stability up to a temperature of 170°C. The predicted viscosity with a regression coefficient (R2) of 0.8 indicates that the experimental data were accounted for by the model. The desirability of 0.95, which is close to unity, indicates that statistical analysis and experimental evidence have demonstrated that LPNF has acceptable flow behaviour under reservoir conditions.
Modelling and Simulation of a CO2 System as a Replacement for Propane in the Pre-Cool Loop of a Propane-Mixed Refrigerant Cycle
Authors: Timighe P. S. , Adeniyi A. T. , Omofoyewa M. G. , Giwa A. 
Published: August 2024

Abstract
To fulfil the energy needs of the nations where it is regasified and delivered via pipelines, specialist tankers transport liquefied natural gas (LNG), a reliable and efficient energy source, across the sea. To improve efficiency and environmental acceptability, research is being done on substituting carbon dioxide for propane in the precool loop of the propane-precooled mixed refrigerant cycle. The primary goal of the research is to compare carbon dioxide and propane as refrigerants for the precooling loop of a propane-mixed refrigerant (C3-MR) cycle, which is utilised to liquefy natural gas, utilising Aspen HYSYS to design the plants. The performance of CO2 and propane were examined in this study using various process factors that were discovered through sensitivity analysis. The study determined both refrigerants’ coefficients of performance (COP), which show how well they cool while consuming less energy. In comparison to propane, which had a COP of 3.555, carbon dioxide showed a slightly higher COP of 3.728, indicating that it was more effective at transmitting heat under the given circumstances. The impact of variations in the precooling refrigerant pressure and molar flow rate on the compressor's duty for the carbon dioxide and propane precooling cycles was also examined in this study. Both refrigerants demanded more effort from the compressors as the molar flow rate was raised, but carbon dioxide fluctuated in compressor duty a little more than propane. In a similar vein, both refrigerants showed reduced compressor workload with increasing pressure, with carbon dioxide once more exhibiting somewhat greater compressor duty changes. Based on the data gathered, the study came to the conclusion that carbon dioxide, with its greater coefficient of performance, superior heat transfer efficiency, safety advantages, and less environmental impact, was the best refrigerant for the precooling loop in the C3-MR cycle.
Optimal Development of a Virgin Heavy Oil Reservoir with Multiple Zones, Fluid Contacts and Structural Uncertainties in the Niger Delta
Authors: Ezeaneche Obinna. S. , Odewoye Oluwatosin , Abdullahi Muhammad , Alaigba David , Udeze Chima Cyril , Oyeleke Kehinde. Z. , Bernard Ebere , Ijeoma Festus 
Published: August 2024

Abstract
Traditionally, the development of heavy oil reservoirs is not attractive to marginal field operators, due to challenges such as low Expected Ultimate Recovery, complex well trajectory, early water breakthrough and high operational cost. However, there has been a recent shift from the old ideology of "abandon" to "development", owing to improved technology and depletion of conventional oil resources. This paper is focused on a virgin reservoir B within the Niger Delta that has good reservoir properties, multiple fluid contacts observed from logs and supported by RFT. The reservoir fluid viscosity is 12cp with an initial solution gas of 32 scf/stb. Two different seismic vintages resulted in two slightly different structural relief. The old seismic vintage shows the structural relief increasing towards the Northeast but in the newly interpreted seismic vintage, the structural high increases towards the Northwest. Simulating these two static model realizations would result in different production profiles. An integrated approach utilizing static and dynamic modeling processes was adopted to evaluate the development of reservoir B. Various well trajectory types were considered during reservoir simulation and well planning. A relatively poor performance was observed from the vertical well path due to rapid water breakthrough. The deviated well path indicated a better performance than the vertical. However, the horizontal well path was finally selected as the most optimal to drain the reservoir due to delayed water breakthrough time compared to the vertical and deviated wells. Sequel to the horizontal well selection, the two structural models from different seismic vintages required the optimization of the horizontal well path whose major constraint was the fixed surface location, and this led to the selection of Well path 3 with recovery of 0.97MMstb. In conclusion, heavy oil field development requires detailed integration of subsurface data to help maximize opportunity.
Assessing the Economic Impacts of Renewable Energy in Sub-Saharan Africa: A GDP-Centric Approach
Authors: Oyegbile M. O. , Emeke Chukwu , Eze M. C. 
Published: August 2024

Abstract
Sub-Saharan Africa, endowed with abundant solar, wind, hydro, and biomass resources, historically faced challenges in harnessing renewable energy due to infrastructural and financial limitations. Recently, the region has seen a surge in renewable energy adoption, driven by technological advancements, declining costs, and increased investments.
This research is driven by the need to quantitatively assess how renewable energy investments and advancements affect economic growth in Sub-Saharan Africa. The study acknowledges the region's unique economic and energy landscape, characterized by vast renewable resources, and aims to understand how harnessing these resources impacts Gross Domestic Product (GDP).
This research paper employs an in-depth analysis of economic data, renewable energy production statistics, and investment trends in Sub-Saharan Africa. Econometric models are utilized to establish correlations and causal relationships between renewable energy development and GDP growth. This approach includes examining the direct impact of renewable energy projects on local economies and the indirect effects through factors like job creation, energy security, and industrial productivity.
Key findings reveal that renewable energy has a positive and significant impact on the economic growth of Sub-Saharan African countries. Investments in renewable energy infrastructure not only provide immediate employment opportunities but also stimulate long-term economic activity by providing reliable and affordable energy. This energy accessibility enhances productivity in various sectors, from agriculture to manufacturing, contributing to GDP growth.
The study recommends policies and investments that integrate renewable energy development with economic objectives. This offers crucial insights for policymakers, underlining renewable energy as a key economic strategy, beyond its environmental and social benefits.
The Study of the Integration Between Pectin and Sodium Dodecyl-Sulfate on Oil Recovery
Authors: Musa M. S. M. , Adam N. H. B. , Gbonhinbor J. R. , Ridzuan N. , Mahat S. Q. A. , Yakasai F. , Ngouangna E. N. , Oseh J. O. , Agi A. 
Published: August 2024

Abstract
Surfactant flooding is a recognized technique within the realm of chemical enhanced oil recovery (CEOR), to enhance oil recovery in cases of continual depletion in conventional oil reserves. This method has a track record of effectively increasing oil recovery rates. The process involves multiple factors, including the reduction of interfacial tension (IFT), alterations in wettability, the creation of foam, and emulsification. Despite its success, a primary concern associated with surfactant flooding is the emergence of increased fingering effects, resulting in conformance issues like excessive water production. The primary objective of this research is to examine the interaction between pectin and sodium dodecyl sulfate (SDS) in the context of oil recovery, assessing the collaborative potential of pectin in conjunction with SDS to amplify oil production, and evaluating the impact of sodium chloride (NaCl) salinity on the behaviour of pectin and SDS in the context of oil recovery. To address water production concerns, this study employs polymer to augment water viscosity. The interaction between pectin and SDS will be scrutinized by observing three distinct sand packs, each containing surfactant, polymer, and a surfactant-polymer combination, respectively. The findings indicated significant improvements in oil recovery using different enhanced oil recovery (EOR) techniques. Freshwater flooding resulted in a 15% increase in oil recovery compared to saltwater flooding. Polymer flooding with a concentration of 0.6 wt% demonstrated the highest efficacy of 77.85% improvement in oil recovery compared to waterflooding. Surfactant flooding, while slightly less effective than polymer flooding, but still showed a remarkable 48.94% increase in oil recovery after waterflooding. However, surfactant flooding's effectiveness could be constrained by factors like reservoir characteristics and fluid properties. Despite this, the study revealed that combining surfactant and polymer flooding with the inclusion of pectin led to substantial gains, boosting oil recovery by 30% - 80% compared to using single polymer or surfactant flooding techniques.
Techno-Economic Analysis of Using Palm Oil Fuel Ash and Quartz for Plugging and Abandonment of Hydrocarbon Well
Authors: Margaret Monday Mary , Stephanie Nwosi-Anele Adaobi , Wopara Fidelis 
Published: August 2024

Abstract
There are large number of wells that need to be permanently plugged and Abandoned (P&A). As P&A operations do not provide any return on investment, developing cost-effective operations methods is very important. Therefore, it is essential to use cost-effective plug-and-abandonment technology without compromising the operation's scope. In this work, Quartz and Palm oil fuel ash (POFA) was used to reduce the cost associated with plugging and abandonment of hydrocarbon wells by reducing the settling time and improving the well integrity by minimizing the shrinkage associated with conventional cement. These materials were selected based on their properties, Quartz a thermodynamically stable material, enables the formulated solid to withstand temperature and not shrink with time despite the temperature and the force applied. The sieve size used for the Quartz was 300 microns and the total gram of 571.6g of the Quartz was added to the cement mixture. POFA possesses a pozzolanic property and a sieve size of 150 microns and 285.88 grams was used for the mixture with the mix ratio of M7 (1:4:8) and M25 (1:1: 2) A total gram of 143g of class G cement was added to the mixture, the water-cement ratio of 0.4 for the mixture of M7 and the water-cement ratio of 0.4 was use for M25. The rheological property and mechanical properties of the formulated slurry were analyzed and compared to that of Class G cement slurry. The Addition of the material (Quartz and POFA) to the class G cement increases the compressive strength from 85 psi to 182 psi and the setting time of 2:20min to reach 50 Bearden consistency. Economic Analysis using cost estimation method was done and the result shows that the cost associated with the usage of the formulated slurry was lower than the usage of class G cement for plugging and abandonment.