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Research on physical explosion crater model of high-pressure natural gas pipeline

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petrOLEUM RESEARCH 2024年第3期9卷 432-438页

作者： Wang, Dongyuan Xu, Shuiying Wang, Zhao Zhou, Yazhe Gao, Na china petr Pipeline Engn corp Langfang Peoples R China Pipechina Engn Technol Innovat co ltd Tianjin Peoples R China Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing Peoples R China china petr Pipeline Bur Engn co ltd Int Business Dept Langfang Peoples R China

In this study, Hypermesh and LS-DYNA numerical simulation software are used to build a multi domain coupling model of natural gas pipeline, including soil, pipeline, TNT explosive and air domain, and the non-reflection boundary conditions are set for the model. The TNT equivalent method is used to convert the physical explosion amount of natural gas pipeline into 1387.38 kg TNT explosive amount. The simulation results show that the physical explosion of pipeline forms an approximate elliptical crater with a width of 12.68 m and a depth of 4.12 m; the TNT equivalent of the model is corrected by comparing the crater simulation value and the size value of the crater calculated by the PRCI empirical formula under the same laying condition, and the correction coefficient is selected as 0.9, and the corrected TNT equivalent is 1248.64 kg; the modified model crater size is 3.72 m deep and 12.66 m wide, compared with the crater size obtained from the field test, the error of crater depth and width calculated by the modified model simulation is 5.7% and 15.5% respectively. (c) 2024 The Authors. Publishing services provided by Elsevier B.V. on behalf of KeAi communication co. ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： High-pressure natural gas Pipeline Physical explosion Crater Numerical simulation

Applicability of deep neural networks for lithofacies classification from conventional well logs: An integrated approach

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petrOLEUM RESEARCH 2024年第3期9卷 393-408页

作者： Khan, Saud Qadir Kirmani, Farzain Ud Din Mari petr co ltd Islamabad Pakistan NFC Inst Engn & Technol Dept Petr & Gas Engn Multan Pakistan

Parametric understanding for specifying formation characteristics can be perceived through conventional approaches. Significantly, attributes of reservoir lithology are practiced for hydrocarbon exploration. Well logging is conventional approach which is applicable to predict lithology efficiently as compared to geophysical modeling and petrophysical analysis due to cost effectiveness and suitable interpretation time. However, manual interpretation of lithology identification through well logging data requires domain expertise with an extended length of time for measurement. Therefore, in this study, Deep Neural Network (DNN) has been deployed to automate the lithology identification process from well logging data which would provide support by increasing time-effective for monitoring lithology. DNN model has been developed for predicting formation lithology leading to the optimization of the model through the thorough evaluation of the best parameters and hyperparameters including the number of neurons, number of layers, optimizer, learning rate, dropout values, and activation functions. Accuracy of the model is examined by utilizing different evaluation metrics through the division of the dataset into the subdomains of training, validation and testing. Additionally, an attempt is contributed to remove interception for formation lithology prediction while addressing the imbalanced nature of the associated dataset as well in the training process using class weight. It is assessed that accuracy is not a true and only reliable metric to evaluate the lithology classification model. The model with class weight recognizes all the classes but has low accuracy as well as a low F1-score while LSTM based model has high accuracy as well as a high F1-score. (c) 2024 The Authors. Publishing services provided by Elsevier B.V. on behalf of KeAi communication co. ltd. This is an open access article under the CC BY-NC-ND license (.

关键词： Lithology identification Deep learning LSTM Imbalanced dataset conventional well logs MACHINE LEARNING-METHODS RESERVOIR CHARACTERIZATION FACIES CLASSIFICATION MULTILAYER PERCEPTRON FORMATION LITHOLOGY IDENTIFICATION PREDICTION

Depositional Environment and Origin of Inertinite-rich coal in the Ordos Basin

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Acta Geologica Sinica(English Edition) 2024年第4期98卷 1064-1085页

作者： SHI Qingmin ZHAO Jun JI Ruijun XUE Weifeng HAN Bo CAI Yue LI Chunhao CUI Shidong college of Geology and Environment Xi’an University of Science and TechnologyXi’an 710054China Shaanxi coal Chemical Industry Technology Research Institute co. Ltd.Xi’an 710199China china National logging corporation Geological Research InstituteXi’an 710077China

Inertinite-rich coal is widely distributed in the Ordos Basin,represented by the No.2 coal seam of the Middle Jurassic Yan'an *** paper combined coal petrology and geochemistry to analyze the origin of inertinite,changes in the coal-forming environment and control characteristics of *** has shown that there are two forms of inertinite sources in the study *** typical fusinization,wildfire events also play a substantial role in inertinite *** are significant fluctuations in the coal-forming environment of samples at different *** samples were formed in dry forest swamp with low water levels and strong oxidation,which have a high inertinite content,and the samples formed in wet forest swamp and limnic showed low inertinite ***,the inertinite content of different origins does not fully correspond to the depositional environment characterized by dryness and *** inertinites were significantly influenced by climatic conditions,while pyrofusinite was not entirely controlled by climatic conditions but rather directly impacted by wildfire *** high oxygen level was the main factor causing widespread wildfire ***,the combination of wildfire activity and oxidation generates a high content of inertinite in the Middle Jurassic coal of the Ordos Basin.

关键词： inertinite-rich coal wildfire coal petrology geochemistry biomarkers depositional environment

A salt-induced tackifying polymer for enhancing oil recovery in high salinity reservoirs: Synthesis, evaluation, and mechanism

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GREEN ENERGY & ENVIRONMENT 2024年第11期9卷 1747-1758页

作者： Wu, Yining Li, Peihan Yan, Bin Li, Xiaohan Huang, Yongping Yuan, Juncong Feng, Xiang Dai, Caili china Univ petr East china Dept Petr Engn Qingdao 266580 Peoples R China Sichuan Univ Coll Biomass Sci & Engn Dept Natl Engn Lab Clean Technol Leather Manufacture Chengdu 610065 Peoples R China Princeton Univ Dept Civil & Environm Engn Princeton NJ 08544 USA china Univ petr East china Dept Chem Engn Qingdao 266580 Peoples R China

Polymer flooding is an effective method widely applied for enhancing oil recovery (EOR) by reducing the mobility ratio between the injected water and crude oil. However, traditional polymers encounter challenges in high salinity reservoirs due to their salt sensitivity. To overcome this challenge, we synthesized a zwitterion polymer (PAMNS) with salt-induced tackifying property through copolymerization of acrylamide and a zwitterion monomer, methylacrylamide propyl-N, N-dimethylbutylsulfonate (NS). NS monomer is obtained from the reaction between 1,4-butanesultone and dimethylamino propyl methylacrylamide. In this study, the rheological properties, salt responsiveness, and EOR efficiency of PAMNS were evaluated. Results demonstrate that PAMNS exhibits desirable salt-induced tackifying characteristics, with viscosity increasing up to 2.4 times as the NaCl concentration reaches a salinity of 30 x 10(4) mg L-1. Furthermore, high valence ions possess a much stronger effect on enhancing viscosity, manifested as Mg2+ > Ca2+ > Na+. Molecular dynamics simulations (MD) and fluid dynamics experiment results demonstrate that PAMNS molecules exhibit a more stretched state and enhanced intermolecular associations in high-salinity environments. It is because of the salt-induced tackifying, PAMNS demonstrates superior performance in polymer flooding experiments under salinity ranges from 5 x 10(4) mg L-1 to 20 x 10(4) mg L-1, leading to 10.38-19.83% higher EOR than traditional polymers. (c) 2023 Institute of Process Engineering, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi communications co., ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： High salinity reservoirs Salt-induced tackifying polymer Anti-polyelectrolyte behavior Enhanced oil recovery ZWITTERIONIC POLYMER BRUSHES SURFACE WATER BEHAVIOR

Sedimentary microfacies of Member 5 of Xujiahe Formation in the Dongfengchang area, Sichuan Basin

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petrOLEUM RESEARCH 2024年第3期9卷 481-488页

作者： Huang, Yanqing Wang, Jian Liu, Junlong Duan, Nan Xiao, Kaihua Qiao, Dawei Li, Yan Wang, Ai Sinopec petr Explorat & Prod Res Inst Beijing 100083 Peoples R China Sinopec Southwest Oil & Gas co Chengdu 610095 Sichuan Peoples R China

Member 5 of the Upper Triassic Xujiahe Formation (T3X5) in central Sichuan Basin has made a breakthrough in exploration recently. However, this new stratum has not been investigated sufficiently with respect to basic geology, making its types and distribution of sedimentary facies unclear, which severely restricts its subsequent exploration evaluation. In this study, types of sedimentary microfacies in the first sand group of T3X5 (T3X51) are clarified through core observation and logging interpretation using core, log and seismic data, and then distribution of sedimentary microfacies in T(3)X(5)(1 )is determined according to seismic waveform features and seismic prediction. The results show that T(3)X(5)(1 )in the Dongfengchang area is mainly composed of deltaic deposits of several microfacies, such as delta front underwater distributary channel, sheet sand, and interdistributary bay. On seismic sections, different microfacies are significantly different in waveform features, the underwater distributary channel is characterized by one trough between two peaks, while diversion bay exhibits chaotic reflections between T6 and T-5(1). The sedimentary microfacies varied greatly during the depositional period of T(3)X(5)(1)in the Dongfengchang area, this is because that the sediment supply was mainly controlled by the southwest and southeast provenance regions. Three superimposed underwater distributary channels are developed in the Dongfengchang area. The phase-1 superimposed underwater distributary channel in the northwest transition to sheet sand in the northeast, the phase-2 superimposed underwater distributary channel in the south extends shortly, the phase-3 superimposed underwater distributary channel in the northeast has a large development scale. These research findings are helpful to guide the subsequent exploration of T3X5 gas reservoir and also theoretically significant for investigating the depositional evolution of the Xujiahe Formation in central Sichuan B

关键词： Seismic facies Sedimentary microfacies Xujiahe formation Upper triassic Dongfengchang area Central Sichuan Basin

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,china

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Energy Geoscience

Energy Geoscience 2024年第4期5卷 240-252页

作者： Yang Li Xiaoguang Li Mingyu Guo Chang Chen Pengbo Ni Zijian Huang Liaohe Oilfield CNPCPanjinLiaoning124010China Tianjin company of china National Offshore Oil corporation Tianjin300452China china-France Bohai Geoservices co. Ltd.Tianjin300450China Panjin Zhonglu Oil and Gas Technology Service co. Ltd.PanjinLiaoning124000China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is *** study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical *** a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological *** linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct *** Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of *** Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and *** the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative *** Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk *** Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend *** analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring it

关键词： Regression analysis Oil and gas exploration Multiple linear regression model Nonlinear regression model Hydrocarbon loss recovery Porosity prediction

Anisotropy measurements and characterization of the Qingshankou shale

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Applied Geophysics 2024年第3期21卷 468-478,617页

作者： Li Qing-feng Yan Xue-hong Yan Wei-lin Ren Li Wang Peng Han Jian-qiang Xia Xue Chen Hao Daqing Oilfield Exploration Division Daqing163000HeilongjiangChina Daqing Branch company of CNPC logging Daqing163000HeilongjiangChina Exploration and Development Research Institute of Daqing Oilfield co. Ltd.Daqing163712China State Key Laboratory of Acoustics Institute of AcousticsChinese Academy of SciencesBeijing100190China

Qingshankou shale(Gulong area,china)exhibits strong acoustic anisotropy characteristics,posing significant challenges to its exploration and *** this study,the five full elastic constants and multipole response law of the Qingshankou shale were studied using experimental *** show that the anisotropy parametersϵandγin the study region are greater than 0.4,whereas the anisotropy parameterδis smaller,generally *** simulations show that the longitudinal and transverse wave velocities of these strong anisotropic rocks vary significantly with inclination angle,and significant differences in group velocity and phase velocity are also *** logging measures the group velocity in dipped boreholes;this differs from the phase velocity to some *** the dip angle increases,the longitudinal and SH wave velocities increase accordingly,while the qSV-wave velocity initially increases and then decreases,reaching its maximum value at a dip of approximately 40°.These results provide an effective guide for the correction and modeling of acoustic logging time differences in the region.

关键词： Transverse isotropy anisotropy parameter sonic logging group velocity phase velocity

Passive neutron multiplicity device for^(240)Pu measurement based on FPGA

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Nuclear Science and Techniques 2024年第9期35卷 141-154页

作者： Yan Zhang Hao-Ran Zhang Ren-Bo Wang Ming-Yu Li Rui Chen Hai-Tao Wang Xiang-Ting Meng Shu-Min Zhou Bin Tang Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China University of TechnologyNanchang 330013China Engineering Research Center for Nuclear Technology Application Ministry of EducationEast China University of TechnologyNanchang 330013China Pan china Detect Technology co.ltd. Nanchang 330013China

A passive neutron multiplicity measurement device,FH-NCM/S1,based on field-programmable gate arrays(FPGAs),is developed specifically for measuring the mass of plutonium-240(^(240)Pu)in mixed oxide ***-NCM/S1 adopts an inte-grated approach,combining the shift register analysis mode with the pulse-position timestamp mode using an *** optimal effective length of the^(3)He neutron detector was determined to be 30 cm,and the thickness of the graphite reflector was ascertained to be 15 cm through MCNP *** fabricating the device,calibration measurements were per-formed using a^(252)Cf neutron source;a detection efficiency of 43.07%and detector die-away time of 55.79μs were *** samples of plutonium oxide were measured under identical conditions using the FH-NCM/S1 in shift register analysis mode and a plutonium waste multiplicity *** obtained double rates underwent corrections for detection efficiency(ε)and double gate fraction(f_(d)),resulting in corrected double rates(D_(c)),which were used to validate the accuracy of the shift register analysis ***,the device exhibited fluctuations in the measurement results,and within a single 20 s measurement,these fluctuations remained below 10%.After 30 cycles,the relative error in the mass of^(240)Pu was less than 5%.Finally,correlation calculations confirmed the robust consistency of both measurement *** study holds specific significance for the subsequent design and development of neutron multiplicity devices.

关键词： Spent fuel Non-destructive assay Neutron multiplicity ^(240)Pu FPGA

Identification of carbonate sedimentary facies from well logs with machine learning Author links open overlay panel

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petroleum Research 2024年第2期9卷 165-175页

作者： Xianmu Hou Peiqing Lian Jiuyu Zhao Yun Zai Weiyao Zhu Fuyong Wang School of Civil and Resources Engineering University of Science and Technology BeijingBeijing100083China Sinopec Chongqing Fuling Shale Gas Exploration and Development co. Ltd.Chongqing408000China Sinopec petroleum Exploration&Production Research Institute SINOPECBeijing100083China National Key Laboratory of petroleum Resources and Engineering China University of Petroleum(Beijing)Beijing102249China ChinKunlun Digital Technology co.ltd. Beijing102206China

Sedimentary facies identification is critical for carbonate oil and gas reservoir *** traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long *** carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective *** many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic *** paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is ***,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well ***,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning *** performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are *** other two wells are used for model *** research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respect

关键词： Carbonate Sedimentary facies Well logs Machine learning Deep learning