Geoenergy Science and Engineering最新文献

{"title":"Potential kinetic effects of wax on clathrate hydrate formation: A review","authors":"Jiaqiang Jing ,&nbsp;Hang Yang ,&nbsp;Jie Sun ,&nbsp;Jiatong Tan","doi":"10.1016/j.geoen.2025.213765","DOIUrl":"10.1016/j.geoen.2025.213765","url":null,"abstract":"<div><div>Waxes and hydrates may coexist in oil-gas-water multiphase mixed transport pipelines. They all occur at low temperatures and may have kinetic influences mutually. The impact of waxes on hydrates has been a topic of considerable discussion, yet a consensus has not been reached. Recently published studies have compiled and found that the uncertainty may be attributed to the inherent complexity of oil-water mixtures. Consequently, the impact of waxes on the entirety of the hydrate formation process in the presence/absence of emulsifiers is deliberated, and the effect of emulsifiers on hydrate formation in the presence of waxes is discussed by comparison. The wax on hydrate nucleation is discussed by analyzing the induction period, the microstructure of emulsion and hydrate. The growth and aggregation properties are inferred by gas transformation, adhesion forces, and rheological properties. Further, the effect of waxes on hydrate formation is discussed at the molecular scale. The results show that waxes have a negative effect on the whole process of hydrate formation under the stable W/O emulsion system. In contrast, waxes have a promoting effect on hydrate formation in the O/W emulsion system under high-content conditions. The impact of waxes on hydrate formation remained uncertain in the absence of emulsifiers due to the disordered distribution of oil and water when no emulsifier was present. Eight potential forms of wax crystals in oil-water mixtures are summarized, and their influence on hydrate formation is discussed. The coexistence of multiple forms of wax will lead to complex results in terms of the effect of wax on hydrates. Existing research's shortcomings and challenges are emphasized, which can be expected to allow rapid development of this direction.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213765"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty analysis for CO2 geological storage due to reservoir heterogeneity based on stochastic numerical model","authors":"Lisong Zhang ,&nbsp;Menggang Jiang ,&nbsp;Qingchun Yang ,&nbsp;Yinghui Bian ,&nbsp;Chuanyin Jiang","doi":"10.1016/j.geoen.2025.213772","DOIUrl":"10.1016/j.geoen.2025.213772","url":null,"abstract":"<div><div>Due to uncertainties of permeability and porosity in actual heterogeneous saline aquifers, CO₂ geological storage exhibit the significant uncertainties for the critical parameters, such as horizontal migration distance, CO₂ dissolved amount, gas CO₂ amount per unit distribution volume and pore pressure. To investigate uncertainties for CO₂ storage, Sequential Gaussian method was introduced to generate stochastic fields of porosity and permeability, under which the governing equation for CO₂ geological storage was re-derived, to establish the stochastic numerical model, which was validated by theoretical solution by comparing CO₂ horizontal migration distance. By conducting the stochastic simulation, the effect of heterogeneity on CO₂ storage was concluded with the faster horizontal migration rate, farther horizontal migration distance, higher CO₂ dissolved amount, lower gas CO₂ amount per unit distribution volume and relatively smaller pore pressure compared to homogeneous model, indicating that the homogeneous model may be not accurate enough and would result in significant deviations in predicting the critical parameters for CO₂ storage in actual heterogeneous aquifers. Furthermore, the total of 100,000 times of stochastic numerical simulations was performed to determine uncertainties of critical parameters for CO₂ storage. The probability distributions and the expected values were obtained for critical parameters for CO₂ storage, and the confidence intervals of critical parameters were obtained under the confidence level of 95 %.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213772"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Friction characteristics and grinding model of cemented carbide for drilling bit affected by WC grain size and rotational speed","authors":"Weiguo Zhang ,&nbsp;Jun Tian ,&nbsp;Hongbo Liu ,&nbsp;Jinyun Yuan ,&nbsp;Nengzhong Lei ,&nbsp;Yulin Wang ,&nbsp;Xiang Sun ,&nbsp;Xiaowei Wu ,&nbsp;Junpeng Xu","doi":"10.1016/j.geoen.2025.213770","DOIUrl":"10.1016/j.geoen.2025.213770","url":null,"abstract":"<div><div>Rotary drilling remains a common geological drilling technique, and drilling costs have increased significantly due to the wear of cemented carbide (WC/Co) drill bits. Material selection and optimization of drilling parameters are effective ways to reduce bit wear. To investigate the wear pattern of cemented carbide materials for drill bits applicable to different soft and hard formations under different rotational speeds, and to provide references for reducing the wear of drill bits from the perspectives of material selection and drilling rotational speed selection. In this study, the effect on WC/Co drill bit wear rate (<em>W</em>_<em>r</em>) of WC grain size and rotational speed is investigated. The findings indicate a positive correlation between <em>W</em>_<em>r</em> and rotational speed, whereas the coefficient of friction (<em>f</em>_<em>c</em>) displays a negative correlation with rotational speed. The G0.5, G1, and G1.5 have the highest wear rates at rotational speeds of up to 500 r/min, which are 0.38 %, 0.55 % and 0.63 %, respectively. The results demonstrated a positive correlation between the grain size of WC in WC/Co and <em>W</em>_<em>r</em>. The <em>W</em>_<em>r</em> of G1.5 reaches the maximum at rotational speeds (i.e.,100, 200, 300, 400, and 500 r/min), with 0.19 %, 0.21 %, 0.29 %, 0.52 %, and 0.63 %, respectively. The WC/Co material, which exhibits a fine WC grain size, displays a low <em>f</em>_<em>c</em>. It is observed that as the rotational speed and WC grain size increased, the width and depth of the wear marks also increased. The oxide layer produced by frictional heat generation has a friction-reducing effect at low speeds, but at high speeds, the oxide layer will peel off. Compared to WC/Co with coarse WC grains, fine WC grains retain their particle integrity during wear, this results in a reduction in the temperature rise caused by the friction of WC fragments. A grinding model of WC/Co is constructed, which better predicts the change rule of <em>W</em>_<em>r</em> with rotational speed and WC grain size. The results offer a theoretical foundation for the selection of drill bit materials and drilling process parameters.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213770"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new interpretation approach to detect induced fracture opening with pressure transient analysis of step-rate tests","authors":"Joshua Mugisha ,&nbsp;Anton Shchipanov ,&nbsp;Alf Midtbø Øverland","doi":"10.1016/j.geoen.2025.213759","DOIUrl":"10.1016/j.geoen.2025.213759","url":null,"abstract":"<div><div>Step-Rate Test (SRT) is a way to monitor induced fracturing and fracture opening after it was created. Nowadays, many modern wells are equipped with permanent downhole gauges (PDG) that provide real-time measurements such as pressure and temperature that has revolutionized well testing and monitoring. Permanent pressure monitoring and flow-metering enable interpretation of flowing periods in combination with shut-ins, providing insights from the start of operations and widely used for monitoring of induced fracturing and fracture opening through time-lapse SRTs during injection operations. This paper proposes a new interpretation approach for early detection of induced fracturing by developing further existing SRT analysis practices using the advantage of permanent pressure measurements available now in many wells.</div><div>This paper proposes a new SRT interpretation approach for early detection of induced fracturing or fracture opening using Pressure Transient Analysis (PTA). Proposed PTA-SRT approach is based on step-by-step technique introduced previously for reservoir flow evaluations and is developed further and tested in this study for induced fracture monitoring using a combination of existing analytical and numerical flow simulations. The flow simulations have revealed a new specific signature of induced fracture creation or opening in the Bourdet derivative of flowing transients, which is further used as theoretical basis of the PTA-SRT approach. This study suggests the concept of uncertainty envelope for practical applications of PTA-SRT for on-the-fly fracture monitoring separating the signature of induced fracture from measurement noise. The PTA-SRT approach has been tested and verified on a real SRT data from a vertical well injecting water in a sandstone reservoir confirming the capabilities of the PTA-SRT approach for early detection of induced fracture opening. The paper concludes with potential application areas of the interpretation approach for well and reservoir containment monitoring in different industries including automated workflows.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213759"},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel stochastic simulation method for sedimentary facies based on the generative adversarial network with a spatially-adaptive conditioning module and comprehensive attention mechanisms","authors":"Lei Liu ,&nbsp;Dali Yue ,&nbsp;Wei Li ,&nbsp;Degang Wu ,&nbsp;Jian Gao ,&nbsp;Qian Zhong ,&nbsp;Wurong Wang ,&nbsp;Jiagen Hou","doi":"10.1016/j.geoen.2025.213758","DOIUrl":"10.1016/j.geoen.2025.213758","url":null,"abstract":"<div><div>Accurate characterization of sedimentary facies using limited observations is essential for reservoir development. Subsurface observations are crucial inputs for sedimentary facies simulation, serving both as constraints and indispensable prior knowledge. Effectively preserving and utilizing this valuable prior information during the simulation is an urgent issue. Furthermore, the complexity and variability of subsurface facies models present significant challenges to comprehensively focus on critical features and accurately reproduce geological patterns. In this work, we propose an innovative stochastic simulation method for complex sedimentary facies based on the generative adversarial network (GAN) integrating with a spatially-adaptive conditioning module (SPACM) and comprehensive attention mechanisms (CAMs), named CSPA-CAGAN. The SPACM is specifically designed to adaptively modulate extracted geological feature maps based on the layout of sparse conditioning data, thereby adequately propagating the conditioning information through the network and significantly enhancing conditional facies modeling. Additionally, CAMs, comprising various attention mechanisms, are employed to comprehensively capture key spatial patterns, feature channels, and multi-scale coordinate features, improving the ability to characterize complex sedimentary facies. The performance of the proposed method is validated through experiments on fluvial and deltaic reservoirs. Statistical metrics, including facies proportion distributions, multi-dimensional scaling plots, connectivity functions, and variograms, are employed to quantitatively evaluate the generated realizations. The evaluation results demonstrate that the realizations successfully reproduce various geological patterns, proving that our method can accurately reconstruct heterogeneous sedimentary facies models with superior pattern diversity.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213758"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation on rock-breaking and debris transport of high-pressure water jet drilling using the discrete element method (DEM)","authors":"Qi Zeng ,&nbsp;Xianpeng Yang ,&nbsp;Jie Tu ,&nbsp;Huaizhou Wei ,&nbsp;Xiaohong Yuan ,&nbsp;Can Cai ,&nbsp;Hao Chen","doi":"10.1016/j.geoen.2025.213750","DOIUrl":"10.1016/j.geoen.2025.213750","url":null,"abstract":"<div><div>To address the problems of low drilling efficiency and high cost in deep formation drilling, a new method combining high-pressure water jet and PDC (polycrystalline diamond compact) tools is proposed. However, the rock-breaking and debris transport of composite rock-breaking technology of high-pressure water jets and PDC cutter in jet drilling are still unknown. In this paper, a numerical model of high-pressure water jet and PDC tooth composite rock-breaking is established by the discrete element method and verified with experimental results. Combined with the research results, the rock-breaking mechanism is investigated from the perspectives of crack extension, interfacial friction angle and cutting force. Additionally, the effects of water jet parameters (jet velocity, jet diameter, jet angle, and striking distance) and confining pressure on composite rock-breaking are investigated. Research results show that as jet velocity increases, the impact force on the rock also increases, resulting in greater pit depth and diameter in the impact area, indicating that water jets can cause pre-damage to the rock; The optimal jet parameters are 75° jet angle, 50 m/s jet velocity, 1–1.5 mm jet diameter, and 10–15 mm stand-off distance, which was 10–15 times of the nozzle diameter, respectively; Applying a certain axial confining pressure can improve the efficiency of rock-breaking, and axial confining pressure is easier to load in the range of 0–10 MPa. The above research can provide theoretical support and technical guidance for composite rock-breaking, which is helpful for the improvement of water jet drilling technology and the design of composite drill bits.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"249 ","pages":"Article 213750"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning framework for history matching CO2 storage with 4D seismic and monitoring well data","authors":"Nanzhe Wang,&nbsp;Louis J. Durlofsky","doi":"10.1016/j.geoen.2025.213736","DOIUrl":"10.1016/j.geoen.2025.213736","url":null,"abstract":"<div><div>Geological carbon storage entails the injection of megatonnes of supercritical CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> into subsurface formations. The properties of these formations are usually highly uncertain, which makes design and optimization of large-scale storage operations challenging. In this paper we introduce a history matching strategy that enables the calibration of formation properties based on early-time observations. Early-time assessments are essential to assure the operation is performing as planned. Our framework involves two fit-for-purpose deep learning surrogate models that provide predictions for in-situ monitoring well data and interpreted time-lapse (4D) seismic saturation data. These two types of data are at very different scales of resolution, so it is appropriate to construct separate, specialized deep learning networks for their prediction. This approach results in a workflow that is more straightforward to design and more efficient to train than a single surrogate that provides global high-fidelity predictions. The deep learning models are integrated into a hierarchical Markov chain Monte Carlo (MCMC) history matching procedure. History matching is performed on a synthetic case with and without 4D seismic data, which allows us to quantify the impact of 4D seismic on uncertainty reduction. The use of both data types is shown to provide substantial uncertainty reduction in key geomodel parameters and to enable accurate predictions of CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> plume dynamics. The overall history matching framework developed in this study represents an efficient way to integrate multiple data types and to assess the impact of each on uncertainty reduction and performance predictions.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"248 ","pages":"Article 213736"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital rock super-resolution reconstruction with efficient 3D spatial-adaptive feature modulation network","authors":"Jinye Wang ,&nbsp;Yongfei Yang ,&nbsp;Fugui Liu ,&nbsp;Lei Zhang ,&nbsp;Hai Sun ,&nbsp;Junjie Zhong ,&nbsp;Kai Zhang ,&nbsp;Jun Yao","doi":"10.1016/j.geoen.2025.213748","DOIUrl":"10.1016/j.geoen.2025.213748","url":null,"abstract":"<div><div>High-quality digital rock images are important for studying the micropore structure and flow characteristics of reservoirs, these images should be characterized by high resolution and large field of view (FOV). However, due to the limited imaging capability of the hardware equipment, high resolution and large FOV are often in conflict with each other. The super-resolution (SR) reconstruction techniques, which can extract features from low-resolution images to restore high-resolution details, are currently the main means of improving image resolution. For reconstructing high-quality 3D digital rock images, we propose a new 3D Spatial-Adaptive Feature Modulation Network (3DSAFMN), which inherits the spatial modelling capability of Transformer, fuses the multi-scale input information, and accomplishes the optimization of efficiency and accuracy. The evaluation results show that compared with the current advanced deep learning algorithm, the number of parameters of 3DSAFMN is reduced by 45.5%, the reconstruction speed is increased by 1.70 times, and the reconstruction effect is better. Visualization shows that 3DSAFMN can eliminate noise and blur to the maximum extent and highlight valuable features such as pores, fractures and minerals. Furthermore, we apply 3DSAFMN to external sandstone samples to verify the generalization ability of the model. The pore structure parameters calculation and direct flow simulation demonstrate that the reconstruction results are very close to the real samples in terms of both geometric topology and connectivity. In summary, this work provides an effective and reliable novel model based on deep learning for resolution enhancement of digital rock images.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"248 ","pages":"Article 213748"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of drilling rate based on genetic algorithms and machine learning models","authors":"Fang Shi ,&nbsp;Hualin Liao ,&nbsp;Shuaishuai Wang ,&nbsp;Omar Alfarisi ,&nbsp;Fengtao Qu","doi":"10.1016/j.geoen.2025.213747","DOIUrl":"10.1016/j.geoen.2025.213747","url":null,"abstract":"<div><div>During the oil and gas exploration phase, the drilling rate is a key indicator for assessing efficiency, and its accurate prediction is crucial for optimizing exploration and production. By constructing multiple data-driven intelligent drilling rate prediction models, including Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), LassoCV, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM), and combining them with Genetic Algorithm (GA) to explore the globally optimal model parameter combinations, the accuracy of drilling rate predictions is enhanced. The models are compared and analyzed based on Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²), with GA-LGBM identified as the optimal intelligent drilling rate prediction model. The GA-LGBM model demonstrated good generalization ability and robustness in field tests on two wells. SHapley Additive exPlanations (SHAP) plots are used to analyze the contribution and impact of parameter features on the predictions. Adjustments to positively impactful parameters are made to optimize the drilling rate. Additionally, two-dimensional contour plots illustrate the variation trends of drilling rate under different Weight on Bit (WOB) and RPM conditions, providing reliable data support and visual guidance for optimizing drilling rate. This research provides engineers with reliable data support and strategic guidance, aiding them in strategy control and optimal parameter adjustments for drilling operations under complex conditions.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"247 ","pages":"Article 213747"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time series production forecasting of natural gas based on transformer neural networks","authors":"Mandella Ali M. Fragalla,&nbsp;Wei Yan,&nbsp;Jingen Deng,&nbsp;Liang Xue,&nbsp;Fathelrahman Hegair,&nbsp;Wei Zhang,&nbsp;Guangcong Li","doi":"10.1016/j.geoen.2025.213749","DOIUrl":"10.1016/j.geoen.2025.213749","url":null,"abstract":"<div><div>Time series forecasting of gas production plays a crucial role in enhancing the stability of production, optimizing development strategies, and effectively increase the life cycle of gas wells. However, the precision of these forecasts is often compromised by two primary factors: (1) the complexity and randomness inherent in production time series data and (2) the limited ability to model dependencies within temporal sequences, especially in the context of long-term, multi-step forecasts, which can lead to instability in the prediction model's results. To address these challenges, this paper introduces a novel method. Initially, Multilevel Discrete Wavelet Decomposition (MDWD) is employed to mitigate the raw gas production series' instability, complexity, and randomness. This is achieved by decomposing the input signals into their respective periodic and trend components. Subsequently, gas production modeling is executed using transformer neural networks equipped with a multi-head attention mechanism to learn sequential dependencies effectively, irrespective of the temporal distance. The architecture of this model is built upon an encoder-decoder framework. The encoder is designed to generate representations of historical gas production sequences of any length, while the decoder can generate arbitrarily long future gas production sequences. The interconnection between the encoder and decoder through the multi-head attention mechanism is a crucial aspect of this model. In two distinct experiments focusing on gas filed production data, the RMSE for one-step forecasting results produced by the proposed method was remarkably low, at 0.1911 and 0.3816, respectively. Moreover, the RMSE for 7-day multi-step predictions stood at 1.7358 and 1.2146, respectively, showcasing significant improvements over other methods. With accurate results of multi-step forecasting, this work contributes to the effective utilization of conventional and unconventional energy resources.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"248 ","pages":"Article 213749"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}