Pub Date : 2025-10-01DOI: 10.1016/j.petsci.2025.05.028
Qi-Xuan Liang , Feng Zhang , Jun-Ting Fan , Dong-Ming Liu , Yun-Bo Zhou , Qing-Chuan Wang , Di Zhang
Neutron well logging, using instruments equipped with neutron source and detectors (e.g., 3He-tubes, NaI, BGO), plays a key role in lithological differentiation, porosity determination, and fluid property evaluation in the petroleum industry. The growing trend of multifunctional neutron well logging, which enables simultaneous extraction of multiple reservoir characteristics, requiring high-performance detectors capable of withstanding high-temperature downhole conditions, limited space, and instrument vibrations, while also detecting multiple particle types. The Cs2LiYCl6:Ce3+ (CLYC) elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency, making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology. This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration. The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency. A double-particle pulsed neutron detection system for porosity determination was established, based on the count ratio of equivalent gamma rays from the range of 2.95–3.42 MeVee energy bins. Results showed that CLYC can effectively replace 3He-tubes for porosity measurement, providing consistent responses. This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.
{"title":"A study on numerical simulation method of Cs2LiYCl6:Ce3+ detection response in neutron well logging","authors":"Qi-Xuan Liang , Feng Zhang , Jun-Ting Fan , Dong-Ming Liu , Yun-Bo Zhou , Qing-Chuan Wang , Di Zhang","doi":"10.1016/j.petsci.2025.05.028","DOIUrl":"10.1016/j.petsci.2025.05.028","url":null,"abstract":"<div><div>Neutron well logging, using instruments equipped with neutron source and detectors (e.g., <sup>3</sup>He-tubes, NaI, BGO), plays a key role in lithological differentiation, porosity determination, and fluid property evaluation in the petroleum industry. The growing trend of multifunctional neutron well logging, which enables simultaneous extraction of multiple reservoir characteristics, requiring high-performance detectors capable of withstanding high-temperature downhole conditions, limited space, and instrument vibrations, while also detecting multiple particle types. The Cs<sub>2</sub>LiYCl<sub>6</sub>:Ce<sup>3+</sup> (CLYC) elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency, making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology. This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration. The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency. A double-particle pulsed neutron detection system for porosity determination was established, based on the count ratio of equivalent gamma rays from the range of 2.95–3.42 MeVee energy bins. Results showed that CLYC can effectively replace <sup>3</sup>He-tubes for porosity measurement, providing consistent responses. This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 10","pages":"Pages 4052-4064"},"PeriodicalIF":6.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.petsci.2025.07.018
Shan Wu , Hong-Kui Ge , Xiao-Qiong Wang , Ke Gao , Hai Ding
Hydraulic fracturing techniques are commonly used to enhance the production of tight reservoirs. Generally, the effect of hydraulic fracturing can be appraised through hydraulic fracturing experiments in the laboratory, in which acoustic emission (AE) is often used to monitor the fracturing process. At present, the number of AE events and spatial distribution of AE locations are the two main factors commonly considered in hydraulic fracturing effectiveness evaluation. However, these commonly used evaluation methods overlook two crucial aspects: the connectivity among fractures and the tensile and shear properties of fractures induced by hydraulic fracturing. In this technical note, we consider the influence of these two previously overlooked aspects on the evaluation of hydraulic fracturing effectiveness by establishing a connected fracture model using AE data. The proposed approach links up AE events based on their spatio-temporal relationship and builds a fracture network called the connection model. Then, the characteristic of the fracture network is represented by the fractal dimension to reveal the complexity of fractures in the network. We extract the tensile-shear properties of each fracture based on the inversion of AE events’ focal mechanism. Finally, based on the pre-known fracturing effectiveness of a fracture network, we compare the connection model of AE events in several triaxial hydraulic experiments. Our findings indicate that a comprehensive evaluation of hydraulic fracturing effectiveness can be achieved by considering both the connectivity of AE locations and the tensile-shear properties of AE events. This work aims to provide a more rational method for characterizing rock fracture networks and evaluating rock fracturing effects using AE data.
{"title":"Evaluating hydraulic fracturing effectiveness based on the improved connection model of acoustic emission events","authors":"Shan Wu , Hong-Kui Ge , Xiao-Qiong Wang , Ke Gao , Hai Ding","doi":"10.1016/j.petsci.2025.07.018","DOIUrl":"10.1016/j.petsci.2025.07.018","url":null,"abstract":"<div><div>Hydraulic fracturing techniques are commonly used to enhance the production of tight reservoirs. Generally, the effect of hydraulic fracturing can be appraised through hydraulic fracturing experiments in the laboratory, in which acoustic emission (AE) is often used to monitor the fracturing process. At present, the number of AE events and spatial distribution of AE locations are the two main factors commonly considered in hydraulic fracturing effectiveness evaluation. However, these commonly used evaluation methods overlook two crucial aspects: the connectivity among fractures and the tensile and shear properties of fractures induced by hydraulic fracturing. In this technical note, we consider the influence of these two previously overlooked aspects on the evaluation of hydraulic fracturing effectiveness by establishing a connected fracture model using AE data. The proposed approach links up AE events based on their spatio-temporal relationship and builds a fracture network called the connection model. Then, the characteristic of the fracture network is represented by the fractal dimension to reveal the complexity of fractures in the network. We extract the tensile-shear properties of each fracture based on the inversion of AE events’ focal mechanism. Finally, based on the pre-known fracturing effectiveness of a fracture network, we compare the connection model of AE events in several triaxial hydraulic experiments. Our findings indicate that a comprehensive evaluation of hydraulic fracturing effectiveness can be achieved by considering both the connectivity of AE locations and the tensile-shear properties of AE events. This work aims to provide a more rational method for characterizing rock fracture networks and evaluating rock fracturing effects using AE data.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 10","pages":"Pages 4145-4156"},"PeriodicalIF":6.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.05.021
Xin Chang , Shi-Long Teng , Xing-Yi Wang , Yin-Tong Guo , Chun-He Yang
Multi-stage and multi-cluster fracturing (MMF) is a crucial technology in unconventional oil and gas development, aiming to enhance production by creating extensive fracture networks. However, achieving uniform expansion of multi-cluster hydraulic fractures (HFs) in MMF remains a significant challenge. Field practice has shown that the use of temporary plugging and diversion fracturing (TPDF) can promote the balanced expansion of multi-cluster HFs. This study conducted TPDF experiments using a true triaxial fracturing simulation system setting a horizontal well completion with multi-cluster jetting perforations to investigate the equilibrium initiation and extension of multi-cluster fractures. The influence of key parameters, including cluster spacing, fracturing fluid viscosity, differential stress, and fracturing fluid injection rate, on fracture initiation and propagation was systematically examined. The results indicate that while close-spaced multi-cluster fracturing significantly increases the number of HFs, it also leads to uneven extension of HFs in their propagation. In contrast, TPDF demonstrates effectiveness in mitigating uneven HF extension, increasing the number of HFs, and creating a larger stimulated reservoir volume, ultimately leading to improved oil and gas well productivity. Moreover, under conditions of high differential stress, the differential stress within the formation exerts a stronger guiding effect in HFs, which are more closely aligned with the minimum principal stress. Low-viscosity fluids facilitate rapid and extensive fracture propagation within the rock formation. High-volume fluid injection, on the other hand, more comprehensively fills the formation. Therefore, employing low-viscosity and high-volume fracturing is advantageous for the initiation and extension of multi-cluster HFs.
{"title":"Enhancing uniformity of multi-fracture propagation by temporary plugging and diversion fracturing in a horizontal well with multi-cluster perforations","authors":"Xin Chang , Shi-Long Teng , Xing-Yi Wang , Yin-Tong Guo , Chun-He Yang","doi":"10.1016/j.petsci.2025.05.021","DOIUrl":"10.1016/j.petsci.2025.05.021","url":null,"abstract":"<div><div>Multi-stage and multi-cluster fracturing (MMF) is a crucial technology in unconventional oil and gas development, aiming to enhance production by creating extensive fracture networks. However, achieving uniform expansion of multi-cluster hydraulic fractures (HFs) in MMF remains a significant challenge. Field practice has shown that the use of temporary plugging and diversion fracturing (TPDF) can promote the balanced expansion of multi-cluster HFs. This study conducted TPDF experiments using a true triaxial fracturing simulation system setting a horizontal well completion with multi-cluster jetting perforations to investigate the equilibrium initiation and extension of multi-cluster fractures. The influence of key parameters, including cluster spacing, fracturing fluid viscosity, differential stress, and fracturing fluid injection rate, on fracture initiation and propagation was systematically examined. The results indicate that while close-spaced multi-cluster fracturing significantly increases the number of HFs, it also leads to uneven extension of HFs in their propagation. In contrast, TPDF demonstrates effectiveness in mitigating uneven HF extension, increasing the number of HFs, and creating a larger stimulated reservoir volume, ultimately leading to improved oil and gas well productivity. Moreover, under conditions of high differential stress, the differential stress within the formation exerts a stronger guiding effect in HFs, which are more closely aligned with the minimum principal stress. Low-viscosity fluids facilitate rapid and extensive fracture propagation within the rock formation. High-volume fluid injection, on the other hand, more comprehensively fills the formation. Therefore, employing low-viscosity and high-volume fracturing is advantageous for the initiation and extension of multi-cluster HFs.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3688-3708"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.06.013
De-Tao Zhou , Zhao-Peng Zhu , Tao Pan , Xian-Zhi Song , Shi-Jie Xiao , Gen-Sheng Li , Cheng-Kai Zhang , Chen-Zhan Zhou , Zi-Yue Zhang
As oil and gas exploration continues to progress into deeper and unconventional reservoirs, the likelihood of kick risk increases, making kick warning a critical factor in ensuring drilling safety and efficiency. Due to the scarcity of kick samples, traditional supervised models perform poorly, and significant fluctuations in field data lead to high false alarm rates. This study proposes an unsupervised graph autoencoder (GAE)-based kick warning method, which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates. The method utilizes the GAE model to process time-series data during drilling, accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features. To further reduce false alarms, the weighted dynamic time warping (WDTW) algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling, with real-time updates applied to prevent normal conditions from being misclassified as kick risk. Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7% and significantly reduces the false alarm rate. The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers, demonstrating its high sensitivity and robustness. After integrating the WDTW algorithm and real-time updates, the false alarm rate is reduced from 17.3% to 5.6%, further improving the accuracy of kick warnings. The proposed method provides an efficient and reliable approach for kick warning in drilling operations, offering strong practical value and technical support for the intelligent management of future drilling operations.
{"title":"An unsupervised intelligent warning model for drilling kick risk based on multi-temporal feature coupling","authors":"De-Tao Zhou , Zhao-Peng Zhu , Tao Pan , Xian-Zhi Song , Shi-Jie Xiao , Gen-Sheng Li , Cheng-Kai Zhang , Chen-Zhan Zhou , Zi-Yue Zhang","doi":"10.1016/j.petsci.2025.06.013","DOIUrl":"10.1016/j.petsci.2025.06.013","url":null,"abstract":"<div><div>As oil and gas exploration continues to progress into deeper and unconventional reservoirs, the likelihood of kick risk increases, making kick warning a critical factor in ensuring drilling safety and efficiency. Due to the scarcity of kick samples, traditional supervised models perform poorly, and significant fluctuations in field data lead to high false alarm rates. This study proposes an unsupervised graph autoencoder (GAE)-based kick warning method, which effectively reduces false alarms by eliminating the influence of field engineer operations and incorporating real-time model updates. The method utilizes the GAE model to process time-series data during drilling, accurately identifying kick risk while overcoming challenges related to small sample sizes and missing features. To further reduce false alarms, the weighted dynamic time warping (WDTW) algorithm is introduced to identify fluctuations in logging data caused by field engineer operations during drilling, with real-time updates applied to prevent normal conditions from being misclassified as kick risk. Experimental results show that the GAE-based kick warning method achieves an accuracy of 92.7% and significantly reduces the false alarm rate. The GAE model continues to operate effectively even under conditions of missing features and issues kick warnings 4 min earlier than field engineers, demonstrating its high sensitivity and robustness. After integrating the WDTW algorithm and real-time updates, the false alarm rate is reduced from 17.3% to 5.6%, further improving the accuracy of kick warnings. The proposed method provides an efficient and reliable approach for kick warning in drilling operations, offering strong practical value and technical support for the intelligent management of future drilling operations.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3613-3626"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.05.006
Ying-Hao Zuo , Zhao-Yun Zong , Xing-Yao Yin , Kun Li , Ya-Ming Yang , Si Wu
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development. However, the anisotropic reflection coefficient equation, based on the transverse isotropy with a vertical axis of symmetry (VTI) medium assumption, involves numerous parameters to be inverted. This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset (AVO) inversion results. In this study, a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten, which reduces the equation's dimensionality and increases its stability. Additionally, the traditional Markov Chain Monte Carlo (MCMC) inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution, limiting the algorithm's convergence and sample randomness. To address these limitations and evaluate the uncertainty of AVO inversion, the IADR-Gibbs algorithm is proposed, which incorporates the Independent Adaptive Delayed Rejection (IADR) algorithm with the Gibbs sampling algorithm. Grounded in Bayesian theory, the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection (DR) strategy. Rejected samples are then added to the support points to update the proposal distribution function adaptively. The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion. The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
流体识别和各向异性参数表征是页岩储层勘探开发的关键。然而，各向异性反射系数方程是基于介质具有垂直对称轴（VTI）的横向各向同性假设，涉及许多需要反演的参数。这种复杂性降低了AVO反演结果的稳定性，影响了AVO反演结果的精度。本文提出了一种包含流体项和Thomsen各向异性参数的新型各向异性方程，降低了方程的维数，提高了方程的稳定性。此外，传统的Markov Chain Monte Carlo （MCMC）反演算法对随机样本的拒绝率较高，且依赖于已知的参数分布（如高斯分布），限制了算法的收敛性和样本的随机性。为了解决AVO反演的局限性，评估AVO反演的不确定性，提出了独立自适应延迟抑制（IADR）算法与Gibbs采样算法相结合的IADR-Gibbs算法。该算法以贝叶斯理论为基础，引入支点构造非参数分布的建议分布，并根据延迟拒绝策略重新选择被拒绝的样本。然后将被拒绝的样本添加到支撑点，以自适应地更新提案分布函数。方程重写法和IADR-Gibbs算法提高了AVO反演的精度和鲁棒性。通过综合采集试验和实际数据应用，验证了该方法的有效性和适用性。
{"title":"Bayesian AVO inversion of fluid and anisotropy parameters in VTI media using IADR-Gibbs algorithm","authors":"Ying-Hao Zuo , Zhao-Yun Zong , Xing-Yao Yin , Kun Li , Ya-Ming Yang , Si Wu","doi":"10.1016/j.petsci.2025.05.006","DOIUrl":"10.1016/j.petsci.2025.05.006","url":null,"abstract":"<div><div>Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development. However, the anisotropic reflection coefficient equation, based on the transverse isotropy with a vertical axis of symmetry (VTI) medium assumption, involves numerous parameters to be inverted. This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset (AVO) inversion results. In this study, a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten, which reduces the equation's dimensionality and increases its stability. Additionally, the traditional Markov Chain Monte Carlo (MCMC) inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution, limiting the algorithm's convergence and sample randomness. To address these limitations and evaluate the uncertainty of AVO inversion, the IADR-Gibbs algorithm is proposed, which incorporates the Independent Adaptive Delayed Rejection (IADR) algorithm with the Gibbs sampling algorithm. Grounded in Bayesian theory, the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection (DR) strategy. Rejected samples are then added to the support points to update the proposal distribution function adaptively. The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion. The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3565-3582"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.06.010
Shu-Da Zhao , Baojun Bai , Thomas Schuman
Re-crosslinkable preformed particle gel (RPPG) has been considered to be one of the most promising gels for dealing with fracture and void space conduit (VSC) conformance problems. However, the dehydration of RPPG during its propagation in the fracture-type features and its effect on gel properties remains unclear. This paper investigates the dehydration behavior during RPPG propagating in an open fracture using matrix-free fracture model. Then the results were verified using real fractured sandstone core model. Moreover, the gel properties after extruding a fracture were studied in detail including gel dehydration and gel strength. Results reveal that the RPPG properties changed significantly with increasing propagation distance, which correlated with the gel injection rate. At high gel injection rates, the dehydration and gel strength () decrease with increasing propagation distance.
In contrast, the opposite result was found at low injection rates. Based on the study of the different gel injection rates, it is found that dehydration time is another key factor affecting dehydration behavior. Results also indicate that the fracture width affects gel dehydration at different locations. Dehydration was more pronounced at narrow fractures but only in the inlet section, while in the outlet section, RPPG contains more water than the initial condition. This study has profound implications for field applications. It provides new insights into the transport of RPPG in fractures and helps field engineers to optimize the gel injection operations.
{"title":"Experimental study of dehydration performance of re-crosslinkable preformed particle gel during extruding through an open fracture","authors":"Shu-Da Zhao , Baojun Bai , Thomas Schuman","doi":"10.1016/j.petsci.2025.06.010","DOIUrl":"10.1016/j.petsci.2025.06.010","url":null,"abstract":"<div><div>Re-crosslinkable preformed particle gel (RPPG) has been considered to be one of the most promising gels for dealing with fracture and void space conduit (VSC) conformance problems. However, the dehydration of RPPG during its propagation in the fracture-type features and its effect on gel properties remains unclear. This paper investigates the dehydration behavior during RPPG propagating in an open fracture using matrix-free fracture model. Then the results were verified using real fractured sandstone core model. Moreover, the gel properties after extruding a fracture were studied in detail including gel dehydration and gel strength. Results reveal that the RPPG properties changed significantly with increasing propagation distance, which correlated with the gel injection rate. At high gel injection rates, the dehydration and gel strength (<span><math><mrow><msup><mi>G</mi><mo>′</mo></msup></mrow></math></span>) decrease with increasing propagation distance.</div><div>In contrast, the opposite result was found at low injection rates. Based on the study of the different gel injection rates, it is found that dehydration time is another key factor affecting dehydration behavior. Results also indicate that the fracture width affects gel dehydration at different locations. Dehydration was more pronounced at narrow fractures but only in the inlet section, while in the outlet section, RPPG contains more water than the initial condition. This study has profound implications for field applications. It provides new insights into the transport of RPPG in fractures and helps field engineers to optimize the gel injection operations.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3760-3769"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.05.029
Fei Zhao , Jin Lai , Zong-Li Xia , Zhong-Rui Wang , Ling Li , Bin Wang , Lu Xiao , Yang Su , Gui-Wen Wang
Coaly source rocks have attracted considerable attention for their significant hydrocarbon generation potential in recent years. However, limited study is performed on utilizing geochemical data and well log data to evaluate coaly hydrocarbon source rocks. In this study, geochemical data and well log data are selected from two key wells to conduct an evaluation of coaly hydrocarbon source rocks of Jurassic Kezilenuer Formation in Kuqa Depression of Tarim Basin. Initially, analysis was focused on geochemical parameters to assess organic matter type, source rock quality, and hydrocarbon generation potential. Lithology types of source rocks include mudstone, carbonaceous mudstone and coal. The predominant organic matter type identified was Type III and Type II2, indicating a favorable hydrocarbon generation potential. Well log data are integrated to predict total organic carbon (TOC) content, and the results indicate that multiple regression method is effective in predicting TOC of carbonaceous mudstone and coal. However, the ΔlgR method exhibited limited predictive capability for mudstone source rock. Additionally, machine learning methods including multilayer perceptron neural network (MLP), random forest (RF), and extreme gradient boosting (XGBoost) techniques are employed to predict TOC of mudstone source rock. The XGBoost performs best in TOC prediction with correlation coefficient (R2) of 0.9517, indicating a close agreement between measured and predicted TOC values. This study provides a reliable prediction method of coaly hydrocarbon source rocks through machine learning methods, and will provide guidance for resource assessment.
{"title":"Coaly source rock evaluation using well logs: The Jurassic Kezilenuer Formation in Kuqa Depression, Tarim Basin, China","authors":"Fei Zhao , Jin Lai , Zong-Li Xia , Zhong-Rui Wang , Ling Li , Bin Wang , Lu Xiao , Yang Su , Gui-Wen Wang","doi":"10.1016/j.petsci.2025.05.029","DOIUrl":"10.1016/j.petsci.2025.05.029","url":null,"abstract":"<div><div>Coaly source rocks have attracted considerable attention for their significant hydrocarbon generation potential in recent years. However, limited study is performed on utilizing geochemical data and well log data to evaluate coaly hydrocarbon source rocks. In this study, geochemical data and well log data are selected from two key wells to conduct an evaluation of coaly hydrocarbon source rocks of Jurassic Kezilenuer Formation in Kuqa Depression of Tarim Basin. Initially, analysis was focused on geochemical parameters to assess organic matter type, source rock quality, and hydrocarbon generation potential. Lithology types of source rocks include mudstone, carbonaceous mudstone and coal. The predominant organic matter type identified was Type III and Type II<sub>2</sub>, indicating a favorable hydrocarbon generation potential. Well log data are integrated to predict total organic carbon (TOC) content, and the results indicate that multiple regression method is effective in predicting TOC of carbonaceous mudstone and coal. However, the ΔlgR method exhibited limited predictive capability for mudstone source rock. Additionally, machine learning methods including multilayer perceptron neural network (MLP), random forest (RF), and extreme gradient boosting (XGBoost) techniques are employed to predict TOC of mudstone source rock. The XGBoost performs best in TOC prediction with correlation coefficient (<em>R</em><sup>2</sup>) of 0.9517, indicating a close agreement between measured and predicted TOC values. This study provides a reliable prediction method of coaly hydrocarbon source rocks through machine learning methods, and will provide guidance for resource assessment.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3599-3612"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.05.024
Hang-Xin Cai , Jun Jin , Er-Ting Li , Zhong-Da Zhang , Shuang Yu , Chang-Chun Pan
Most oil reservoirs that were found in the Junggar Basin are located in the Mahu sag and neighboring areas. Oil sources and classifications remain unresolved in this region. Oil source assessment can be partially inconsistent on the basis of different molecular and isotopic parameters. In the present study, classifications for the 92 studied oils from the Mahu sag and neighboring areas were performed using chemometric analysis, e.g., hierarchical cluster analysis (HCA) and principal component analysis (PCA) on the basis of integration of sixteen facies parameters. These parameters consist of isotope reversal index (RI), δ13C of n-C25, Ph/n-C18, β-carotane/n-C21, six terpane ratios of Ts/C23 tricyclic terpanes, Ts/(C28+C29 tricyclic terpanes), C29 Ts/C23 tricyclic terpanes, C29Ts/(C28+C29 tricyclic terpanes), C30 diahopane/C23 tricyclic terpane and C30 diahopane/(C28+C29 tricyclic terpanes), and six ratios of polynuclear aromatic hydrocarbons (PAH) including trimethylnaphthalenes (TMNs)/(TMNs + phenanthrene (Phen)), tetramethylnaphthalenes (TeMNs)/(TeMNs + Phen), TMNs/(TMNs + methylphenanthrenes (MPs)), TeMNs/(TeMNs + MPs), TMNs/(TMNs + chrysene (Ch)) and TeMNs/(TeMNs + Ch). These sixteen parameters are mainly influenced by source facies and less influenced by maturity as demonstrated in the crossplots of these sixteen parameters versus concentrations of C30 hopane. Oil classifications are more reliable and convenient using chemometric analysis (HCA and PCA) integrating the sixteen facies parameters, compared with using crossplots of two parameters or star charts of several parameters. The 92 oils are classified into three groups using HCA and PCA, i.e., Group I, II and III. Group I and II oils are derived from source rocks within the Lower Permian Fengcheng Formation (P1f) and Middle Permian Lower Wuerhe Formation (P2w), respectively. Group III oils are mixtures of Group I and II oils. Group I consists of fifty oils mainly located at the northeastern and central areas of the Mahu sag with only three oils at the southwestern area of the Mahu sag. Group II consists of fourteen oils at the southwestern area of the Mahu sag. Group III consists of twenty-eight oils located at the southwestern and central areas of the Mahu sag. Locations of Group I, II and III oils reflect the distributions of effective source rocks containing oil-prone Type I/II kerogen within the Fengcheng (P1f) and Lower Wuerhe formations (P2w).
{"title":"Chemometric differentiation of oil families in the Mahu sag, Junggar Basin, NW China","authors":"Hang-Xin Cai , Jun Jin , Er-Ting Li , Zhong-Da Zhang , Shuang Yu , Chang-Chun Pan","doi":"10.1016/j.petsci.2025.05.024","DOIUrl":"10.1016/j.petsci.2025.05.024","url":null,"abstract":"<div><div>Most oil reservoirs that were found in the Junggar Basin are located in the Mahu sag and neighboring areas. Oil sources and classifications remain unresolved in this region. Oil source assessment can be partially inconsistent on the basis of different molecular and isotopic parameters. In the present study, classifications for the 92 studied oils from the Mahu sag and neighboring areas were performed using chemometric analysis, e.g., hierarchical cluster analysis (HCA) and principal component analysis (PCA) on the basis of integration of sixteen facies parameters. These parameters consist of isotope reversal index (RI), <em>δ</em><sup>13</sup>C of <em>n</em>-C<sub>25</sub>, Ph/<em>n</em>-C<sub>18</sub>, β-carotane/<em>n</em>-C<sub>21</sub>, six terpane ratios of Ts/C<sub>23</sub> tricyclic terpanes, Ts/(C<sub>28</sub>+C<sub>29</sub> tricyclic terpanes), C<sub>29</sub> Ts/C<sub>23</sub> tricyclic terpanes, C<sub>29</sub>Ts/(C<sub>28</sub>+C<sub>29</sub> tricyclic terpanes), C<sub>30</sub> diahopane/C<sub>23</sub> tricyclic terpane and C<sub>30</sub> diahopane/(C<sub>28</sub>+C<sub>29</sub> tricyclic terpanes), and six ratios of polynuclear aromatic hydrocarbons (PAH) including trimethylnaphthalenes (TMNs)/(TMNs + phenanthrene (Phen)), tetramethylnaphthalenes (TeMNs)/(TeMNs + Phen), TMNs/(TMNs + methylphenanthrenes (MPs)), TeMNs/(TeMNs + MPs), TMNs/(TMNs + chrysene (Ch)) and TeMNs/(TeMNs + Ch). These sixteen parameters are mainly influenced by source facies and less influenced by maturity as demonstrated in the crossplots of these sixteen parameters versus concentrations of C<sub>30</sub> hopane. Oil classifications are more reliable and convenient using chemometric analysis (HCA and PCA) integrating the sixteen facies parameters, compared with using crossplots of two parameters or star charts of several parameters. The 92 oils are classified into three groups using HCA and PCA, i.e., Group I, II and III. Group I and II oils are derived from source rocks within the Lower Permian Fengcheng Formation (P<sub>1</sub><em>f</em>) and Middle Permian Lower Wuerhe Formation (P<sub>2</sub><em>w</em>), respectively. Group III oils are mixtures of Group I and II oils. Group I consists of fifty oils mainly located at the northeastern and central areas of the Mahu sag with only three oils at the southwestern area of the Mahu sag. Group II consists of fourteen oils at the southwestern area of the Mahu sag. Group III consists of twenty-eight oils located at the southwestern and central areas of the Mahu sag. Locations of Group I, II and III oils reflect the distributions of effective source rocks containing oil-prone Type I/II kerogen within the Fengcheng (P<sub>1</sub><em>f</em>) and Lower Wuerhe formations (P<sub>2</sub><em>w</em>).</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3530-3547"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.06.015
Wei-Wei Xu , Can Yang , Wei-Lin Yu , Zhao-Zeng Liu , Qiang Li
Filler cleaning is a challenge that affects the efficient separation of FCCS particles by electrostatic methods and limits the utilization of the oil slurry. Two filler cleaning methods are proposed in this paper, the flushing desorption method and the electrostatic desorption method, where desorption is achieved by respectively applying a flow field or an electric field to the fillers immersed in a cleaning solution (ethyl acetate). Also, the “rough particle-smooth plane” contact model between particles and filler was established, and the particle force model was established by analyzing the movement of particles in the process of cleaning. Furthermore, combining the established contact model and force model, the detachment model of particles was proposed. In this model, the dimensionless number λ is used to discriminate the attachment state of particles whose validity was verified by experiments. The experimental results showed that the cleaning efficiency of flushing desorption method and electrostatic desorption method increase with the increase of flow rate and voltage, which reached 50.5% and 61.4% at 0.1 m/s and 14 kV.
{"title":"Influence of electric and flow field characteristics on the cleaning of fillers for FCCS electrostatic separation","authors":"Wei-Wei Xu , Can Yang , Wei-Lin Yu , Zhao-Zeng Liu , Qiang Li","doi":"10.1016/j.petsci.2025.06.015","DOIUrl":"10.1016/j.petsci.2025.06.015","url":null,"abstract":"<div><div>Filler cleaning is a challenge that affects the efficient separation of FCCS particles by electrostatic methods and limits the utilization of the oil slurry. Two filler cleaning methods are proposed in this paper, the flushing desorption method and the electrostatic desorption method, where desorption is achieved by respectively applying a flow field or an electric field to the fillers immersed in a cleaning solution (ethyl acetate). Also, the “rough particle-smooth plane” contact model between particles and filler was established, and the particle force model was established by analyzing the movement of particles in the process of cleaning. Furthermore, combining the established contact model and force model, the detachment model of particles was proposed. In this model, the dimensionless number <em>λ</em> is used to discriminate the attachment state of particles whose validity was verified by experiments. The experimental results showed that the cleaning efficiency of flushing desorption method and electrostatic desorption method increase with the increase of flow rate and voltage, which reached 50.5% and 61.4% at 0.1 m/s and 14 kV.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3843-3853"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.petsci.2025.08.017
Qi-Xin Liu , Jian-Jun Zhu , Hai-Bo Wang , Shuo Chen , Hao-Yu Wang , Nan Li , Rui-Zhi Zhong , Yu-Jun Liu , Hai-Wen Zhu
Timely anomaly detection is critical for optimizing gas production in plunger lift systems, where equipment failures and operational issues can cause significant disruptions. This paper introduces a two-dimensional convolutional neural network (2D-CNN) model designed to diagnose abnormal operating conditions in gas wells utilizing plunger lift technology. The model was trained using an extensive dataset comprising casing and tubing pressure measurements gathered from multiple wells experiencing both normal and anomalous operations. Input data underwent a rigorous preprocessing pipeline involving cleaning, ratio calculation, window segmentation, and matrix transformation. Employing separate pre-training and transfer learning methods, the model's efficacy was validated through stringent testing on new, previously unseen field data. Results demonstrate the model's acceptable performance and strong diagnostic capabilities on this novel data from various wells within the operational block. This confirms its potential to fulfill practical field requirements by offering guidance for adjusting production systems in plunger lift-assisted wells. Ultimately, this data-driven, automated diagnostic approach provides valuable theoretical insights and technical support for sustaining gas well production rates.
{"title":"Deep feature learning for anomaly detection in gas well deliquification using plunger lift: A novel CNN-based approach","authors":"Qi-Xin Liu , Jian-Jun Zhu , Hai-Bo Wang , Shuo Chen , Hao-Yu Wang , Nan Li , Rui-Zhi Zhong , Yu-Jun Liu , Hai-Wen Zhu","doi":"10.1016/j.petsci.2025.08.017","DOIUrl":"10.1016/j.petsci.2025.08.017","url":null,"abstract":"<div><div>Timely anomaly detection is critical for optimizing gas production in plunger lift systems, where equipment failures and operational issues can cause significant disruptions. This paper introduces a two-dimensional convolutional neural network (2D-CNN) model designed to diagnose abnormal operating conditions in gas wells utilizing plunger lift technology. The model was trained using an extensive dataset comprising casing and tubing pressure measurements gathered from multiple wells experiencing both normal and anomalous operations. Input data underwent a rigorous preprocessing pipeline involving cleaning, ratio calculation, window segmentation, and matrix transformation. Employing separate pre-training and transfer learning methods, the model's efficacy was validated through stringent testing on new, previously unseen field data. Results demonstrate the model's acceptable performance and strong diagnostic capabilities on this novel data from various wells within the operational block. This confirms its potential to fulfill practical field requirements by offering guidance for adjusting production systems in plunger lift-assisted wells. Ultimately, this data-driven, automated diagnostic approach provides valuable theoretical insights and technical support for sustaining gas well production rates.</div></div>","PeriodicalId":19938,"journal":{"name":"Petroleum Science","volume":"22 9","pages":"Pages 3803-3816"},"PeriodicalIF":6.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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