Energy Exploration & Exploitation

The East African Rift System (EARS) is the youngest rift with the eastern and western branches, consisting of multiple secondary rifts. It is generally characterized by low extent of exploration and great exploration potential. Identifying the differences in hydrocarbon accumulation conditions in various rifts is very important in providing guidance for hydrocarbon exploration and selecting favorable exploration targets in this area. Based on the interpretation of first-hand seismic-geological data, and combining other materials such as commercial databases and open access literature, the structural characteristics of the main rifts in the eastern and western branches of the EARS are comparatively analyzed; based on the anatomy of the discovered hydrocarbon reservoirs, the rifts of the EARS are classified into four types depending on their structural styles, including double-fault type, simple single-fault type, single-fault transfer type, and single-fault terrace type, and the control of structural style over hydrocarbon accumulation in the EARS is discussed. In the Western Branch of the EARS, large rifts such as the Albertine Rift and the Tanganyika Rift are mainly of double-fault type and single-fault transfer type, and only some small rifts are of the simple single-fault type. The rifts in the Eastern Branch of the EARS are generally small; the rifts in the northern section of the Western Branch are mainly of the simple single-fault type, and those in the southern section are mainly of the single-fault terrace type. The hydrocarbon potential of double-fault rifts is the greatest, followed by that of simple single-fault rifts and single-fault transfer rifts, and the hydrocarbon potential of single-fault terrace rifts is relatively limited. The results obtained from a comparative analysis of the structures, sediment fills, and reservoir elements of various rifts show that the west side of the Albertine Rift, the southeast of the Tanganyika Rift, the Kerio Rift, and the land in the northwest of the Turkana Rift have great exploration potential.

The excessive use of single-use plastic products in modern life has caused severe environmental, social, economic, and health consequences globally. Mostly all plastics manufactured are one-time-use materials that end up in landfills or as unmanageable garbage. This situation has led to the production of around 400 million tonnes of plastic waste per year, and if this trend continues, global production will reach up to 1100 million tonnes by 2050. India alone produced over 34.7 lakh tonnes per annum (TPA) of plastic waste, with only half of it being recycled or co-processed. As such, there is an urgent need to develop ways to reduce plastic waste. One possible solution is the use of waste plastic biofuel in engines, which has been shown to have promising results. The study aimed to analyse waste plastic oil using (Gas Chromatography Mass Spectrometry) GC-MS and (Fourier Transform Infrared Spectroscopy) FTIR analysis to identify its chemical composition. The findings of the study revealed the presence of various chemical compounds, such as alcohol, hydroperoxide, carbonyl acid groups, ester, carboxylic acid, ketones, aldehyde groups, and others. FTIR analysis confirmed the presence of alcohol, hydroperoxide, carbonyl acid groups, methyl and methylene groups, ester, carboxylic acid, ketones, aldehyde group, symmetric and asymmetric C-H bending, C-O stretch for ethers, carboxylic acids, and esters, and=C-H bending out for alkenes. The study further explains that primary plastic consumption and packaging lifetime have a significant impact on plastic waste generation. The research indicates the need to explore alternative ways to recycle and dispose of single-use plastics to mitigate its negative impact on the environment. Furthermore, this study analyses the statistical optimisation method to develop a model fit for engine behaviour using waste plastic biofuel on a single-cylinder (common rail direct injection engine) CRDi diesel engine using the (weighted aggregated sum product assessment) WASPAS approach. Additionally, the objective is to develop a model that can optimise the engine's performance while using waste plastic biofuel. The uncertainty analysis demonstrated that the experiment was carried out with a high degree of accuracy and the results were reliable. The study employed the WASPAS methodology to evaluate the performance of different (waste plastic oil) WPO samples, and the results showed that the optimal parametric setting to obtain the desired responses can be achieved with a fuel blend of 5%, load of 21 bar, and speed of 2000 RPM. However, the results demonstrate that the use of waste plastic biofuel can significantly improve engine performance, and the proposed optimisation model can accurately predict the engine's behaviour. The regression equation that was formulated showed a reasonable degree of agreement between the actual experimental results and the predicted values, thereby indicating the reliability of the experiment. Significant effects were observed from fuel blend, and speed, whereas load did not make a substantial contribution. The findings regarding the effect of parameters suggest that a reduction in fuel blend, and engine speed resulted in a decline in the performance index, while variations in load had little impact. The relationship between load and speed demonstrates that a rise in load and a reduction in speed contributed to enhanced combustion and a higher performance index. The interaction among fuel blend and speed, with a particular emphasis on the significance of reduced fuel blend and speed values in order to optimise the performance index. The findings of the analysis underlined the vitality of process parameters, specifically fuel blend and speed, wherein speed exhibited a significant impact on the outcomes. The study concludes that the use of waste plastic biofuel in engines can be an effective way to reduce plastic waste while improving engine performance. This study's findings can be applied to various engines to improve their performance while reducing plastic waste. All in all, the outcomes of the study make a substantial contribution to the advancement of scientific information regarding the properties of waste plastic oil as well as its combustion characteristics. This expands the potential for advanced breakthrough innovations in sustainable energy solutions and the conservation of the environment.

Nowadays, the surge in energy demand due to economic growth and extreme weather conditions has put immense pressure on the usage of fossil fuels in Kuwait. As a result, scheduled load-shedding is performed in some regions during the summer season to meet the energy demand. To address this issue, this paper proposes a photovoltaic-based street lighting system as an alternative solution to meet the rising energy demand in Kuwait during the daytime. This study initially investigates the existing street lighting systems in the state of Kuwait. Subsequently, three different configurations of photovoltaic panels are proposed based on the existing streetlight pole structures. The simulation models are then developed and evaluated using physical security information management and PVSyst simulation platforms, aiming to validate their performance against conventional power generation models in Kuwait. The proposed photovoltaic system is designed based on feedback information collected from the existing installed capacity. Finally, an overall energy model is presented to demonstrate how solar potential can offset energy consumption during peak demand hours. Practical testbed data from the Al-Jahra residential area of Kuwait is used for validation. The results indicate that the proposed photovoltaic street lighting system can generate a maximum power output of 18.8 GWh in August and a minimum of 11.8 GWh in December, compared to the monthly consumption of 30.45 GWh. The study showcases the economic viability of the solution, with an average degradation ratio of 13% of the total cost. Moreover, the proposed system contributes to a reduction in CO 2 emissions from traditional power plants.

Identifying subsurface resource analogs from mature subsurface datasets is vital for developing new prospects due to often initial limited or absent information. Traditional methods for selecting these analogs, executed by domain experts, face challenges due to subsurface dataset's high complexity, noise, and dimensionality. This article aims to simplify this process by introducing an objective geostatistics-based machine learning workflow for analog selection. Our innovative workflow offers a systematic and unbiased solution, incorporating a new dissimilarity metric and scoring metrics, group consistency, and pairwise similarity scores. These elements effectively account for spatial and multivariate data relationships, measuring similarities within and between groups in reduced dimensional spaces. Our workflow begins with multidimensional scaling from inferential machine learning, utilizing our dissimilarity metric to obtain data representations in a reduced dimensional space. Following this, density-based spatial clustering of applications with noise identifies analog clusters and spatial analogs in the reduced space. Then, our scoring metrics assist in quantifying and identifying analogous data samples, while providing useful diagnostics for resource exploration. We demonstrate the efficacy of this workflow with wells from the Duvernay Formation and a test scenario incorporating various well types common in unconventional reservoirs, including infill, outlier, sparse, and centered wells. Through this application, we successfully identified and grouped analog clusters of test well samples based on geological properties and cumulative gas production, showcasing the potential of our proposed workflow for practical use in the field.

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.

This review presents a techno-economic analysis of microbial fuel cells (MFCs) in the domain of generating sustainable energy and treating wastewater with the aim of attracting investors through research and development for residential and commercial applications. The operation principles and various MFC types, along with their advantages and disadvantages, are thoroughly considered. The efficiency of various MFC types is considered to present appropriate options for commercial applications. However, large-scale integrations face substantial financial limitations owing to the reluctance of investors. This review explores the cost-benefit balance associated with the operation of an MFC system. For encouraging investors, different cost variables, such as the initial investment, operating costs, potential electricity generation, and waste treatment capacity, are thoroughly considered. These variables are placed on the spectrum of a cost-benefit analysis to vitalize the economic feasibility of the MFC technology in various scenarios, considering an order of financial variables. MFC development at an optimized cost is the pivotal prerequisite to secure a competitive advantage over conventional sources of energy with carbon emissions. Thus, this study is expected to prompt decision-makers to adopt the MFC technology at the commercial level.

Serbia's energy sector is heavily reliant on Russian influence. On the other hand, Serbia’s status of a candidate country for joining the European Union (EU) membership requires active working towards diversifying of energy sources of supply. In the past decade, Serbia has secured a reduced price for natural gas through a bilateral agreement with Russia, addressing the shortfall in its domestic production. The former agreement priced Russian gas at US$270 per thousand cubic meters and expired in 2021. The new deal links gas prices to crude oil and ranges between US$310 and US$408, maintaining its competitive position as one of Europe’s lowest import prices. Furthermore, alongside the new gas pipeline for Russian gas exports, the EU is funding the construction of a new interconnector, both with entry points from Bulgaria. Serbia also faces significant dependence on crude oil, and this reliance is compounded by the inability to import it from Russia any longer. Opposite, Serbia is usually self-sufficient in electricity production which still remains under state ownership. The domestic exploration and processing of oil and gas, as well as the sole underground gas storage facility in Serbia, have partial ownership by Russian Gazprom while the transportation of gas is under the full control of the Serbian government. This Communication about energy situation in the Republic of Serbia put particular emphasis on the evolving political dynamics in the global energy market with a specific focus on the Russia-Ukraine war. The topic is also linked to the contentious status of the southern Serbian autonomous province, recognized as an independent state by the majority of western nations but not by Serbia. It is feared that Serbia’s energy dependence on Russia could have significant ramifications for its EU candidacy.

Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km ² , indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km ² , respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.

Wind and sunlight are increasingly being exploited as energy supplies that never run out. Additionally, renewable energy resources, including sun, wind, and geothermal heat, are being used for different technologies. It was considered the use of hybridized wind-solar energy resources in smart vehicle technology. A thorough understanding of an integrated framework of the hybridized renewable energy for smart vehicle-to-grid (V2G) systems is essential and required to further identify and perhaps maximize existing opportunities. Aiming to develop a vehicle-to-grid (V2G) system where the smart vehicle runs on stored sunshine and wind energy, and vehicle batteries store energy and release it to the electricity grid in peak demand periods. To achieve this aim, mathematical models for solar and wind systems were created and entire 24-h simulations were run for case studies of three smart vehicles, which were assessed for different scenarios and circumstances, using the MATLAB/SIMULINK environment. The estimated values obtained were home load 10 MW, power factor 0.15 MVA, industrial load 0.16 MVA, and smart car-to-grid, solar panel farm, and wind farm power of 4 MW, 8 MW, and 4.5 MW, respectively. Therefore, the hybridized wind-solar energy sources were applicable for all three smart vehicles considered.

The present study reports on an investigation of teak sawdust pyrolysis oil blended with commercial diesel in a small four-stroke compression ignited engine. The engine performance and emissions were evaluated. The teak sawdust pyrolysis oil was obtained from a single-stage fixed bed pyrolysis reactor at 600 °C. Its physicochemical properties were characterized and found to be acceptable for the engine. Teak sawdust pyrolysis oil blends with diesel at the ratios of 10%, 25%, and 50% by mass were utilized. The small engine was tested at constant speeds from 800 to 2600 r/min. 25% teak sawdust pyrolysis oil blend at 2000 r/min was found to have better brake thermal efficiency with lower brake-specific fuel consumption compared to the other teak sawdust pyrolysis oil blends. Meanwhile, the highest engine load was obtained at 50% teak sawdust pyrolysis oil blend and 2600 r/min to be 8 kW. Furthermore, the emissions of CO, CO 2 , and hydrocarbon at 50% teak sawdust pyrolysis oil and 2000 r/min were slightly lower than other teak sawdust pyrolysis oil blends, no NO x detection in tested fuels, moreover, at 2600 speed, the smoke opacities of the fuels show lower than those the others. It was noted that a blend of 25% teak sawdust pyrolysis oil with diesel was suitable for the small engine (at 2000 r/min) in terms of performance and CO, CO 2 , and NO X emission for sustainability in agriculture and rural areas.

The present work experimentally evaluated the performance of a solar collector comprised evacuated tube heat pipe (ETHP) coupled with a compound parabolic concentrator at different tilt angles. Therefore, experiments have been conducted in the climate conditions of Tamil Nadu (77.07°E,11.04°N), India, from April 15, 2019, to May 20, 2019. The objective of the work is to explore the effect of a tilting angle on the performance of an evacuated tube solar collector with a thermosyphon attached to the compound parabolic concentrator. The CPC is/home/eea designed with an aperture width of 343 mm, concentration ratio of 2.32, and aperture angle of 25.4°, improving the solar collector efficiency with the help of MATLAB Programming, which gets Coordinate points based on these coordinate points. CPC Profile is fabricated. The Thermosyphon heat pipe is constructed with a Copper tube having a 19 mm diameter with 40% Acetone charged. The experiments were conducted by varying the tilting angles of the solar collector at 15°, 30°, 45°, and 60°horizontal. The heat resistance and instantaneous efficiency of the solar collector are studied in this study. The result reveals a minimum thermal resistance of 0.02 kW −1, and a maximum efficiency of 78% was recorded at a 45°tilt-ing angle.

The efficient extraction of gas from low-permeability coal seams is an urgent problem in coal mine safety production. The traditional gas extraction technology generally suffers from problems that limited penetration enhancement or extraction effect, low construction efficiency, large workload, etc. Thus, it is especially urgent and important to explore the new technology applicable to efficient underground gas extraction. In this paper, based on the principle of hydraulic fracturing to increase permeability, we innovatively propose a technique to enhance the effect of hydraulic fracturing to increase permeability and further improve the efficiency of gas extraction using the gas desorption activity of native microorganisms in coal seams. Herein, the composition of the primary microbial community of a coal seam in Xinji No.2 mine was analyzed by bacterial and archaeal 16SrDNA amplicon sequencing, the community structure of the main functional microorganisms was clarified, the optimal combination of functional microorganisms for organic matter degradation in coal seam under anaerobic culture conditions was obtained. Besides the Biolog microplate technology was used to screen the nutrients of the excitation carbon source to stimulate the rapid decomposition of coal organic matter by microorganisms and to define the optimal ratio of the excitation carbon source to microorganisms. Finally, the effect of this technology on the application of coal seam fracturing and gas extraction was tested through field industrial tests, revealing that the extraction effect of this technology was more significant than that of the common coal seam perforation extraction technology. The results of this paper provide a new technical idea for gas extraction from low permeability coal seams, which is an important reference value for subsequent similar studies.

Geothermal energy is a renewable energy that is environmentally friendly and will help reduce greenhouse emission resulting from the burning of fossil fuels. Nigeria has numerous geothermal surface manifestations like hot and warm springs in most parts of the country that has not been exploited or explored due to the initial high cost of exploration. Most research conducted on geothermal energy prospects in Nigeria considered specific geographical sections. Due to the geological features of Nigeria, the direct and indirect tectonic activities, there is the need to map the thermal anomalies over Nigeria to determine likely geothermal wells and ground heat catchment in Nigeria. Forty years remote sensing dataset (1980–2019), was obtained from the MERRA-2 for three hundred and two (302) locations across Nigeria. The acquired thermal parameters were processed using known models. The data was also analyzed statistically and spatially using the Statistical Package for Social Sciences (SPSS) and Quantum Geographic Information System (QGIS). The results of show that the Gummel-Kumaganum areas of chad basin, Owode-igbo ora areas of the Dahomey basin, Belli area of the basement complex in Taraba state, potiskum areas of the Upper benue Basin, Ekpoma-kwale, itu areas of the Niger delta basin are middle geothermal wells with the terrestial radiation of < −702 W/m ² while the terrestial radiation >200 W/m ² in the Gummi area of the sokoto basin, Hunkuyi area of the basement complex in the northeast, Gashua area of the chad basin, Ozubulu-idah area of Anambra basin, Atijere area of the Dahomey basin, Agbasa, Omoko, Akamkpa area of Niger delta basin, Shaki area of the southwest basement complex are heat catchment regions. Other heat catchment areas had been identified for standalone energy generation. Also, the thermal anomalies in those areas were significant. The validation of the result was achieved via benchmarking similar geothermal well around the globe and ground truthing at Ijebu-Ode Nigeria. Based on the thermal reversal depth (TRD) concept, Ijebu-Ode may have a deep geothermal well with temperatures pattern similar to geothermal wells in other parts of the globe. It is recommended that ground measurement should be carried out in the basement complex to cater for geothermal systems whose mechanism is based on conduction.

The small-scale roadway model is often used in the fine simulation of mining engineering. The determination of the structure and load conditions of the model has an important influence on the accuracy of the simulation. In this paper, a large-scale stratum model and a small-scale roadway model are established by using finite element method. The optimal loading mode of the roadway model and its applicability under different roof-sidewall stiffness ratios are studied. The simulation accuracy of the roadway model is quantitatively evaluated by comparing the distribution laws of stress field and strain field with those of the stratum models. Under the same roof-sidewall stiffness ratio, the similarity between the simulation results of the roadway model and the stratum model under displacement load is much higher than that under stress load. Under the same load mode, the stress and strain similarity between the stratum model and roadway model increases with the increase of the roof-sidewall stiffness ratio. Furtherly, the simulation application of the roadway drilling pressure relief is carried out. Compared with the large-scale stratum model with small-size elements, the small-scale roadway model under displacement load also shows obvious stress transfer after drilling pressure relief, while it has faster computational efficiency. Finally, a small-scale roadway model simulation method suitable for surrounding rock disaster occurrence mechanism and control is proposed.

Accurate prediction of photovoltaic (PV) power generation is the key to daily dispatch management and safe and stable grid operation. In order to improve the accuracy of the prediction, a finite iterative PV power prediction model with long range dependence (LRD) characteristics was developed using fractional Lévy stable motion (fLsm) and applied to a real dataset collected in the DKASC photovoltaic system in Alice Springs, Australia. The LRD prediction model considers the influence of current and past trends in the stochastic series on the future trends. Firstly, the calculation of the maximum steps prediction was introduced based on the maximum Lyapunov. The maximum prediction steps could provide the prediction steps for subsequent prediction models. Secondly, the order stochastic differential equation (FSDE) which describes the fLsm can be obtained. The parameters of the FSDE were estimated by using a novel characteristic function method. The PV power forecasting model with the LRD characteristics was obtained by discretization of FSDE. By comparing statistical performance indicators such as root max error, mean square error with Conv-LSTM, BiLSTM, and GA-LSTM models, the performance of the proposed fLsm model has been demonstrated. The proposed methods can provide better theoretical support for the stable and safe operation of PV grid connection. They have high reference value for grid dispatching department.

This study investigated the spillover effects of geopolitical risks on energy (crude oil, coal and natural gas) markets. The empirical evidence is based on the CoVaR index and the CAViaR-EGARCH model. Results demonstrate that the spillover effects of geopolitical risks on the global energy market are nonlinear, asymmetric and time-varying. With each 1% rise in global geopolitical risks, the left tail risks in the crude oil, coal, and natural gas markets decreased by 0.179%, 0.119% and 0.113%, while the right tail risks increased by 0.144%, 0.135% and 0.097%, respectively. In addition, the magnitude of energy crises triggered by different geopolitical events varies. Lastly, the spillover effects of GPR on energy markets vary considerably across nations, with more substantial effects observed on average in BRICS than in G7 countries. The primary implication is to provide references for government and energy investors to avoid energy market risks timely.

Taking the steeply dipping and large mining height working face of a mine as the engineering background, through the combination of physical simulation experiment, numerical calculation, theoretical analysis and field monitoring, based on a comprehensive analysis of the deformation and failure characteristics of the macrostructure of surrounding rock, the roof breaking mechanism and support instability characteristics of large mining height working face under the angle effect are studied. The research shows that due to the influence of the dip angle of the coal seam, the roof stress is asymmetrically deflected along the tendency, and the load of the overlying strata is transmitted to the upper and lower coal bodies with the stress-deflection boundary as the boundary, resulting in the deformation and failure of the roof and the filling showing obvious asymmetric characteristics. With the increase of dip angle, the asymmetric characteristics of roof stress transfer are enhanced, the stress release arch is reduced, the height of caving zone is reduced, the deformation and failure area is gradually moved up, and the regional characteristics of roof loading and deformation and failure are more obvious, which leads to the significant increase of unbalanced loading degree and instability probability of supports in different areas. Combined with the actual production, the prevention and control measures of hard roof caving and support crushing in fully mechanized mining face with steeply dipping seam and large mining height are put forward.

Storage systems are needed to boost the reliability of intermittent solar and wind resources in power networks. Rather than focus on one storage system or one hybrid energy storage system (HESS), this work models the operation of six HESS configurations in a Renewable Energy (RE) based grid-tied network. The objective is to minimise the daily operational costs of the microgrid while prolonging the storage lifetime by considering storage degradation costs. The influence of fixed tariffs and time-of-use (TOU) tariffs on the optimal operational of the HESS configurations have also been investigated; as well as deferrable demand satisfaction, charge-discharge pattern of different HESS and availability of the power-dense storage system within the microgrid. Results show that the lead-acid battery and hydrogen fuel cell (HFC) HESS incurs the highest operational costs, while the supercapacitor-lead-acid battery HESS incurs the lowest operational costs. The supercapacitor-lead acid battery and the supercapacitor-HFC HESS incur the highest annual storage degradation costs. The grid expenses were seen to be the same for all HESS under each tariff scheme. Lastly, decreasing the minimum storage level further by 10% from the 30% in the base case, led to an increase in the number of hours of availability of the power-dense storage system of five of the six HESS. These results have given a deeper understanding to the operation of HESS systems and can inform better decision making of the suitable HESS to be deployed in different operating scenarios.

This research reports the implementation of logarithmic mean Divisia index (LMDI) and categorizes the growth of total energy usage in three different industrial sectors for the years of 2010 to 2021. Furthermore, it classifies and evaluates the factors influencing on energy consumption in Punjab province thru a sustainable way. The growth consumption is classified into scale influence, structure influence, and efficiency influence. Likewise, the long-term energy alternatives planning-Punjab model is executed with the energy consumption, scale impact, structure impact, and efficiency impact. Besides, comprehensive adjustment scenarios are also introduced to examine the impact of three different factors on overall energy usage. The results from the qualitative decomposition of LMDI indicate that the high scale can lead to high-energy consumption in Punjab Province. However, it can be reduced by high-efficiency reinforcement. The total energy consumption in 2024, 2036, and 2044 under reference scenario is 304.12, 460.01, and 590.04 million tons compared to structure influence analysis for slow terminology (SIAS) and comprehensive scenario. For that reason, it can predict and provide earlier energy management planning for the province. Conversely, the structure factor does not display obvious effect on the energy use. Equally, the quantitative results of the long-term energy alternative planning (LEAP) model are relatively consistent with those of LMDI model, whose advantageous impact on the structure influence is reasonably extrapolated. This phenomenon indicates that the structure influence and efficiency influence will maintain the disruptive impact on increasing overall energy use for the future perspectives.

Deep-shaft mining in roadway stress concentration, difficult control of the surrounding rock, and mining imbalance affect mining efficiency in coal mines. The mechanical parameters and crack development law of roadway surrounding rock were studied by laboratory experiments. The mechanical model was established by the method of theoretical analysis, the maximum empty roof distance was deduced, and two support schemes were designed. Through the numerical simulation method, the support scheme was compared and analyzed, the deformation rule of roadway surrounding rock, the optimization of roadway support parameters and the application of hysteresis technology are studied. The effect of different support schemes was verified by three-dimensional similar simulation experiments. The practical engineering verification showed that the construction time of each support circle was reduced by approximately 1 h, and the work was completed 60 days earlier. The research showed that the optimal support scheme was support scheme 1 (7 bolts with 800 mm × 1000 mm spacing between the roof, 10 bolts with 800 mm × 1000 mm spacing between the two sides, and 2 × 1 × 2 cables with 1600 mm × 2000 mm spacing between the roof). Support scheme 1 was applied to the engineering site to control the deformation of surrounding rock at 80 mm, and the deformation was less than the original support scheme. The construction was completed in advance under the hysteresis process, and the support efficiency and operation safety were improved. The results revealed the mechanism of surrounding rock control and proved the effectiveness of digging support synergy. This optimization plan serves as a reference for studying roadway support in rapid excavation and provides theoretical support for safe and efficient coal roadway mining.

The law of ground pressure behavior can accurately guide the material proportion and performance of the roadside backfill body (RBB) in gob-side entry retaining (GER), thereby reducing the waste of materials and the cost of retaining roadway. In this study, a similar material modeling is used to verify the spatiotemporal law of the ground pressure in the engineering case of solid dense backfilling mining in Xingtai Mine, China. Based on that law, the theoretical requirements for the bearing performance of the RBB are proposed. Finally, a material mix proportion that meets the theoretical requirements is obtained by compression test, and the deformation and failure characteristics of the backfill body with that mix proportion are analyzed. The results show that the maximum pressure of the backfill body measured in Xingtai Mine is 5.5 MPa, which is about 40 m away from the coal face; after 40 m, the force on the backfill body will not increase anymore. The physical simulation experiment also proved that the ground pressure behind the coal face increases gradually and tends to be during the backfilling process, which shows certain spatiotemporal characteristics. Through the proportioning experiment, it is determined that the optimal material mix proportion of the RBB is gangue:fly ash:cement = 10:3:1, which meets the theoretical requirement that the strength of the RBB at any position is not less than the ground pressure at that position. The research results provide theoretical support for the field practice of GER in solid dense backfilling mining.

The hydrocarbon phase state of deep to ultra-deep reservoirs in the Tarim and Sichuan basins has been of great interest in oil and gas exploration. Based on a combination of molecular dynamics simulation, gold-tube pyrolysis experiments, and geological-geochemical theory, this study discusses the mechanisms governing the stability of oils in deep reservoirs from the perspectives of their reservoir accumulation histories and chemical reactions. Generally, the reason for the existence of liquid oil in the Tarim Basin is widely considered to be only controlled by external geological conditions, mainly including low geothermal gradient, absence of thermal events, low maximum reservoir temperatures, and late hydrocarbon generation process. However, this study firstly proposed that the chemical composition of oil is an internal factor for its thermal stability. The simulation results reveal that the polycondensation reactions of asphaltene will release hydrogen atoms, which can provide a necessary hydrogen source for cracking of liquid chain hydrocarbons. It means that the presence of asphaltene components can promote the cracking of chain hydrocarbons and generate methane. The normal mature oil in the Sichuan Basin generally has higher contents of asphaltenes than that of the high-mature light oil of the Tarim Basin, so more hydrogen has historically been available for the cracking of oil to gas. By looking at the accumulation histories and chemical compositions of the crude oils, this study first explains the stable long-term storage of liquid hydrocarbons in the Tarim Basin, providing important guidance for future deep to ultra-deep oil and gas exploration.

The advancement of horizontal drilling and hydraulic fracturing technologies has led to an increased significance of shale gas as a vital energy source. In the realm of oilfield development decisions, production forecast analysis stands as an essential aspect. Despite numerical simulation being a prevalent method for production prediction, its time-consuming nature is ill-suited for expeditious decision-making in oilfield development. Consequently, we present a data-driven model, ASGA-XGBoost, designed for rapid and precise forecasting of shale gas production from horizontally fractured wells. The central premise of ASGA-XGBoost entails the implementation of ASGA to optimize the hyperparameters of the XGBoost model, thereby enhancing its prediction performance. To assess the feasibility of the ASGA-XGBoost model, we employed a dataset comprising 250 samples, acquired by simulating shale gas multistage fractured horizontal well development through the use of CMG commercial numerical simulation software. Furthermore, XGBoost, GA-XGBoost, and ASGA-XGBoost models were trained using the data from the training set and employed to predict the 30-day cumulative gas production utilizing the data from the testing set. The outcomes demonstrate that the ASGA-XGBoost model yields the lowest mean absolute error and offers optimal performance in predicting the 30-day cumulative gas production. Additionally, the mean absolute error of the unoptimized XGBoost model is markedly greater than that of the optimized XGBoost model, indicating that the latter, refined through the application of intelligent optimization algorithms, exhibits superior performance. The insights gleaned from this investigation have the potential to inform the development of strategic plans for shale gas oilfields, ultimately promoting the cost-effective exploitation of this energy resource.

Traditional machine learning methods are difficult to accurately forecast oil production when development measures change. A method of oil reservoir production prediction based on normalized mutual information and a long short-term memory-based sequence-to-sequence model (Seq2Seq-LSTM) was proposed to forecast reservoir production considering the influence of liquid production and well spacing density. First, the marine sandstone reservoirs in the Y basin were taken as the research object to establish the sample database. Then, the feature selection was carried out according to the normalized mutual information, and liquid production, production time, equivalent well spacing density, fluidity and original formation pressure were determined as input features. Finally, a Seq2Seq-LSTM model was established to forecast reservoir production by learning the interaction from multiple samples and multiple sequences, and mining the relationship between oil production and features. The research showed that the model has a high accuracy of production prediction and can forecast the change of production when the liquid production and well spacing density change, which can provide scientific recommendations to help the oilfield develop and adjust efficiently.

As human demand for energy continues to grow, energy security has become an important research topic for national economic and social development. As the country with the highest energy demand and import in the world, China needs to ensure its energy import security in a personal way. Against this research background, this paper investigates the causal relationship between bilateral political relations and China's energy import security. This research selected HS 6-digit percentile trade data from 47 energy-exporting countries engaged in energy trade with China from 2000 to 2020. A trade gravity model was constructed to examine the impact of bilateral political relations on China's energy import security. Multiple empirical analyses were conducted using the PPMLHDFE method to investigate various aspects of the relationship. The research shows that: (1) Bilateral political relations can significantly affect China's energy trade imports. (2) The regional security situation of exporting countries and the signing of free trade agreements with China play a moderating role between bilateral political relations and energy imports. (3) In the heterogeneity analysis, the influence of bilateral political relations on China's energy trade has obvious stage characteristics, and the influence of bilateral political relations on China's energy trade is stronger in countries and regions along the Belt and Road Initiative, and there is a certain path-dependent type of China's energy imports.