bluuebunny/crossref_metadata_2025 huggingface dataset:
Biohydrogen Production: A Review of Current Trends and Future Prospects
Authors: Umunnawuike C. , Mahat S. Q. A. , Ridzuan N. , Gbonhinbor J. , Agi A.
Published: August 2024

Growing concerns regarding climate change and fossil fuel depletion have spurred the development of clean and sustainable energy sources. Biohydrogen, a carbon-neutral fuel produced using biological processes, has emerged as a promising alternative to traditional fossil fuels. This review delves into the diverse landscape of biohydrogen production methods, encompassing light-dependent (photolysis, photofermentation) and light-independent (dark fermentation, microbial electrolysis) techniques. The study analyzes the advantages and limitations of each approach, exploring strategies for integrating these methods to enhance biohydrogen yield and optimize production efficiency. Additionally, the study discusses the critical role of various factors, including microbial selection, substrate utilization, and operational parameters, in maximizing hydrogen output. Furthermore, it assesses the potential of novel strategies like biomimetics and synthetic biology to further advance biohydrogen production. Finally, this study will showcase the economic considerations and challenges associated with large-scale biohydrogen production, outlining practical approaches for overcoming these hurdles and paving the way towards a sustainable future powered by biohydrogen.
Rheological and Filtration Property Enhancement of a Water-Based Drilling Mud Using Modified Graphene Nanoplatelets Derived from Kenaf Stem
Authors: Yahya M. N. , Norddin M. N. A. M , Ismail I. , Rasol A. A. A. , Oseh J. O. , Mohammed U. , Yakasai F. , Umunnawuike C. , Gbonhinbor J. , Agi A.
Published: August 2024

Drilling fluids’ rheological and filtration qualities have been discovered to be improved by graphene nanoplatelets (GNPs). Research has demonstrated that adding GNPs to water-based mud (WBM) can enhance its plastic viscosity (PV), yield point (YP), gel strength (GS), filtration loss (FL), and filter cake thickness (FCT). Their small particle size, excellent material dispersion, and thermal stability are the reasons behind the usage of GNPs in drilling fluid. Here, GNP from Kenaf stem was synthesized, and its surface was modified with nonionic surfactant triton-X100 to improve its dispersion in aqueous media. The synthesized nanomaterials were subjected to particle size distribution (PSD), energy dispersive X-ray analysis (EDX), and zeta potential analysis to confirm their elemental content, stability, and particle size. Critical analysis and a comparison of the properties of surface modified GNP (GNT) and synthesized GNP from Kenaf (GNK) were conducted in WBM formulations. The materials are stable, according to the zeta potential study, although the EDX results showed that surface modification reduced the oxygen-containing group in GNT. Better rheological properties were obtained by adding the synthesized materials to WBM as an additive. PV increased from 13.5 to 17cp, YP increased from 31 to 40 lb/100ft2, FL control performance improved from 12mL to 6.5mL, and FCT decreased from 2.60 to 1.20mm. It has been discovered that adding GNT to WBM enhances its rheological and filtration properties more effectively than adding GNK, which makes it a viable inclusion for drilling operations.
Revisiting Troubleshooted Drill Stem Test: Methodological Framework Incorporating Artificial Intelligence
Authors: Abbas A. H. , Serikov G. , Zhuniskenov Y. , Serikkali A. , Nyah F. , Ridzuan N. , Gbonhinbor J. , Agi A.
Published: August 2024

Drill Stem Testing (DST) plays a crucial role in the petroleum industry, particularly in understanding a formation's behavior under production conditions. DST is instrumental in identifying critical reservoir engineering parameters such as permeability, skin factor, anisotropy, and vertical connectivity. These techniques are crucial for understanding reservoir behavior, optimizing production strategies, and making informed decisions regarding reservoir development. Despite its significance, DST faces challenges, primarily relating to the accuracy and reliability of the data obtained. The integrity of the data collected during DST is crucial, as inaccurate data, possibly resulting from fluctuated readings, can lead to suboptimal field development decisions. Many software offers pressure tie options to avoid errors if the data is not precise and reliable. Yet, the challenge is in the selection of effective data processing and analysis. The current study offers a detailed evaluation of DST data, using Kappa Software and analytical models, to assess permeability, pressure, well performance, and reservoir characteristics. The study incorporates the potential integration of Artificial Intelligence (AI) to enhance pressure reports as a preprocess analysis tool. AI applications could revolutionize DST data interpretation by quickly processing large datasets, identifying patterns, and providing accurate reservoir estimates. The methodological framework offers time reduction and improved noise filtering which leads to enhanced predictive insights into reservoir behavior. The use of AI in DST analysis promises a better use of noisy and troubleshooted DST data.
Snail Shells as Sustainable Remediation Agents: A Novel Approach to Removing Heavy Metals from Produced Water in the Oil and Gas Industry
Authors: Alumonah J. A. , Emeke C. , Ehihamen P. E. , Igwegbe C. A. , Onyechi K.
Published: August 2024

Global environmental standards mandate Oil and Gas producers to treat Produced Water (PW) before disposal. This can pose an economic conundrum as PW's low economic value does not justify the high cost of treatment.

This study explores the use of the adsorption capabilities of Achatina fulica snail shell particles in eliminating heavy metal contaminants from PW thus providing a better alternative without compromising industry standards. Through comprehensive optimization using response surface methodology and central composite design, various adsorption parameters such as temperature, adsorbent dosage, contact time, pH, and lead concentration were examined on wastewater samples. Lead nitrate content was measured as a heavy metal contaminant. The snail shell adsorbent underwent thorough characterization employing Scanning Electron Microscope (SEM), Energy- Disperse X-ray (EDX) analyses, and Fourier Transform Infra-Red Spectroscopy (FTIR). Atomic Absorption Spectrophotometry was employed for heavy metal analysis. Response Surface Methodology was employed to evaluate the adsorption of lead (II) ions on the bioadsorbent.

The snail shell bioadsorbent exhibited a composition rich in various organic compounds and results indicated that lead ion removal positively correlated with increasing lead concentration and pH, inversely correlated with escalating snail shell dosage, and exhibited a slight decrease with contact time. Furthermore, optimal conditions for achieving a 99.3167% adsorption percentage of lead ions were identified. This study underscores the sustainable nature and efficacy of snail shells as a remarkable adsorbent for mitigating heavy metal contamination, particularly relevant to the challenges posed by heavy metal contamination in produced water in the oil and gas industry.
Investigating the Optimal Conditions for Heavy Metal Removal from Oilfield Wastewater by Activated Carbons Using Response Surface Methodology
Authors: Ogidi Samuel , Yusuf Mistura , AYODELE EMMANUEL , Aladeitan Yetunde , Sanni Khadijah
Published: August 2024

Oilfield wastewater, also known as produced water, presents an environmental concern due to its high concentration of heavy metals. Effective treatment strategies are necessary to address this issue. In the Niger Delta oilfields and refineries, produced water is a waste stream containing hydrocarbons, heavy metals, and other pollutants. This research focuses on utilizing Response Surface Methodology (RSM) to optimise metals’ adsorption process using activated carbon. Activated carbon is well known for its ability to adsorb various substances. It is being explored for its potential to reduce heavy metal pollutants from the wastewater of the Field X oilfield. To enhance the adsorption capacity, coconut husk-based activated carbon is modified with oxide, an emerging material derived from graphite using the "wet impregnation technique." A systematic experimental design is employed to analyze how various factors influence adsorption. The data obtained is then subjected to response surface methodology (RSM) using Design Expert software for analysis. This study considers factors such as adsorbent dosage, contact time and temperature. The significance and impact of these operating factors on the removal percentage (%) of pollutants will be tested using ANOVA (analysis of variance) with a correlation coefficient (R2) while maintaining a significance level of p < 0.05. The adsorption efficiency was determined by measuring final concentration, with a 93.59%, 99.01% and 96.38% reduction observed for Nickel, Cadmium and Lead respectively. The concentration values decreased from 0.6121 to 0.039 for Nickel, 0.9067 to 0.009 for Cadmium and 2.9044 to 0.104 for Lead. The findings from this study provide efficient approaches to tackle the environmental issues linked to heavy metal pollution in oilfield wastewater from field X. This will contribute towards promoting practices in the Nigerian oil and gas industry.
Effect of Oil Price on Carbon Dioxide Flooding of a Marginal Well
Authors: Ibe A. P. , Nwosi-Anele A. S.
Published: August 2024

The effects of oil price on the production of oil wells cannot be overstated especially in Nigeria where many wells are currently being termed marginal wells by the NUPRC and cannot be produced with primary recovery and soon enough even secondary recovery won't be enough to economically produce oil. This work aims to check the economic viability of CO2-enhanced oil recovery on marginal wells at different oil prices (current, high, and low). Using Eclipse software, CO2 injection in a marginal well was simulated. The resulting production data was put in an economical model at the three different oil prices and the breakeven oil price was found to be $48.59. A Monte Carlo risk analysis was carried out with 1000 iterations of randomly generated oil prices resulting in an average NPV of $364.84MM, the cash at risk of 95% resulted in $557.09MM and the probability of breaking even at the current fiscal regime and operating expenditure was 89%. A sensitivity analysis on the effect of certain parameters (Oil price, CO2 Cost, CAPEX, field OPEX) on the net present value was carried out to understand the components whose change would impact profitability the most, it was seen to be oil price. In conclusion, it was seen that while the oil price is the most crucial factor to be considered it is not the only factor, and as such cost-saving measures need to be applied in the field of OPEX. This work contributes to the existing work of the past by adding the use of CO2-enhanced oil recovery on a marginal well and checking its viability economically. However, further work can be done on this to improve the accuracy of the economic model.
Annular Velocity Effect on the Lifting of Sandstone Cutting Particles in Inclined Rotating Wellbore Pipes using Novel Hydroxyapatite Nanoparticles
Authors: Oseh J. O. , Norddin M. N. A. M. , Duru U. I. , Ridzuan N. , Onyejekwe I. A. , Odo J. E. , Ngouangna E. N. , Ismail I. , Gbadamosi A. O. , Yahya M. N. , Gbonhinbor J. , Agi A.
Published: August 2024

Deviated wellbore regions make cutting transport harder owing to the substantially low annular velocity distribution in these regions. This causes migrating cuttings to deposit on the lower borehole wall, creating cutting beds and thus resulting in drilling problems like pipe sticking. Water-based mud (WBM) that includes nanoparticles (NPs) to remove cuttings still needs further investigation due to the substantial drop in annular velocity of the mud in inclined boreholes. This research, therefore, employed a nanohydroxyapatite (nanoHAp) additive to improve the properties of WBM, including its cutting transport ratio (CTR). All the fluid performances of nanoHAp were compared with those of aluminium oxide NPs (nano-Al2O3). An inclined cutting rig simulator of 4.88 m long, 0.0609 m ID, and 0.0305 m OD was constructed. 0.4–2.0 g of nanoHAp and nano-Al2O3 were used to clean cutting particles that were 0.80–3.60 mm in diameter and at velocities of 1.5, 2.5, and 3.5 m/s and 120 rpm. The pipes were deviated at 40°, 50°, and 60°. The results indicate that nanoHAp particles are mostly 70–600 nm in size and thermally stable at 750 oC. Additionally, the fluid consistency index demonstrates that nanoHAp and nano-Al2O3 increased the viscosity of WBM of 2.114 Pa. s by 15 to 130% and 5.7 to 94.3%, respectively. Also, nano-HAp concentrations decreased the WBM fluid loss from 9.4 mL to 8.1–5.4 mL, and nano-Al2O3 concentrations decreased it to 8.9–8.2 mL. The CTR of all the fluid samples improves with an increase in concentration and annular velocity at all hole angles. NanoHAp increased the CTR by 10.0% to 31.4% at all concentrations, hole angles, and annular velocities, while nano-Al2O3increased it by 4.0% to 27.1%. A higher percentage of CTR was observed with pipe rotation compared to without pipe rotation. The drill pipe's orbiting motion and increasing axial velocity distribution have a positive effect on CTR. Compared with 40° and 60° deviations, 50° wellbore cuttings are the most difficult to clean, while 40° wellbore is the easiest. This research highlights the advantages of nanoHAp over industrial-based nano-Al2O3, offering options for selecting nanoHAp for drilling operations.
Strategies for Commercializing Compressed Natural Gas in Nigeria
Authors: Oruwari Humphrey , Obunwa Queen , Ahuchogu Joe
Published: August 2024

In this era of energy transition there is the need to commercialize the use of compressed natural gas (CNG) in Nigeria due to low carbon emission it generates. The widespread use of CNG as automotive fuel will serve as a transition fuel for sustainable energy transformation in Nigeria. Compressed Natural Gas (CNG) was first utilized as an automotive fuel by NNPC Ltd Gas Infrastructure Company (NGIC} a subsidiary of Nigerian National Petroleum Company Ltd under a pilot scheme over thirty (30) years ago. This scheme was part of NGIC's strategic efforts at promoting natural gas utilization and expanding the revenue base of the Company through commercialization of CNG. Despite NGIC's efforts, CNG is yet to be fully commercialized. The qualitative research method was adopted. Data for the study were collected through literature review and multiple case study of emerging countries that have promoted commercialization of CNG. Based on the analysis of the data and records, it was discovered that the target market for CNG is yet to be clearly defined. No economic incentive exists for vehicle conversion and establishment of CNG refueling stations. Cooperation between NNPC Gas Marketing Limited (NGML) and gas distribution companies which is critical in providing cost-effective gas infrastructures for CNG commercialization is nonexistent. In view of the above findings and conclusions, it is recommended that policy makers should: Promote the development of on-site refueling stations and/or locate public stations as close as possible to fleet vehicle owners. Partner with gas distribution companies through attractive revenue sharing (e.g., 50/50 split) as a strategy for promoting CNG market. Target fleet vehicles particularly those owned by existing gas users. Sell gas to CNG refueling station at a price lower than petrol price to encourage private investment in the station.
Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques
Authors: Isehunwa S. O. , Awojinrin G. T. , Oluwatayo P.
Published: August 2024

Traditional techniques, such as decline curve analysis (DCA) and material balance, are industry-accepted standards for evaluating reservoir performance and estimating developed reserves. However, while these methods are reliable for estimating well productivity under natural flow conditions, accurate evaluation of gas lifted wells can be challenging due to the additional energy injected to augment mthe natural flow. Although attempts have been made to model gas lifted wells using machine learning (ML) techniques, these models provide little or no interpretability, thereby offering no clues to the underlying physical interactions behind their predictions. To address this issue, an empirical correlation was developed to predict gas lifted oil production rates using interpretable non-linear regression ML models.

Production data from four wells were obtained from literature, encompassing production periods both with and without gas lift. A Python program was written to train the ML models. A ridge regression model was trained on production data from three of the wells using this program. The coefficients and intercepts of the resulting model were extracted to generate an empirical correlation and its predictive performance was then statistically analyzed.

The performance of the derived empirical correlation was validated using production data from a new well, exhibiting a correlation coefficient of 0.96 and a root mean squared error of 27.55 STB. Analysis of predicted production rates under both natural and gas lifted flow conditions indicated that proper utilization of gas lift can increase developed reserves under natural flow by as much as 200%. The predicted production life of the well also increased significantly from 28 months under natural flow to 48 months with gas lift.

A non-linear empirical correlation has been developed and tested to predict the oil production rate in gas lifted wells. With slight modifications, this correlation and the workflow implemented here can be applied to other wells, providing a practical tool for forecasting oil production rates not only in gas lifted scenarios but for other artificial lift methods as well.
Design and Execution of Deepwater Jetting Operations – A Correlative Design Approach
Authors: Sakpa Ogaga , Igbeka Uche , Ojubanire Damilola , Okoli Ugo’ , Okwa Hope
Published: August 2024

Conductor jetting operations in deep water are vital in offshore drilling to securely install conductors and prevent subsidence. This paper details the meticulous procedures employed to ensure the successful jetting of conductors to the seabed for a deep water well in the offshore region of the Niger Delta in West Africa.

With meticulous planning, considering factors such as seabed composition, water depth, and conductor specifications. There were no advanced simulation tools to aid in predicting the ideal jetting parameters, thus the methodology employed in this study relies on non-site-specific data, calibrated through information/data obtained from the Gulf of Mexico.

During the operation, precise control and monitoring of jetting parameters are pivotal. Continuous monitoring of penetration depth, fluid pressure, and soil response facilitates real-time adjustments to optimize the jetting process. The application of appropriate back pressure assists in creating the requisite skin of friction, ensuring secure placement of the conductor.

Post-installation assessments, which include the confirmation of conductor alignment and embedment, are conducted to verify the integrity of the installed conductor. Additionally, long-term monitoring systems are often implemented to track subsidence and ensure the stability of the conductor installation over time.

This paper details the comprehensive procedures and stringent protocols integral to conductor jetting operations in deep water. The meticulous planning, precise execution, and post-installation assessments collectively contribute to ensuring the successful installation of conductors while mitigating the risks of subsidence, in challenging deep-water environments.

It also confirms that with the use of the right data set and correlation, it is possible to produce an efficient design.
Cost Benefit Analysis of Solar Panel System in Residential Buildings in Sokoto State, Nigeria
Authors: Stephen S. O. , Ataundaolu O. E. , Ikirigbe O.
Published: August 2024

In Nigeria, the current state of inadequate power supply necessitates the search for alternative energy sources. The urgency lies in finding a solution that is both clean and renewable, promoting green and sustainable development. This study focuses on evaluating the cost benefit of solar energy for residential buildings in Sokoto State, Nigeria. To assess the feasibility of solar energy, global solar radiation, clearness index, and sunshine hours based on analyses of weather conditions in Sokoto State were considered in the analyses. Notably, August exhibited the lowest solar radiation levels.

Quantitative analysis was conducted to ascertain the viability of solar panel systems as an energy solution for residential buildings in Sokoto State. Over a projected 25-year lifespan of the solar panels, the cumulative output production is estimated at 54.54 terra-watt hours. The return on investment (ROI) is calculated at an impressive 209%, indicating a payback period of just 9-10 years. Additionally, compared to other renewable energy sources, solar energy in residential buildings in Sokoto State stands to save more money, with a revenue of $3.55 billion. These findings highlight the significant potential and economic benefits of adopting solar power in Sokoto State. By embracing renewable energy solutions, Nigeria can address its pressing power supply challenges while fostering a sustainable and environmentally friendly future.
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

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

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

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

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

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

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

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

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

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

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>D>GR>VSh>RHOB>NPHI>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

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.
Optimisation Techniques for Reduction of Enviromental Emissions from the Energy Industry
Authors: Iwegbu M. C. , Ugolo J. U. , Onwochei A. , Osokogwu U.
Published: August 2024

With the 2023 United Nations Climate change conference focus on transitioning to clean energy and theme on fast tracking energy transition and cutting emissions before 2030 to limit global warming to 1.5 0C above pre-industrial levels, it has become imperative especially for developing countries requiring more and more energy for their developmental needs to strike a balance between providing basic energy infrastructure for her economic development and the obligation for these developing countries to make commitments, set targets including adjusting national legislation in order to cope with increasing global environmental pressure calling for the reduction of environmental emissions and subsequently build climate resilience.

This paper examined the possible optimization techniques for reducing environmental emissions of a typical minigrid power plant by carrying out a technical and economic simulation of an existing 85KW solar based renewable energy minigrid project in Nigeria with the objective of providing the cheapest energy cost along with zero or minimum environmental emissions.

HOMER pro software was used for the simulation using Solar PV (PV), Storage Battery (B), Diesel Generator (DG) and Converter as the base case scenario; also, other alternative sources of fuel scenarios were also modelled using Liquefied Petroleum Gas (LPG), Compressed Natural Gas (CNG). The results showed that the Solar PV + BAT + LPG scenario using LPG as an alternative source of fuel reduced the base case scenario C02 emissions by 8% while the Solar PV + BAT + CNG scenario using CNG as an alternative source of fuel reduced the base case scenario C02 emissions by 13.5%.

This paper proposes the following optimization techniques for minigrid power projects in Nigeria in order to achieve the least environmental emissions targets by adopting the following; increase in the renewable energy fraction by further adopting any existing local renewable energy resource, switching from higher to lower carbon emitting sources of fuel, use of lower emission generating devices, optimization of the stored battery power and consequently a reduction in the running hours required by the backup power source to support the intermittent renewable energy based minigrids.
Combating Pipeline Internal Corrosion Using Jatropha Tanjorensis
Authors: Ngwu Daniel , Toyin Odutola
Published: August 2024

This study explores the corrosion inhibition potential of Jatropha tanjorensis on aluminum (Al), mild steel (MS), and galvanized steel (GA) specimens immersed in a corrosive crude oil medium. Twelve specimens, divided into 4 groups for each metal type, were suspended in the corrosive medium using a unique setup involving plastic containers and wooden sticks. The inhibitor, extracted from 100g of Jatropha tanjorensis leaves in 400ml of ethanol, resulted in a 0.25M concentration solution. The MS, Al, and GA specimens were exposed to a control solution containing 100ml of crude oil with 1M H2SO4. Three concentrations (4ml, 6ml, and 8ml) of the Jatropha tanjorensis extract were added to separate cylinders, and corrosive crude oil with H2SO4 was introduced, following a gravimetric method for weight loss calculations. Results over 120 hours indicated poor inhibitor performance on aluminum, with increasing weight loss. Conversely, galvanized steel and mild steel showed minimal weight loss at all concentrations for the initial 48 hours, with a slight increase thereafter. Notably, the 6ml concentration of inhibitor exhibited the best overall corrosion inhibition and an optimal effectiveness in inhibiting corrosion of galvanized steel by 85.38%, and mild steel by 91.91%. This research contributes insights into combating internal corrosion in pipelines, particularly in the context of the oil and gas industry, and suggests avenues for further studies to refine corrosion inhibition strategies