International Journal of Oil, Gas and Coal Technology (20 papers in press)

Regular Issues

• Biodiesel production from third-generation feedstock: process parameter modelling and optimisation using RSM-ANN approach  
  by Aqeel Ahmad, Ashok Kumar Yadav, Shifa Hasan 
  Abstract: This study employed response surface methodology (RSM) coupled with a central composite design (CCD) approach to ascertain the optimal conditions for biodiesel production from Neochloris oleoabundans microalgae oil. Four key process variables, including the methanol-to-oil molar ratio, catalyst concentration, reaction time, and temperature, were investigated across five levels to develop an L30 orthogonal array for experimentation. An artificial neural network (ANN)-based prediction model was developed using the experimentally obtained data, yielding high accuracy with mean square error (MSE) values of 0.019, 2.4327, and 0.8269 and coefficient of determination (R2 ) values of 0.9996, 0.9796, and 0.9890 for training, validation, and testing sets, respectively, indicating robust predictive capability. The optimisation analysis reveals a biodiesel yield of 94.94% under optimised conditions: 6.92:1 molar ratio, 1.22% catalyst concentration, 64.36 min reaction time, and 56.46 C temperature. Experimental validation confirmed the reliability of the optimisation results, demonstrating a marginal error of 2%. [Received: November 25, 2022; Accepted: April 30, 2024]
  Keywords: biodiesel production; Neochloris oleoabundans; response surface methodology; RSM; artificial neural network; ANN; sustainable fuel; central composite design; CCD; mean square error; MSE.
  
  
• Experimental study on prediction of gas pressure variation during coal and gas outburst  
  by Erhui Zhang, Xukai Dong, Baokun Zhou, Lei Yang 
  Abstract: To accurately predict the variation of gas pressure during the coal and gas outburst experiment, a gas pressure prediction model was established based on Keras and long short-term memory (LSTM). Meanwhile, the ARMA and ARIMA models were selected for comparative analysis. The findings reveal that the ARMA model exhibits the shortest prediction time, but its RMSE and MAE values are the largest, suggesting that the ARMA model yields the poorest predictive performance. The LSTM model achieved the lowest RMSE, and its MAE closely approached that of ARIMA, but the ARIMA model could only predict the gas pressure in the short term. Therefore, it can be employed as the prediction model for gas pressure in coal and gas outburst experiments. The research findings offer significant auxiliary support for predicting the change of gas pressure during coal and gas outbursts, thereby facilitating further prediction and prevention of such occurrences. [Received: October 9, 2023; Accepted: February 27, 2024]
  Keywords: coal and gas outburst; gas pressure prediction; LSTM neural network; multistep prediction.
  
  
• Application of pigging track approach in gas-exhausting for gas-liquid two-phase flow in undulating pipelines  
  by Sihang Chen, Gong Jing, Yang Qi 
  Abstract: During the gas-liquid replacement process of the oil undulating pipeline, there are liquid single-phase parts, gas single-phase parts and liquid gas two phase parts appearing alternately, which pose challenges to the pig tracking and gas-exhaust simulation. In this paper, a novel approach to exhaust the gas from the pipe applying the pigging tech is proposed, and the complicated motion of pig in the undulating pipeline is described by the self adapting momentum equations based on the hydraulic analysis during operation process. The analysis focus on the changeable pressure and force condition happen to the pig with its moving along the pipeline, and switch the describe-equation to the pigs automatically. The model validation by the field data of a real pipeline in China shows that the relative deviation of pig real time speed is within 0.5%, and the relative deviation of pig position is within 2.2%. [Received: October 11, 2023; Accepted: March 7, 2024]
  Keywords: pigging process; undulating pipelines; gas-liquid two-phase flow; pig-tracking; gas exhausting.
  
  
• A study on the transient permeability behaviour of coal core under confinement  
  by Arpita Roy, Santanu Bhowmik, Pratik Dutta 
  Abstract: A high-volatile bituminous coal core was maintained at 40 C, for flooding separately with helium, methane, and carbon dioxide in steps, up to 6 MPa and under various confining pressures (8.2 MPa, 9.2 MPa, and 10.2 MPa). Time-variations and correlation among the flow parameters were analysed during the non-steady state flow. Changes in the effective stress and the specific pore volume were significant at higher confining pressure but less/negligible at lower confinement pressure. Permeability varied inversely with gas pressure within a pressure step but as pore pressure was decreased, the permeability was found to decrease. Permeability also varied with the volumetric flow rate, whereas negative trends were observed with effective stress and cleat compressibility. Effective stress dominated the gas permeability significantly and directly, but indirectly affected specific pore volume and cleat compressibility. In general, helium and CO2 permeability were observed to be the lowest and highest among the gases, respectively. Helium, methane, and CO2 permeability in coal are tested at different effective stresses. Changes in permeability and important parameters with time are investigated. [Received: September 19, 2023; Accepted: February 29, 2024]
  Keywords: pore pressure; confining pressure; effective stress; gas permeability; cleat compressibility; Indian coal.
  DOI: 10.1504/IJOGCT.2025.10066449
   
  
• Investigating a machine learning algorithms applicability for simulating the apparent viscosity of waxy crude oil in a pipeline  
  by Andaç Batur Çolak 
  Abstract: Accurately estimating the formation of viscosity is a vital aspect of pipeline functioning. A study was undertaken here to investigate the precision of utilising a machine learning system for predicting the viscosity of waxy oil in a pipeline environment. A neural network model was developed to ascertain the viscosity of waxy crude oil based on a collection of eight independent parameters. The network model, derived from 30 experimental data points, consists of a hidden layer including 14 neurons. An accuracy analysis was conducted by comparing the predicted viscosity of the network model to the experimental viscosity. The model was built via the Levenberg-Marquardt training algorithm. The accuracy of the artificial neural networks predictions was assessed by calculating the mean squared error value of 2.75 x 10-3 and the correlation coefficient of 0.99850. The models anticipated viscosity values had an average deviation of 0.5%. The experiment yielded conclusive evidence that the specifically engineered artificial neural network successfully forecasted the viscosity of waxy crude oil within the pipeline with great precision. [Received: November 15, 2023; Accepted: May 9, 2024]
  Keywords: crude oil; pipeline; wax; viscosity; machine learning.
  
  
• Simulation and analysis of natural ice-making based on gravity heat pipes  
  by Xiaohong Gui, Shengwei Wang, Junhui Huang, Ziqiang Zhu, Chengyang Zhao 
  Abstract: The emerging natural ice-making technology has garnered significant attention due to its potential to utilise natural cold sources for reducing mine temperatures. This paper suggests employing gravity-based heat pipe cooling technology and employs FLUENT to simulate the heat transfer process in heat pipes and the natural ice-making phenomenon. Research findings reveal that the devised single-tube ice-making model operates without extra energy consumption and effectively produces ice by relying solely on the temperature variance between the water and its surrounding environment. At three varying temperatures (262.15 K, 266.15 K, 270.15 K), the rate and thickness of ice formation increase inversely proportional to the temperature decline, indicating a negative correlation. Moreover, with higher inlet wind speeds (4 m/s, 7 m/s, 10 m/s), the rate and thickness of ice formation increase, showcasing a positive correlation. Finally, the heat pipe structure equipped with fins in the condensation section can partially expedite the ice formation rate and augment the ice thickness. These research findings are of substantial significance in mitigating high-temperature heat hazards in mining environments. [Received: June 2, 2023; Accepted: December 12, 2023]
  Keywords: mine heat damage; ice production; gravity heat pipe; numerical simulation; natural cold source.
  
  
• Development of coal quality exploration technique based on convolutional neural network and hyperspectral imaging  
  by Swati Hira, Manoj B. Chandak, Devendra Kumar Sakhre, Lalit Kumar Sahoo 
  Abstract: Coal is Indias prime energy source, contributing about 60% of total electricity production. Coal India, a major coal-producing public sector unit, has produced record 703.2 million tons of coal during the year 20222023. Therefore, this paper proposes an idea of instant prediction of coal quality parameters using hyperspectral imaging and deep neural network. We have collected coal samples from 35 different coal mines of all areas of Western Coalfields Ltd (WCL), and 257 different types of samples have been generated. All 257 coal samples were imaged using camera PIKA NIR 320. The RegNet model was applied to predict coal quality based on moisture, ash, volatile matter, gross calorific value, fixed carbon, and sulphur. The results were validated through chemical analysis results received from the lab. The proposed approach achieved good prediction accuracy, nearly 96% for coal quality parameters. Moisture showed the highest accuracy, 96.09% in quality prediction. [Received: October 25, 2023; Accepted: April 14, 2024]
  Keywords: coal quality parameters; hyperspectral imaging; HSI; deep learning; spectral data; spatial data; PIKA NIR-320.
  
  
• Study of in-cylinder mixing performance of gas-fuel engine under PFI-DI hybrid injection condition  
  by Tianbo Wang, Yu Wang, Jing Chen, Lanchun Zhang, Li Li, Yanyun Sun 
  Abstract: To further explore the possibility of integrating port fuel injection (PFI) and direct injection (DI) to enhance the in-cylinder mixing performance in natural gas engines, a computational fluid dynamics (CFD) model of PFI-DI hybrid injection was developed to analyse the effects of PFI/DI supply ratio on methane mixing uniformity. The results indicate that the best mixing performance is achieved under the PFI method, with a high percentage of 56.13% for the best methane concentration region (BMCR) at ignition. When the PFI/DI supply ratio is 70/30, the BMCR percentage at ignition is the highest compared to other hybrid injection scenarios, and the methane distribution is more favourable to flame propagation than 100% direct injection. When the PFI/DI supply ratio stands at 65/35 or below, the BMCR percentage at ignition tends to stabilise, influenced by in-cylinder flow velocity, turbulent kinetic energy, and concentration differentials. [Received: December 4, 2023; Accepted: May 29, 2024]
  Keywords: port fuel injection; PFI; direct injection; DI; mixing performance; natural gas engine; hybrid injection.
  
  
• Production model of fractured horizontal wells in shale gas reservoirs considering different gas diffusion mode  
  by Shuyong Hu, Wenhai Huang, Jiayi Zhang, Daqian Rao, Bingyang Zheng, Tingting Qiu 
  Abstract: Because of the complexity of the shale gas seepage mechanism, the establishment of a fractured horizontal well model of shale gas reservoirs based on multiple migration mechanisms is helpful to the development of shale gas reservoirs. In this study, different matrix diffusion modes in different regions are considered. The fractured horizontal well model of the shale gas reservoir can be divided into a fracture network region (SRV region) and a shale matrix region. The quasi-steady-state matrix diffusion in the fracture network region is described by Fick’s first law, the non-steady diffusion in the matrix region is described by Fick’s second law, and seepage of the fracture system is described by Darcy’s law. Based on the above ideas, a fractured horizontal well production model of fractured horizontal wells in shale gas reservoir is established. The Laplace transformation, Duhamel principle, and Stehfest numerical inversion are used to solve the mathematical models of seepage flow, and a sensitivity analysis of the dimensionless production curve is performed. [Received: January 22, 2023; Accepted: February 13, 2024]
  Keywords: shale gas; fractured horizontal well; seepage mechanism; gas diffusion; seepage model.
  
  
• Experiments and numerical investigation on rock-breaking enhancement mechanism of supercritical CO2 jet drilling  
  by Can Cai, Shengwen Zhou, Hao Chen, Bangrun Li, Wenyang Cao, Lang Zeng, Xianpeng Yang, Kejie Chen, Tianzhou Li, Liehui Zhang 
  Abstract: Supercritical CO2 (SC-CO2) jet drilling technology has been proposed to solve the problems of low rock-breaking efficiency and severe thermal wear of PDC cutters in high temperature formation. However, the rock-breaking enhancement mechanisms of SC-CO2 jet on PDC cutter are poorly understood. Therefore, in this paper, SC-CO2 jet-PDC cutter composite rock-breaking experiments and numerical simulation have been employed to study the fundamental factors of SC-CO2 jet enhanced rock-breaking and the influence of different working parameters on composite rock-breaking. The results indicated that the rock debris carrying and impact effects of SC-CO2 jet are the fundamental causes of cutting force reduction. The main reason for the SC-CO2 jet cooling cutter is the heat absorption of gas expansion and phase transition. The research findings offer a theoretical basis for SC-CO2 jet-PDC cutter composite rock-breaking and could support gas drilling, hot dry rock drilling, and deep oil-gas development. [Received: 25 April 2024; Accepted: 24 June 2024]
  Keywords: supercritical CO2 jet; rock-breaking; numerical simulation; PDC cutter; experimental study; rock debris carrying; one-way fluid-solid coupling; cooling effect; jet impact.
  
  
• Early detection of overflow based on genetic algorithm for capturing multiple feature changes in managed pressure drilling  
  by Meng Wang, Zhiyong Chang, Mengxuan Cao, Jiasheng Fu, Xiaosong Han 
  Abstract: Drilling overflows can result in significant losses of money and personnel. So early detection of overflows is of great practical importance. In this paper, six managed pressure drilling features related to overflow are selected, outlet and inlet flow difference, standpipe pressure, methane, ethane, pump flush and hook height. Then the variance, slope and mean of each feature within a statistical time window are calculated. Their thresholds are optimised to detect the overflow point according to the change of statistics by an improved multi-objective genetic algorithm. Logistic chaotic mapping is used to initial the genetic algorithm, and the Levy flight is employed to improve the mutation operator. Experiments show that the new algorithm achieves an average overflow recall rate of 93.7%. The method is able to provide early warning for drilling engineers, thus further safeguarding wellbore safety. [Received: April 7, 2024; Accepted: June 6, 2024]
  Keywords: overflow detection; genetic algorithm; multiple feature; managed pressure drilling.
  
  
• Comparative analysis of energy consumption and carbon emission in the boil-off gas recondensation process  
  by Kun Huang, Xin Wang, Li Cao, Kun Chen, Nan Zhou, Yuxuan Gao 
  Abstract: In this study, the goal is to minimise energy and carbon emissions in liquefied natural gas (LNG) receiving stations by optimising the boil-off gas (BOG) recondensation process. Four processes were evaluated using a performance model that considered both energy and emissions. A genetic algorithm optimised the parameters for lowest possible consumption and emissions. The analysis revealed that while increasing stages of recondensation and compression, and altering cooling methods, led to a minor increase in energy consumption, it resulted in significant emission reductions. Higher BOG content further amplified these savings. Notably, a two-stage recondensation process with pre-cooling and post-cooling (case D) achieved the greatest reduction in carbon emissions, confirming its effectiveness in reaching carbon neutrality goals, despite a slight rise in energy use compared to the base process (case A). [Received: March 19, 2024; Accepted: July 4, 2024]
  Keywords: BOG recondensation; parameter optimisation; energy consumption; carbon emission; process selection.
  
  
• Experimental investigation for partially premixed compression ignition in a diesel engine using n-butanol, biodiesel, and diethyl ether blends  
  by Gangeya Srinivasu Goteti, P. Tamilselvan 
  Abstract: This research aims to optimise combustion by reducing emissions and improving performance parameters. This research also investigates biodiesel usage and an ignition improver with an increased compression ratio of 20 by supplying n-butanol with preheated air. The experimental work was first conducted with diesel to generate baseline data. It was then performed using a blend of n-butanol, diesel, and an ignition improver. The experiment was repeated by using the combustible mixture B25N15DE1, which contains Prosopis juliflora methyl ester, diesel, DEE, and n-butanol vapours on a volume basis. The n-butanol mists were added by port injection in the proportion of 15% into the preheated air stream to attain partially premixed compression ignition. The increased brake thermal efficiency (33.21%) and reduced emissions of hydrocarbons with 65 ppm and carbon monoxide of 0.29% were observed, along with the increased heat release rate (48.1 J/ CA) at a partially premixed mode. [Received: 14 June 2022; Accepted: 24 June 2024]
  Keywords: brake thermal efficiency; combustion; crank angle; emission; heat release rate.
  
  
• Prediction and optimisation of electricity market clearing price in Turkey by using machine learning methods  
  by Murat Ince, Ahmet Kabul, Mesut Aksoy 
  Abstract: This research aims to reduce price instability in the market clearing price (MCP) in Turkey by estimating MCP using machine learning techniques based on production resource-based data. The model will balance market prices by shifting from a price-based to a resource-based approach, minimising the price of electricity units by decreasing imported energy production and increasing domestic and renewable energy production. Thus, in this study, the effect of MCP on electricity unit prices and forecast values until July 29, 2023, was compared. By using past year data between 2014 and 2022, the MCP price in 2023 is determined. As a result of artificial neural network prediction, the average MCP value for 2023 was revealed 85.9 USD. The best results were obtained with artificial neural network (ANN) (R2 = 0.8827, RMSE = 0.0309 and MAE = 0.0223). Also, the model predicts estimated 2023 energy production by incorporating real-time production values from energy resource production data. The performance indicators of the implemented forecasting methods increase efficiency in future production forecasts and contribute to accurate pricing in energy purchases. [Received: February 17, 2024; Accepted: July 7, 2024]
  Keywords: market clearing price; MCP; energy efficiency; machine learning; regression; Turkey; artificial neural network; ANN.
  
  
• A prediction of China's dependence on foreign oil up to 2060  
  by Guangyue Xu, Lanmei Zang, Shuang Li, Qiuyu Song, Kyaw Jaw Sine Marma 
  Abstract: Chinas dependence on foreign oil has increased rapidly in the past few decades. If it continues to grow at the current rate, it will have a series of negative impacts on energy security, economic development, and international competition. The future trajectory of Chinas foreign oil dependence has become the most critical subject for debate. This paper focuses on such issues from three different perspectives - the historical perspective of Chinas oil dependence on foreign countries, the main factors affecting Chinas oil dependence on foreign countries, and the prediction of oil dependence on foreign countries. The forecast shows that Chinas oil dependence on foreign countries will likely reach its peak before 2030, and it is expected to reach its peak as early as 2026, with a maximum value of 75.24%. The realisation of this possible peak depends on the control of oil demand and the progress of oil production technology. Therefore, it is necessary to increase innovative technology orienting the oil industry and control consumption to address with high dependence on foreign oil. [Received: October 4, 2023; Accepted: February 12, 2024]
  Keywords: China’s petroleum; external dependency; prediction; energy security; peak.
  
  
• Evolutionary algorithms for integrated oil and gas supply chain management considering enhanced oil recovery methods  
  by Raheleh Ardestani, Esmaeil Mehdizadeh, Farhad Etebari 
  Abstract: The aim of this study is to model and solve the problem of integrated oil and gas supply chain management (SCM) considering the enhanced oil recovery (EOR) methods in upstream and midstream sectors. For this purpose, the problem is modelled as a bi-objective mixed integer nonlinear program and solved by using BARON solver in the GAMS software. The LP-metric method is used to solve the bi-objective problem. In addition, we used three evolutionary algorithms, namely non-dominated sorting genetic algorithm (NSGAII), multi-objective whale optimisation algorithm (MOWOA), and multi-objective cuckoo search algorithm (MOCSA) to solve the problems of large sizes. To investigate the efficiency of the solution method, 15 problems were solved with a wide range of dimensions. Based on the results, small-size problems can be solved in less than 100 seconds to reach a relative gap of 0.01. However, the solution time increases rapidly when the size of the problem increases. The results of the evolutionary algorithms show that these algorithms (especially MOWOA) can solve the large problems in a reasonable time. In addition, MOWOA is superior to other implemented algorithms in terms of multi-objective solution quality measures. [Received: March 3, 2023; Accepted: October 27, 2023]
  Keywords: oil; gas; supply chain management; SCM; enhanced oil recovery; multi-objective; whale optimisation; NSGAII; cuckoo search.
  DOI: 10.1504/IJOGCT.2024.10065288
   
  
• Designing an environmentally friendly nano-chemical dispersant/inhibitor for asphaltene in Iraqi oil-field crudes  
  by Dana Mohammad, Hiwa Sidiq 
  Abstract: Asphaltenes, a diverse family of molecules, pose significant challenges in the oil and gas industry. The use of nano inhibitors has emerged as an effective method for controlling asphaltene deposition, offering several advantages over traditional inhibitors. The presence of asphaltene can lead to flow assurance issues throughout the crude oil production life cycle. This research paper introduces the synthesis of a novel environmentally friendly nano chemical inhibitor as a potential solution to address these challenges. Promising results have been observed with the use of green-zinc oxide nanoparticles (NPs) and green copper oxide NPs. The findings indicate that employing ZnO and CuO NPs can significantly enhance asphaltene dispersion in crude oil by approximately 23% compared to commercially available inhibitors using ADT. The study will further evaluate and compare two synthesised nanofluids with a commercial chemical. [Received: July 19, 2022; Accepted: February 13, 2024]
  Keywords: asphaltene; asphaltene dispersant test; ADT; nanoparticle; inhibitors.
  DOI: 10.1504/IJOGCT.2024.10066410
   
  
• Prediction of shale gas horizontal well production using particle swarm optimisation-based BP neural network  
  by Qi Chen, Wei Wang 
  Abstract: The production prediction of shale gas horizontal wells is a critical task. Traditional empirical formulas and mathematical analytical methods have significant errors in predicting shale gas production capacity. To address this issue, we propose a method based on particle swarm optimisation (PSO) to optimise a backpropagation (BP) neural network for shale gas production prediction. Through extensive research, it has been demonstrated that the factors affecting shale gas horizontal well production primarily consist of geological and engineering factors. We employ the grey relational analysis (GRA) method to analyse the main influencing factors on well production and select relevant factors as parameters. The experimental results demonstrate that the utilisation of algorithm-optimised backpropagation (BP) neural networks for predicting the production capacity of hydraulic fracturing wells in actual shale gas reservoirs is more accurate. [Received: August 30, 2023; Accepted: November 21, 2023]
  Keywords: particle swarm optimisation; PSO; BP neural network; shale gas; grey correlation method.
  DOI: 10.1504/IJOGCT.2024.10066423
   
  
• Temperature and pressure calculation model of the wellbore annulus in the formation with narrow safe drilling fluid density window during gas kick process  
  by Qiangui Zhang, Gen Yang, Xiangyu Fan, Jiawei Ran, Zhilin Li, Juntian Shuai, Qiang Wei, Mubai Duan 
  Abstract: In this paper, a mathematical model is developed to simulate gas-liquid flow for calculating temperature and pressure in the wellbore annulus of formations with a narrow safe drilling fluid density window (NSDW). The model incorporates equations for liquid mass conservation, gas mass conservation, additional energy conservation of other components in the wellbore annulus-formation system, and gas-liquid slip velocity. The flux vector splitting method (AUSMV) is employed to solve the mass and momentum conservation equations. The effectiveness of the proposed model is demonstrated by predicting the temperature and pressure in the wellbore annulus for well XX1, revealing a strong agreement between the predicted results and the field-measured values. Additionally, this model is applied in order to analyse the impacts of formation pore pressure, injected drilling fluid displacement, and wellhead back pressure on well kick. The results provide valuable insights that can guide drilling operations in NSDW formations. [Received: November 21, 2022; Accepted: February 13, 2024]
  Keywords: narrow safe drilling fluid density window; gas-liquid flow; temperature and pressure prediction; well kick evaluation; mathematical model.
  DOI: 10.1504/IJOGCT.2024.10066418
   
  
• Effects of methanol substitution rate on combustion and emission characteristics of methanol/diesel dual fuel engines  
  by Changchun Xu, Huabing Wen, Haiguo Jing, Jingrui Li, HaengMuk Cho, Daifen Chen 
  Abstract: In order to investigate how methanol affects combustion and emission, this article tries to alter the ratio of substitution of methanol. The methanol replacement rate is set as 0%, 10%, 20%, 30%, 40% for researching engine combustion performance and exhaust emissions. The results indicate that, under medium and low load circumstances, the maximum burst pressure in the cylinder falls, the ignition delay lengthens, and the general consumption of energy increases as the methanol substitution rate increases. HC and CO emissions exhibited an increase, whereas NO_x emissions demonstrated a decrease. Under high load conditions, the maximum explosion pressure initially rose and subsequently declined. Additionally, the total energy consumption displayed an initial decreasing trend followed by an increasing trend. HC and CO emissions experienced a slight increase, while NO_x levels remained relatively stable before eventually decreasing. [Received: January 17, 2024; Accepted: April 1, 2024]
  Keywords: methanol substitution ratio; high latent heat of vaporisation; combined injection; performance; exhaust emissions.
  DOI: 10.1504/IJOGCT.2024.10066406