bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the Impact of Fault Seal on Hydrocarbon Accumulation: The Result of a Blind Well Test, Onshore Niger Delta
Authors: Tochukwu Uzoma Prosper , Booth Jacqueline , Uzoma Kelechi , Murry Titus , Abidoye Lanre , Macaulay Peter , Kobiowu Olaitan 
Published: August 2024

Abstract
Faults create one or more lateral trap seals across mukiple Niger Delta oil and gas fields. An intrinsic uncertainty exists over a fault's sealing capacity and leak point elevation over stratigraphy and fault geometry. Recognising that a single deterministic prediction will not allow for the modelling of these uncertainties is essential. To overcome this problem, Stochastic fault and trap analysis can be used to model the parameter uncertainties and thus derive the probability of trapping hydrocarbons and the distributions of hydrocarbon column heights within a reservoir. Our modelling approach provides an estimation of the fault's juxtaposition sealing capacity. A blind well evaluation has been used to assess the effectiveness of our stochastic modelling approach to investigate the error between the predicted and observed column heights and fluid contacts. The evaluation was conducted on a well (Well AA-1) with known fluid contacts and hydrocarbon column heights. The forecasting of hydrocarbon columns was based on structural (fault polygons and regional-scale grids) and stratigraphic data from Well AA-1. This well situated onshore Niger Delta encountered 25 hydrocarbon-bearing reservoirs with an average hydrocarbon column height of 149ft. Of the 25 reservoirs encountered by this well, only 4 penetrated hydrocarbon water contacts (HWC) while 21 penetrated hydrocarbon down-to (HDT). A comparison of the P50 forecast of column heights and the drilled column heights seen by the AA-1 Well suggests that of the four reservoirs which encountered HWC, two were within 12% of the observed depth, while the other two were slightly overpredicted. Juxtaposition analysis outperforms the SGR approach in forecasting 21 reservoirs with HDTs; simulation results suggest that ~86% of the reservoirs have their predicted contacts significantly deeper than the drilled HDTs suggesting thicker columns. The blind well test demonstrates that the stochastic fault seal juxtaposition approach better predicts the hydrocarbon-water contacts while significant departure of the post-drill results from the pre-drill predictions are usually attributable to nonfault related factors such as the effects of stratigraphy.
Exploring Diverse Funding Mechanisms for Oil and Gas Companies in Nigeria: An In-Depth Analysis with Practical Case Studies
Authors: Umoh Gabriel , Etim Ntietemi , Oko Christopher 
Published: August 2024

Abstract
The petroleum industry is one of the largest, significant, and most complicated global industries in the world. Despite the numerous crises experienced in the oil and gas industry, companies continue to invest money in new projects for the development of hydrocarbon resources. The industry has incredible opportunities yet untapped. However, several factors such as a lack of investment resources hamper the dynamic development of the industry. Sources of financing are needed to solve this problem. Sourcing for funds could be very frustrating, and time-consuming, especially in times of economic recession where bank loans could be at a provocative interest rate. Several studies have explored and recommended conventional and alternative funding options for oil and gas companies in Nigeria, however, no study has comprehensively presented the various alternative funding options with practical examples as applicable in the Nigerian oil and gas industry. This paper conducted a comprehensive review of various funding options available for projects in the hydrocarbon industry with mentions of successful project funding case studies from the Nigerian landscape. By examining case studies of successful funding initiatives by certain oil and gas companies in Nigeria, in-depth studies could be done to identify the key factors contributing to their success. This would serve as a roadmap for other companies, specifically indigenous marginal field operators.
Property Aggregate Approach for Hydrocarbon Prediction Using Integrated 3D Seismic and Well log Data
Authors: Daniel Ayodere , Mode Ayonma Wilfred , Igbafe Anselm Iuebego , Adesmia Olusola Auledayo 
Published: August 2024

Abstract
This paper presents a preliminary study using the property aggregate approach (PAA) to predict the presence of hydrocarbons in a seismic volume. Seismic attributes are measures generated by performing one or more operations on seismic data. The PAA was extended to Well logs in order to improve their interpretation value and this yielded the Bulk Volume Hydrocarbon(BVH). The BVH is a synergistic aggregate of density, resistivity and gamma ray log properties which are generally used singly in several steps to determine the reservoir characteristics of a formation. Furthermore, the effective medium theory for mixing various constituents of rock was used to expand the acoustic impedance equation. The BVH was found to have a quadratic relationship with acoustic impedance thus making the integration of well data and 3D seismic data amenable to genetic inversion using Petrel. Inversion of the 3D acoustic impedance yielded a BVH volume which clearly showed zones of BVH. Zones of BVH greater than 0.15 are interpreted as highly commercial reservoir with viable prospects and guides for optimal well placement.
Application of Palm Oil Mill Effluent and Palm Bunch as Emulsion Chemicals in Oil Reservoir Flooding Operation
Authors: Ndem D. O. , Onwukwe S. I. 
Published: August 2024

Abstract
Oil reservoir flooding is a widely employed method to enhance oil recovery from depleted fields. However, conventional flooding techniques often encounter challenges related to environmental concerns and cost-effectiveness. In recent years, there has been growing interest in utilizing sustainable and eco-friendly materials for enhanced oil recovery processes
This research focused on the potential utilization of two abundant agro-waste by-products from the palm oil mill industry, namely palm oil mill effluent (POME) and ash derived from burnt empty palm fruit bunches (EPFB). Oil/grease/water was extracted from palm oil mill effluent using a centrifuge; surfactant solution was prepared with ash from burnt empty fruit bunch and distilled water. Oil/surfactant emulsions were formulated using different ratios of oil/surfactant ratio (10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20 and 90/10). The formulated emulsion was analyzed to evaluate its stability and compatibility with reservoir formation water.
Nine Core samples of 10cm x 8cm x 6cm dimensions were initialized at reservoir conditions by flooding them with brine of salinity of 15000ppm until the differential pressure became stabilized. The brine was produced till pressure stabilized, and the original oil in place was determined by flooding the brine-saturated core samples with crude oil (32.9 API). Then the crude oil-saturated core samples were firstly flooded with water till oil production ceased, and oil production volume was observed. Secondly, to produce the residual oil in the previously water-flooded core samples, the core samples were respectively flooded with the formulated emulsions of different oil/surfactant solution ratios. The results revealed that water flooding yielded 32.5% of the oil in the respective core samples while different ratios of formulated emulsion (10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20 and 90/10) yielded incremental oil recovery of 31.3%, 30.5%, 30.9%, 29.3%, 29.1%, 30.6%, 30.7%, 31.0%, 31.2% respectively. These brought the total oil recovery for both water flooding and formulated emulsions operation from the core nine core samples to 70.5%, 69.4%, 68.8%, 68.7%, 69.1%, 69.5%, 69.9%, and 70.2%, respectively. The experiments suggest that certain emulsion compositions enhance the mobilization and displacement of oil, leading to better reservoir sweep efficiency. It was found that certain ratios of oil-in-surfactant solution, and surfactant solution-in-oil emulsions yield higher recoveries.
Integration of Hydrothermal Liquefaction of Algae to Biofuel Production
Authors: Kelechi F. M. , Aribisala A. A. 
Published: August 2024

Abstract
Hydro Thermal Liquefaction (HTL) is a transformative process capable of converting algal biomass into four distinct phases: biocrude, gaseous, aqueous products, solid residue. Algal biofuel is regarded as the promising "third" generation biofuel, with three primary routes for liquid biofuel production from algae: biodiesel extraction or transesterification, bio-oil through pyrolysis, and biocrude via hydrothermal liquefaction (HTL). Both pyrolysis and HTL fall under the umbrella of thermochemical liquefaction technologies. HTL, specifically, involves the direct liquefaction of algal biomass into biocrude oil within a closed, oxygen-free reactor. This process utilizes pressurized inert gases like N2 or He, or reducing gases such as H2 or CO, at temperatures ranging from 250 to 380°C and pressures from 5 to 28 MPa. Notably, HTL employs hot compressed water, functioning as both a solvent and a reaction medium, with the advantage of being near-critical water, which is abundant, non-toxic, non-flammable, cost-effective, and naturally present in biomass. The paper further considers key challenge of HTL with organic solvents, which is the relative high cost, the use of hot compressed water which offers significant advantages. HTL with hot compressed water eliminates the need for an expensive solvent, and it has the flexibility to process wet algal feedstock directly, as the total solids (TS) content in the feedstock typically ranges from 10 to 25%. Furthermore, HTL enables the conversion of the entire algal composition, including lipids, proteins, and carbohydrates, resulting in a higher biocrude yield. Unlike other liquid biofuel production technologies such as oil extraction or pyrolysis, HTL offers distinct benefits: elimination of the drying process, broader feedstock applicability, enhanced mass transfer facilitated by sub-/super-critical water acting as both a reaction medium and solvent, and improved energy efficiency due to reduced latent loss during phase change.
Thermochemical Conversion of Microalgae: Challenges and Prospective of HTL Pathway for Algae Biorefinery
Authors: Kelechi F. M. , Aribisala A. A. 
Published: August 2024

Abstract
Hydro Thermal Liquefaction (HTL) emerges as a promising method for converting wet biomass into liquid fuels. However, additional processing of the resulting HTL biocrude is imperative. Elevated levels of oxygen and nitrogen in HTL-produced biocrude necessitate deoxygenation and denitrogenation before it can be effectively used as a transport fuel. Managing the by-product aqueous stream is crucial for the success of an algal biorefinery employing HTL. Consequently, maximizing HTL efficiency and optimizing the utilization of biocrude and co-products, especially aqueous by-products, are current research priorities in biorefinery studies. To boost HTL efficiency, the focus is on using only carbon and hydrogen for hydrocarbon liquid fuels, as the presence of oxygen and nitrogen is undesirable for oil applications. Oxygen lacks inherent heating value, and nitrogen, if combusted, contributes to environmental pollution. Hence, HTL involves concurrent deoxygenation and denitrogenation during biocrude formation. The primary role of HTL lies in sustainable energy and chemicals production, aligning with a commitment to environmental preservation. Biocrude, characterized by high oxygen and nitrogen contents, along with elevated molecular weight and viscosity, typically undergoes upgrading processes like solvent extraction/distillation, hydrogenation/hydrodeoxygenation, catalytic cracking, esterification, and hybrid techniques. These processes lead to the partial removal of oxygen as CO2 or H2O and the conversion of nitrogen into ammonium. The study explores potential routes for the thermochemical conversion of microalgae, distinguishing between dry processes (pyrolysis and gasification) and wet processes (near-critical water hydrothermal liquefaction and hydrothermal gasification). The work identifies key engineering advantages and challenges, focusing on biofuel production for transportation. The future perspectives for each route are presented.
Machine Learning Approach to Oil Spill Detection
Authors: Kelechi F. M. , Aribisala A. A. 
Published: August 2024

Abstract
In recent years, environmental scientists committed to safeguarding the planet have prioritized the detection and monitoring of oil spills in marine waters, a focus expected to persist. Rapid identification of oil spill incidents on the water's surface is crucial for timely monitoring and cleanup efforts, especially to protect the delicate ecology, particularly marine life. Delayed or inefficient response to oil spills exacerbates the adverse impact on marine ecosystems over time. In cases of oil spills in marine systems, swift identification and monitoring facilitate precise cleanup and recovery of hydrocarbons on the water surface. This, in turn, contributes to the preservation of both the marine ecosystem and human lives. The integration of artificial intelligence (AI) in the detection and monitoring of oil spill incidents in aquatic environments holds promise for enhancing the response process to such events. This paper aims to investigate and evaluate the feasibility of employing AI techniques, such as machine learning (ML) and deep learning (DL), to expedite the cleanup and other response operations related to oil spills over water surfaces.
Petroleum Industry Act 2021 and Natural Gas Development in Nigeria: Matters Arising
Authors: Malami Mohammed , Nwosi-Anele A. S. , Iledare O. 
Published: August 2024

Abstract
Until recent times, gas utilization in Nigeria has been low-key. The need for transiting to a cleaner source of energy has made natural gas a desired source of energy because it burns cleaner than fossil fuel. To fulfill the need for energy transition in Nigeria, National leaders and policymakers have joined the global accord towards finding cleaner energy sources to address the imminent dangers of climate change and ensuring environmental sustainability. Natural gas is considered a cleaner energy source than the rest of the fossil fuels and has the potential to occupy a major share in the global energy mix of the future. Nigeria is endowed with vast Natural Gas resources, with an estimated 2P reserve of circa 208.62 Tcf (as of 1/1/2022).
Over the years, production of natural gas in Nigeria has been hovering around (6.5 to 8.5 bcf/d). Out of these volumes, only around 15% (i.e. 975 to 1,275 MMscfd) supplies to the domestic market. At this rate, the domestic utilization of natural gas in Nigeria is very low compared to the existing demand and opportunities for gas in Nigeria. In an effort to promote domestic gas utilization, the Federal Government has over the years developed many laudable programmes and policy interventions with a view to stimulating the growth of the sector. Prominent among these is the recent declaration of the Decade of Gas (DoG - 2021); the National Gas Expansion Programme (NGEP - 2020); Nigerian Gas Flare Commercialization Programme (NGFCP – 2018) and National Gas Policy (NGP – 2017). Similarly, the Federal Government of Nigeria via the Central Bank recently rolled out a financing programme tagged "Gas Intervention Funds" to the tune of NGN 250 billion to support the development of the gas sector in the country.
This paper seeks to discuss the vast natural gas resources in Nigeria vis-à-vis the numerous government interventions with a view to identifying and unlocking the opportunities available in harnessing the potential of the gas sector in Nigeria. Furthermore, the paper shall also outline the threats militating against the realisation of these potentials and proffer some recommendations.
Mitigating the Risks of Natural Fractures During Drill Cuttings/Produced Water Reinjection in Conventional Wells
Authors: Nwonodi R. I. , Dosunmu A. , Okoro E. E. 
Published: August 2024

Abstract
Drilled Cuttings Reinjection (DCRI) is crucial in addressing environmental pollution and achieving a circular economy. However, the success of DCRI is hampered by the risks and uncertainties associated with unplanned natural fractures (NF). These NFs result from tectonic activities, which distribute them predominantly, particularly between South America and the Niger Delta. Consequently, slurry leakage via unplanned NFs poses a nontrivial challenge during DCRI. Developing practical steps to mitigate the risks of the NFs is a welcome development. Thus, this study presents a model that enables the operators to predict the density of NF (NFD) to mitigate the risk of slurry leakages. The authors combined geomechanical modelling with fracture mechanics to predict the NFD and used a vertical well in the Passive Continental Margin of the Gulf Coast for evaluation. The results suggest that the operators need to avoid high porosity intervals to mitigate the risks of NFs during DCRI. Such intervals possess lower compressive strength and tend to open up, enhancing slurry leakage. The NFD is higher around the wellbore than away; the near wellbore is a high-risk region. Consequently, the authors suggest the addition of siloxanes into the slurry to seal off high-porosity intervals, due to their chemical properties, including water-repelling capacity, unusual stability over a wide temperature range, low toxicity, and production of silicon carbide (SC) under inert combustion. SC has a high melting point of up to 2830°C, which can withstand high-temperature injections. However, the slurry need not contain alkali solution or molten iron to prevent SC solubility. Furthermore, a higher Poisson's ratio yields a higher NFD, and the decrease in slurry temperature leads to an increase in the NFD. Thus, the authors recommended that the operators should inject deeper into the well, strengthen the near wellbore region, ensure thermal equilibrium, and minimize vibrations in the wellbore.
Machine Learning Approach for Reservoir Petrophysical Properties Prediction from Well-Logs Data in the Niger Delta
Authors: Eyo Antigha Effiong , Okon Anietie Ndarake , Tugwell Kilaliba Wanaemi 
Published: August 2024

Abstract
In this study, machine learning (ML) models were developed to predict permeability (k), porosity (φ) and water saturation (Sw) using 1241 datasets obtained from well-logs data in the Niger Delta. The datasets were screened to remove incomplete sets and outliers and make them suitable for adequate training using the maximum-minimum normalization approach. Three multiple-input multiple-output (MIMO) machine learning methods, namely artificial neural network (ANN), decision tree (DT) and random forest (RF), were used to train the datasets. Five performance metrics, coefficient of determination (R2), correlation coefficient (R), mean absolute error (MAE), average absolute relative error (AARE), and average relative error (ARE), were used to evaluate the performance of the developed models. The results indicate that the MIMO neural-based model had overall MSE and R values of 1.9801×10-3 and 0.9866, while the DT model had 2.2540×10-3 and 0.98281, and the RF model had 5.1490×10-3 and 0.95989. The ANN model predicted k resulted in R2, R, MAE, ARE, and AARE of 0.95740, 0.97847, 2.0677, -0.0011, and 0.0343, respectively, while the predicted φ had R2 of 0.96336, R of 0.98151, MAE of 0.0055, ARE of -0.0006, and AARE of 0.0185. The predicted Sw had an R2 of 0.98430, R of 0.99212, MAE of 0.0265, ARE of -0.0045, and AARE of 0.0521. Also, the developed DT model predicted k resulted in R2, R, MAE, ARE and AARE of 0.95250, 0.97596, 0.0277, 5.6981 and 0.0382, respectively, while the predicted φ had R2 of 0.9380, R of 0.9685, MAE of 0.0276, ARE of -0.5796 and AARE of 5.8199. The predicted Sw had R2 of 0.99039, R of 0.9518, MAE of 0.0182, ARE of -0.49969 and AARE of 5.0452. Furthermore, the developed RF model predicted k resulted in R2, R, MAE, ARE, and AARE of 0.88438, 0.94041, 0.0552, -6.8754 and 15.8391, respectively, while the predicted φ had R2 of 0.90377, R of 0.95067, MAE of 0.0504, ARE of -5.3429 and AARE of 12.8260. The predicted Sw had R2 of 0.95495, R of 0.97722, MAE of 0.0469, ARE of -25.1422 and AARE of 32.6698. The relative importance of the ML input parameters on the predicted outputs is RES&gt;D&gt;GR&gt;VSh&gt;RHOB&gt;NPHI&gt;CALI. Based on the statistical indicators obtained, the predictions of the developed ML-based models were close to the actual field datasets. Thus, the ML-based models should be used as tools for predicting k, φ and Sw in the Niger Delta.
Geothermal Energy Assessment in Africa a Pathway to Renewable Energy
Authors: Akagbosu P. I. , Areola J. J. , Ofi A. , Odofin D. 
Published: August 2024

Abstract
Geothermal energy stands as a promising renewable resource with vast untapped potential. This paper presents a comprehensive review of geothermal resource assessment by integrating petrophysical insights and machine learning (ML) for enhanced reservoir characterization for predictive modelling for economic feasibility assessments. The examination of global geothermal hotspots, considering thermal attributes alongside porosity and permeability characteristics, contributes to understanding the efficiency and feasibility of geothermal reservoirs for future sustainable energy initiatives. The exploration of geothermal reservoirs plays a pivotal role in harnessing energy.
Through analysis of various geological surveys, reservoir modelling, and advanced exploration technologies aimed to provide insights into the estimation and characterization of geothermal reservoirs. this paper explains the prospects and challenges associated with harnessing this clean, reliable, and renewable energy source for future energy sustainability in Africa.
This Paper shows illustration of reservoir characterization and modelling of two wells in west Africa to investigates Potential for the sustainable exploitation of geothermal energy, encompassing enhanced geothermal systems (EGS), coupled with economic feasibility assessments.