Geoenergy Science and Engineering最新文献

{"title":"Rheological and displacement performance of modified nano-SiO2 grafted polymeric system for enhanced oil recovery","authors":"Ousseini Seidina Ousseini ,&nbsp;Bo Peng ,&nbsp;Zhuang Miao ,&nbsp;Kai Cheng ,&nbsp;Jingwei Li ,&nbsp;Muhammad Faisal Altaf ,&nbsp;Ibrahim Issaka Ramatou ,&nbsp;Moussa Z. Salim ,&nbsp;Xuechao Yang ,&nbsp;Honfeng Zhang ,&nbsp;Weifeng Lv","doi":"10.1016/j.geoen.2025.214213","DOIUrl":"10.1016/j.geoen.2025.214213","url":null,"abstract":"<div><div>Polymer flooding is a widely used enhanced oil recovery technique, yet its efficiency is frequently limited by viscosity loss, shear degradation, and poor salt tolerance under harsh reservoir conditions. to address these challenges, this study presents a novel hybrid polymer nanocomposite, synthesized by grafting KH570-modified nano-SiO₂ into a poly(acrylamide-co-acrylic acid-co-2-acrylamido-2-methylpropanesulfonic acid) backbone. This molecular architecture has not been previously reported in EOR applications and provides dual advantages, such as robust covalent bonding between polymer and nanoparticle, and significantly enhanced nanoparticle dispersion within the polymer matrix. Comprehensive characterization using FTIR, SEM, and rheological analyses confirmed successful grafting and improved structural integrity. Unlike conventional polymer solutions, the new nanocomposite exhibited a 23 % higher viscosity retention in 10 g/L NaCl solutions, demonstrating excellent salt resistance and shear stability. Dynamic oscillation tests revealed significantly improved viscoelastic properties, suggesting superior performance in controlling mobility and minimizing viscous fingering. Core flooding experiments achieved an impressive oil recovery increase of 41–46 %, surpassing typical polymer flooding systems and highlighting the material's potential for improved sweep efficiency and conformance control in high salinity reservoirs. These results represent a significant breakthrough in polymer EOR technology, demonstrating that KH570-modified nano-SiO₂ grafting can effectively overcome traditional polymer limitations and offers promising pathway toward next generation EOR agents capable of maintaining performance under challenging reservoir conditions.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214213"},"PeriodicalIF":4.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049991","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":"Volume change pattern and mechanism of CO2 + petroleum hydrocarbon binary system under reservoir temperature and pressure conditions","authors":"Qizhi Shao ,&nbsp;Dan Yang ,&nbsp;Ke Bei ,&nbsp;Xiangyong Zheng ,&nbsp;Min Zhao ,&nbsp;Zhiyan Pan ,&nbsp;I-Ming Chou ,&nbsp;Zhenmin Jin ,&nbsp;Fengchang Wu","doi":"10.1016/j.geoen.2025.214211","DOIUrl":"10.1016/j.geoen.2025.214211","url":null,"abstract":"<div><div>In the oil sector, understanding the volume change patterns and mechanisms of the CO₂ + petroleum hydrocarbon (PH) system is critical, as it enhances comprehension of the dynamics governing the CO₂ + raw petroleum system. This study measured the volume expansion (VE) of several typical mixtures, including CO₂ + pentylcyclopentane (PCP), CO₂ + pentylbenzene (PB), and CO₂ + pentylcyclohexane (PCH), under reservoir temperature and pressure conditions. The variation pattern of VE in the CO₂ + PH system was analyzed, revealing a decrease with increasing carbon numbers of the alkyl side chain and cycloalkane. Furthermore, the VE of the CO₂ + phenyl mixture was found to be greater than that of the CO₂ + cyclohexyl mixture, and the VE of the CO₂ + n-alkane mixture was greater than that of the CO₂ + cycloalkane mixture at the same carbon number. Additionally, in situ Raman spectroscopy was employed to investigate the dissolution and expansion mechanisms. The Raman peak positions and shapes of the C–H stretching band, C–C stretching band, and CO₂ Fermi diad in the CO₂ + PH mixtures exhibited a blue shift and shape change as the VE increased, which was attributed to interactions between CO₂ and PH molecules. The bandwidth of the Fermi diad increased (indicating a redshift of the Fermi diad), suggesting the presence of intermolecular C–H•••O interactions, which are believed to be the primary mechanism responsible for CO₂ dissolving in PH and leading to significant VE.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214211"},"PeriodicalIF":4.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106279","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":"Modeling and experimental studies on high-velocity slug and churn gas-liquid upward flows in vertical pipes","authors":"Rida Elgaddafi ,&nbsp;Ramadan Ahmed ,&nbsp;Raj Kiran ,&nbsp;Saeed Salehi","doi":"10.1016/j.geoen.2025.214210","DOIUrl":"10.1016/j.geoen.2025.214210","url":null,"abstract":"<div><div>Two-phase gas-liquid flow in vertical pipes is widely encountered in various industrial applications including oil and gas, nuclear, and petrochemical sectors. In petroleum production, it occurs in wellbores, risers, and pipelines, where pressure variations significantly influence flow patterns. Gas release from the liquid phase due to pressure reduction leads to distinct two-phase flow regimes, which, if not properly controlled, may result in blowout incidents. A comprehensive understanding of flow characteristics, including liquid holdup, void fraction, and pressure drop, is crucial for accurately modeling Worst-Case Discharge (WCD) and predicting wellhead pressure. This knowledge facilitates the estimation of hydrocarbon flow rates and the development of effective well control strategies to mitigate blowout risks.</div><div>This study aims to improve the understanding of gas-liquid slug, and churn flows in vertical pipes under high superficial gas and liquid velocities (V_Sg: 8.5–70 m/s, V_Sl: 0.12–2.89 m/s), conditions that remain insufficiently characterized in the existing literature. A combination of experimental and theoretical approaches was employed to investigate these flow regimes. Air-water two-phase experiments were conducted in an 83 mm vertical stainless steel pipe equipped with a transparent section, allowing direct visual observation of flow patterns using a high-speed digital camera. To improve the prediction of slug and churn flow behavior at elevated gas and liquid velocities, new mechanistic models are developed by integrating hydrodynamic frameworks from existing mechanistic models, refining the description of flow dynamics.</div><div>The study identified high-velocity slug and churn flow patterns within the investigation section of the experimental setup, which aligned with established flow maps. The pressure gradient behavior was found to vary with superficial gas and liquid velocities, revealing a critical transition point where the dominant flow mechanism shifts from gravitational to frictional forces. The proposed models significantly enhance the accuracy of pressure gradient predictions, with discrepancies generally remaining below 20 % and a maximum deviation of 15 % for churn flow predictions. However, at high gas velocities exceeding 30 m/s, the models exhibit an overestimation of liquid holdup by up to 64 %, particularly near the churn-annular transition. Improved accuracy in liquid holdup predictions is achieved by averaging the predictions from the churn and annular flow models, emphasizing the necessity of combining flow-specific models to enhance reliability in transitional flow conditions.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214210"},"PeriodicalIF":4.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049988","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 hybrid physics augmented predictive model for friction pressure loss in hydraulic fracturing process based on experimental and field data","authors":"Abdulrahman Abdulwarith ,&nbsp;Mohamed Ammar ,&nbsp;Sarkis Kakadjian ,&nbsp;Nathan McLaughlin ,&nbsp;Birol Dindoruk","doi":"10.1016/j.geoen.2025.214212","DOIUrl":"10.1016/j.geoen.2025.214212","url":null,"abstract":"<div><div>Accurate estimation of frictional pressure losses is critical for optimizing hydraulic fracturing in applications such as geothermal operations, carbon capture and sequestration (CCS), and hydrocarbon production from tight systems, where excessive pressure drops in the casing/tubing and near-wellbore regions can hinder fluid placement and potentially lead to screen-outs. Total pressure losses during stimulation treatments are composed of wellbore friction losses, occurring inside the casing or/and tubing and near-wellbore losses attributed to perforation entry and formation tortuosity. Overall, having efficient wells, especially for CCS and geothermal applications, will lead to minimizing surface footprint (especially in cases of adverse conditions of low injectivity in CCS) and maximizing energy extraction with fewer wells in geothermal developments. In this study, a hybrid physics-augmented, data-driven model is developed to predict these frictional losses with high accuracy, using both laboratory and field data. The model leverages flow loop experimental results performed with various friction reducers (FR) types, concentrations, and flow rates to quantify wellbore pressure losses. These lab findings are then integrated with field data collected from multiple hydraulic fracturing stages across different wells, incorporating parameters such as fluid and proppant properties, FR concentrations, slurry rates, perforation geometry (diameter and density), and formation characteristics. This dual-model approach enables real-time forecasting of total friction losses and wellhead pressures during operations. Validation against actual field data shows strong agreement between predicted and measured pressures, confirming the model’s reliability for operational decision-making. By using flow loop tests to train the model, the need for extensive repeat experimentation is significantly reduced, allowing for robust sensitivity analyses and parametric optimization. Importantly, this methodology applies not only to hydraulic fracturing but also to geothermal stimulation processes, where similar high-rate fluid injection challenges exist. The model supports more precise fluid and additive management, improves pump efficiency, and enhances design reliability, ultimately leading to reduced surface footprint, lower resource consumption, and more sustainable stimulation operations. This integrated, physics-informed machine learning framework offers a practical and scalable solution for predicting frictional losses across diverse stimulation scenarios, contributing to improved operational efficiency, cost savings, and reduced environmental impact in both unconventional oil and gas and geothermal applications. This article is an extended version of our paper (<span><span>Abdulwarith et al., 2024</span></span>).</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214212"},"PeriodicalIF":4.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106281","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":"Multiscale wavelet-adversarial learning eliminates imaging artifacts in digital rock analysis for reliable reservoir evaluation","authors":"Guoli Ma ,&nbsp;Zegen Wang ,&nbsp;Bing Su ,&nbsp;Bin Wei ,&nbsp;Guobin Jiang","doi":"10.1016/j.geoen.2025.214214","DOIUrl":"10.1016/j.geoen.2025.214214","url":null,"abstract":"<div><div>Generative super-resolution (SR) reconstruction models are widely applied in digital rock research to balance the trade-off between image resolution and the scanning device's field of view. Existing methods often enhance visual details or structural fidelity separately. However, they fail to balance these goals effectively. This failure frequently leads to artifacts that distort porosity and permeability measurements. This paper proposes the Stationary and Discrete Wavelet-Enhanced Generative Adversarial Network (SDWGAN). The model is a hybrid SR approach that integrates two wavelet decomposition methods. This integration addresses the challenge effectively. By integrating multi-scale frequency constraints from wavelet decomposition with adversarial training focused on high-frequency components, our method effectively distinguishes rock boundary details from imaging artifacts. The proposed model adopts a global-local feature integration architecture to preserve fine-grained textures and macroscopic structures. Experimental results on the DeepRock-SR dataset (carbonate, sandstone, coal) demonstrate SDWGAN's enhancements: 0.63–2.12 dB PSNR and 0.01–0.11 SSIM improvements in fidelity, alongside 0.001–0.005 LPIPS and 0.62 NIQE gains in perceptual quality over RGB-domain loss-based models. Simulated seepage results indicate that SDWGAN estimates porosity and permeability with 98 % similarity to the reference images. In conclusion, the proposed model manages the perception-distortion trade-off via frequency domain optimization, ensuring petrophysical consistency between SR results and benchmarks. This approach offers a novel and reliable method for reservoir characterization in the field of petroleum geology.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214214"},"PeriodicalIF":4.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060617","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":"The impact of ScCO2 exposure on the imbibition of shale and the potential for water block removal","authors":"Nianjie Kuang ,&nbsp;Junping Zhou ,&nbsp;Xuefu Xian ,&nbsp;Hongzhang Wang ,&nbsp;Jinyuan Zhang ,&nbsp;Zhengjie Liu ,&nbsp;Zixuan Huo ,&nbsp;Chenghao Xu ,&nbsp;Yifan Peng ,&nbsp;Huaquan Jiang","doi":"10.1016/j.geoen.2025.214207","DOIUrl":"10.1016/j.geoen.2025.214207","url":null,"abstract":"<div><div>Shale gas production in its later stages is severely limited by water block due to the retention of massive fracturing fluids in the reservoir. Effective water block removal is critical for improving shale gas production. Supercritical CO₂ (ScCO₂) has emerged as a promising fluid for alleviating water block in shale; however, its potential and mechanisms remain poorly understood. This study investigates the changes in shale imbibition behavior induced by ScCO₂ exposure, and verifies its water blockage removal effect. Then, from a microscopic perspective, the water block removal mechanisms by ScCO₂ exposure were clarified. The results reveal that ScCO₂ exposure promotes the imbibition capability of shale, especially accelerating both its initial spontaneous imbibition rate and subsequent diffusion rate, which can be attributed to ScCO₂ exposure-induced formation of new corrosion pores and microfractures, as well as increases in pore size, total pore volume, and specific surface area. Consequently, the hydrophilicity of shale is reinforced, which facilitates the diffusion of fracturing fluid. As water saturation within the reservoir decreases, the flow resistance of gas in shale reservoir accordingly decreased, then the water block was alleviated. This study provides new insight for understanding the mechanism of ScCO₂ enhanced shale gas recovery.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214207"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049989","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":"Leveraging machine learning to predict water flowback in tight-gas wells: Insights from low-pressure reservoirs in the Ordos Basin","authors":"Yishan Cheng ,&nbsp;Yingkun Fu ,&nbsp;Zhiping Li ,&nbsp;Kui Hong","doi":"10.1016/j.geoen.2025.214190","DOIUrl":"10.1016/j.geoen.2025.214190","url":null,"abstract":"<div><div>Tight-gas wells are commonly reported with varying water-flowback volumes after hydraulic fracturing. The key features which determine the varying water-flowback volume remain unclear. Also, predicting water-flowback volume is of great importance for fracturing/production optimization and reservoir management in tight-gas fields. This study assembles a large dataset comprising 18 geological, fracturing, and flowback features from 3579 tight-gas wells, and presents a machine-learning (ML) workflow to predict water flowback volume for target wells in the Ordos Basin. The workflow mainly involves six steps: dataset assembly and preprocessing, correlation analysis, train/test split, algorithm testing, scenario analysis, and feature importance analysis. Application of the ML workflow demonstrated that the Random Forest algorithm outperformed Multiple Linear Regression, Neural Networks, Support Vector Machines, and XGBoost in both predictive accuracy and computation cost. Also, reliable predictions were achieved by using an optimal combination of key features, including total injected water volume, proppant amount, formation thickness, perforation depth, well type, liquid nitrogen volume, and permeability. Additionally, we employ the ML workflow to determine the optimized nitrogen volume for the fracturing treatments of target wells. This work provides a practical ML-based tool for predicting water flowback, and may help guide the optimizations of fracturing design and flowback strategies in the target field.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214190"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158379","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":"Predicting permeability of natural gas hydrate reservoir based on machine learning assisted by sparrow search algorithm","authors":"Cheng Lu ,&nbsp;Jiuyu Zhao ,&nbsp;Yuxuan Xia ,&nbsp;Yiming Sun ,&nbsp;Jianchao Cai","doi":"10.1016/j.geoen.2025.214192","DOIUrl":"10.1016/j.geoen.2025.214192","url":null,"abstract":"<div><div>Natural gas hydrate, recognized as a clean energy source, holds immense potential as a future alternative to conventional oil and gas. The permeability of gas hydrate reservoirs is a crucial parameter for the exploration and development of these resources, with accurate calculation being vital for reservoir evaluation and natural gas extraction. The gas hydrate reservoir in the South China Sea is characterized by strong heterogeneity, significant permeability variation, and stress sensitivity, posing challenges for accurate modeling. In this study, we develop machine learning models enhanced by the Sparrow Search Algorithm to predict the absolute and dynamic permeability of clayey silt samples in natural gas hydrate reservoirs. Our results indicate that the optimal support vector regression, random forest, and deep neural network (DNN) models, optimized by <span>Sparrow Search Algorithm</span>, closely align with lattice Boltzmann method simulation results in terms of root mean square error and correlation coefficient. Among the pore structure parameters, porosity, average throat radius, average coordination number, and average pore radius emerge as the primary controlling factors of absolute permeability. In the dynamic permeability prediction model, the DNN model not only accurately learns the permeability variation process following pressure adjustments but also provides values closest to the experimental data. Incorporating pore structure parameters further enhances the prediction accuracy of dynamic permeability. The relative error of the DNN model is lower than that of semi-empirical permeability models, underscoring its superiority. Key factors for dynamic permeability include time, average pore radius, and flow rate.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214192"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106182","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":"Research on wellbore trajectory control of Rotary Steerable System using back-propagation neural network-fuzzy method","authors":"Nan Zhang ,&nbsp;Fei Li ,&nbsp;Baian Ren ,&nbsp;Ziqi Liu ,&nbsp;Deming Wang ,&nbsp;Junhao Chen ,&nbsp;Fangxing Lyu","doi":"10.1016/j.geoen.2025.214201","DOIUrl":"10.1016/j.geoen.2025.214201","url":null,"abstract":"<div><div>The core tasks of wellbore trajectory control are to control inclination and azimuth of steerable drilling tool. During the drilling process, RSS (Rotary Steerable System) constantly changes the target inclination and azimuth. The non-intelligent downhole closed-loop control method often leads to larger hysteresis of wellbore trajectory control and an increase in non-productive time. This study proposed a control methodology for RSS downhole closed-loop control, which combined a back-propagation neural network with a fuzzy control system (BP-Fuzzy). This paper also investigated the control method of PID, fuzzy, and BP. In the simulation experiments, both inclination and azimuth assigned new targets, and the performance of four control methods were evaluated with a RSS dynamic model. Furthermore, in the simulation, the fuzzy method initializes control parameters using PID values, while the BP-Fuzzy method adopts the same fuzzy rules as the fuzzy method and the same neural network structure as the BP method. Therefore, the simulation experiments are methodically sequential and control other variables. In multiple simulations, BP-Fuzzy method shows better control effect in response speed, overshoot, steady-state error and disturbance resistance. Finally, a three-dimensional drilling trajectory, encompassing vertical drilling, build-up, and horizontal drilling, was planned and implemented, with random disturbance introduced throughout the process. The BP-Fuzzy method exhibited superior performance in tracking the target attitude and demonstrated enhanced disturbance suppression capabilities during the entire drilling operation. This method can be applied to downhole closed-loop control to enhance the automatic performance of RSS and establish the foundation for future autonomous drilling.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214201"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060618","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":"Statistical assessment of optimization algorithms in empirical correlations for enhancing methane hydrate equilibrium predictions","authors":"Mohammed S. Asif,&nbsp;R. Asaletha","doi":"10.1016/j.geoen.2025.214194","DOIUrl":"10.1016/j.geoen.2025.214194","url":null,"abstract":"<div><div>In the pursuit of a low carbon future, natural gas emerges as a promising energy resource. One environmentally conscious and non-explosive approach to efficient natural gas storage is through its preservation in clathrate hydrates, renowned for their remarkable volumetric storage capacity. In the gas and oil industry, gas hydrates, which often form in pipelines causing flow blockages, present significant flow assurance challenges. Accurate estimation of gas hydrate formation conditions is essential for maximizing clathrate hydrate storage potential, mitigating hydrate related issues, and addressing varied industrial and environmental needs. This paper assesses some of the existing empirical correlations and subjects them to statistical analysis for identifying the most suitable model for calculating methane (CH₄) hydrate equilibrium in pure water, particularly for storage applications. Statistical analysis reveals that these correlations excel in localized pressure conditions but exhibit limitations in high pressure environments. To address this challenge, optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are employed to optimize the correlation coefficients. The modified correlations resulting from optimization are compared with their original counterparts, demonstrating enhancements in predictive accuracy.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"257 ","pages":"Article 214194"},"PeriodicalIF":4.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049987","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}