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1. Intelligent early kick recognition via knowledge-guided Bi-LSTM
Wang, Dong-Yue; Sun, Wei-Feng; Guo, Yan-Liang; Gong, Chen-Tao; Huang, Wei-Min
Pet. Sci. 23(7), 4082-4099.
Keywords: Early kick recognition; Knowledge-guided neural network; Bi-directional long short-term memory; Loss function; Knowledge embedding
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2. A novel method for drilling rate of penetration prediction and transfer generalization based on physics-informed neural networks
He, Miao; Yuan, Qing-Rui; Li, Jun; Zhou, Chang-Cheng; Liu, Jing-Wei; Chen, Xin; Cao, Jia-Hao
Pet. Sci. 23(7), 4009-4033.
Keywords: Rate of penetration; Deep learning; Physics-informed neural networks; Adaptive loss; Model generalization
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3. MSA-DETR: Multi-scale attention enhanced DETR for object detection in oilfield surveillance
Cao, Qian-Wen; Ma, Jin-Rong; Zhang, Lai-Bin
Pet. Sci. 23(5), 2686-2697.
Keywords: Object detection; Petroleum drilling safety; Multi-scale perception; Drilling engineering; Artificial intelligence
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4. A quantitative interpretation method of gas kick driven by physics-informed neural network
Yang, Hong-Wei; Wang, Biao; Li, Jun; Zhang, Geng; Liu, Gong-Hui; Zhan, Jia-Hao; Long, Zhen-Yu; Wang, Chao
Pet. Sci. 23(5), 2599-2621.
Keywords: Gas kick; Quantitative interpretation; Physics-informed neural network; Downhole dual measurement tools; Partial differential equation
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5. An integrated deep learning framework for full-cycle CCUS-EOR evaluation and optimization under carbon neutrality
Shen, Bin; Yang, Sheng-Lai; Zhang, Yi-Qi; Gao, Xin-Yuan; Bi, Lu-Fei; Du, Kai; Zhao, Er-Meng; Zeng, Hong-Bo
Pet. Sci. 23(4), 2288-2307.
Keywords: CCUS-EOR; CO2 storage; Deep learning; Tabular prior-data fit network (TabPFN); ACAM-NSGA-II; Multi-objective optimization
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6. Multi-component composite steam flooding (MCCSF) expanding steam chamber and suppressing water invasion in edge-water heavy oil reservoirs: A comparative 3D experimental study
Hong, Qing-Jing; Pang, Zhan-Xi; Tang, Peng; Liu, Xiao-Hong; Wang, Bo
Pet. Sci. 23(4), 2086-2105.
Keywords: Edge-water; Steam flooding; 3D experiments; Multi-component; Steam chamber; Image recognition; EOR mechanisms
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7. Physics-informed neural network for reconstruction of seismic data under compressed sensing sampling
Li, Yin-Shuo; Lu, Wen-Kai; Huang, Xiao-Gang; Ding, Ji-Cai; Song, Cao
Pet. Sci. 23(4), 1829-1841.
Keywords: Physics-informed neural network; Reconstruction of 3-D seismic data; Move-out transformation; Alias suppression
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8. Unconventional oil production forecasting based on PiAM meta-learning
Zhao, Yu-Long; Xiao, Qing-Yu; Zeng, Xing-Jie; Xiong, Bin; Wang, Shuai; Zhao, Song; Wei, Yun-Sheng
Pet. Sci. 23(3), 1335-1347.
Keywords: Production forecasting; Meta learning; Transformer; Time series
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9. A layer-specific constraint-based enriched physics-informed neural network for solving two-phase flow problems in heterogeneous porous media
Lin, Jing-Qi; Yan, Xia; Wang, Er-Zhen; Zhang, Qi; Zhang, Kai; Liu, Pi-Yang; Zhang, Li-Ming
Pet. Sci. 22(11), 4714-4735.
Keywords: Physics-informed learning; Explainable artificial intelligence; Constraint learning; Two-phase flow; Heterogeneous porous media
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10. Uncertainty-aware neural networks with manual quality control for hydraulic fracturing downhole microseismic monitoring: From automated phase detection to robust source location
Zhang, Yi-Lun; Yu, Zhi-Chao; He, Chuan
Pet. Sci. 22(11), 4520-4537.
Keywords: Microseismic monitoring; Phase detection; Phase arrival picking; Source location; Deep learning; Uncertainty estimation
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11. Physics-informed graph neural network for predicting fluid flow in porous media
Chen, Hai-Yang; Xue, Liang; Liu, Li; Zou, Gao-Feng; Han, Jiang-Xia; Dong, Yu-Bin; Cong, Meng-Ze; Liu, Yue-Tian; Hosseini-Nasab, Seyed Mojtaba
Pet. Sci. 22(10), 4240-4253.
Keywords: Graph neural network (GNN); Deep-learning; Physical-informed neural network (PINN); Physics-informed graph neural network (PIGNN); Flow in porous media; Perpendicular bisectional grid (PEBI); Unstructured mesh
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12. Integrated optimization of reservoir production and layer configurations using relational and regression machine learning models
Dai, Qin-Yang; Zhang, Li-Ming; Zhang, Kai; Hao, Hao; Chen, Guo-Dong; Yan, Xia; Liu, Pi-Yang; Zhang, Bao-Bin; Wang, Chen-Yang
Pet. Sci. 22(9), 3745-3759.
Keywords: Surrogate model; Reservoir management; Evolutionary algorithm; Joint optimization; Layer configuration; Production optimization; Relational learning
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13. Deep learning-assisted optimization for enhanced oil recovery and CO2 sequestration considering gas channeling constraints
Zhuang, Xin-Yu; Wang, Wen-Dong; Su, Yu-Liang; Dai, Zhen-Xue; Yan, Bi-Cheng
Pet. Sci. 22(8), 3397-3417.
Keywords: Spatio-temporal sequence prediction; Multi-objective optimization; Enhanced oil recovery; CO2 sequestration; Gas channeling
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14. Evaluation of dynamic inter-well connectivity by using the state-variable-capacitance model
Guo, Li-Wen; Qu, Shi-Yuan; Lei, Yuan-Yuan; Kang, Zhi-Hong; Wang, Shuo-Liang
Pet. Sci. 22(8), 3380-3396.
Keywords: Capacitance model; Inter-well connectivity; Time-lag coefficient; Connected volume; Reservoir simulation
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15. A new method for the rate of penetration prediction and control based on signal decomposition and causal inference
Fan, Yong-Dong; Pang, Hui-Wen; Jin, Yan; Meng, Han; Lu, Yun-Hu; Chen, Hao-Dong
Pet. Sci. 22(6), 2414-2437.
Keywords: Rate of penetration; Signal decomposition; Causal inference; Parameters regulation; Machine learning; List of abbreviations
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16. Optimization of plunger lift working systems using reinforcement learning for coupled wellbore/reservoir
Xing, Zhi-Sheng; Han, Guo-Qing; Jia, You-Liang; Tian, Wei; Gong, Hang-Fei; Jiang, Wen-Bo; Mai, Pei-Dong; Liang, Xing-Yuan
Pet. Sci. 22(5), 2154-2168.
Keywords: Plunger lift; Liquid loading; Deliquification; Reinforcement learning; Deep deterministic policy gradient (DDPG); Artificial intelligence
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17. Efficient deep-learning-based surrogate model for reservoir production optimization using transfer learning and multi-fidelity data
Cui, Jia-Wei; Sun, Wen-Yue; Jeong, Hoonyoung; Liu, Jun-Rong; Zhou, Wen-Xin
Pet. Sci. 22(4), 1736-1756.
Keywords: Subsurface flow simulation; Surrogate model; Transfer learning; Multi-fidelity training data; Production optimization
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18. Machine learning approaches for assessing stability in acid-crude oil emulsions: Application to mitigate formation damage
Shakouri, Sina; Mohammadzadeh-Shirazi, Maysam
Pet. Sci. 22(2), 894-908.
Keywords: Acid-crude oil emulsion; Emulsion stability; Classification; Machine learning; Artificial neural network; Formation damage
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19. A survey on multi-objective, model-based, oil and gas field development optimization: Current status and future directions
Rostamian, Auref; de Moraes, Matheus Bernardelli; Schiozer, Denis Jose; Coelho, Guilherme Palermo
Pet. Sci. 22(1), 508-526.
Keywords: Derivative-free algorithms; Ensemble-based optimization; Gradient-based methods; Life-cycle optimization; Reservoir field development and; management
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20. Multi-stage and multi-objective optimization of anti-typhoon evacuation strategy for riser with new hang-off system
Li, Yan-Wei; Liu, Xiu-Quan; Hu, Peng-Ji; Hu, Xiao-Yu; Chang, Yuan-Jiang; Chen, Guo-Ming
Pet. Sci. 22(1), 457-471.
Keywords: Anti-typhoon evacuation strategy; Riser; Multi-stage and multi-objective; Optimization; Genetic algorithm; Least square method
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21. Real-time drilling torque prediction ahead of the bit with just-in-time learning
Bai, Kan-Kan; Sheng, Mao; Zhang, Hong-Bao; Fan, Hong-Hai; Pan, Shao-Wei
Pet. Sci. 22(1), 430-441.
Keywords: Drilling torque prediction; Just-in-time learning; Digital twin; Machine learning
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22. An automatic workflow for the quantitative evaluation of bit wear based on computer vision
Yang, Dong-Han; Song, Xian-Zhi; Zhu, Zhao-Peng; Pan, Tao; Tian, Long; Zhu, Lin
Pet. Sci. 21(6), 4376-4390.
Keywords: Bit wear evaluation; Computer vision; Drilling bit information database
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23. The real-time dynamic liquid level calculation method of the sucker rod well based on multi-view features fusion
Yin, Cheng-Zhe; Zhang, Kai; Liu, Jia-Yuan; Wang, Xin-Yan; Li, Min; Zhang, Li-Ming; Zhou, Wen-Sheng
Pet. Sci. 21(5), 3575-3586.
Keywords: Dynamic liquid level; Multi view; Features fusion; Sucker rod well; Dynamometer cards
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24. A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade
Li, Qian; Li, Jun-Ping; Xie, Lan-Lan
Pet. Sci. 21(5), 3496-3516.
Keywords: Drilling; Rate of penetration (ROP) prediction; Machine learning; Accuracy evaluation
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25. Machine learning methods for predicting CO2 solubility in hydrocarbons
Yang, Yi; Ju, Binshan; Lu, Guangzhong; Huang, Yingsong
Pet. Sci. 21(5), 3340-3349.
Keywords: Machine learning; Support vector regression; Extreme gradient boosting; Random forest; Multi-layer perceptron; CO2 solubility
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26. Optimized parameters of downhole all-metal PDM based on genetic algorithm
Lu, Jia-Xing; Kong, Ling-Rong; Wang, Yu; Feng, Chao; Gao, Yu-Lin
Pet. Sci. 21(4), 2663-2676.
Keywords: Positive displacement motor; Genetic algorithm; Profile optimization; Matlab programming; Overflow area
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27. Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China
Bai, Wen-Peng; Cheng, Shi-Qing; Guo, Xin-Yang; Wang, Yang; Guo, Qiao; Tan, Chao-Dong
Pet. Sci. 21(4), 2554-2570.
Keywords: Data mining technique; Analogy parameters; Oilfield analogy; Productivity prediction; Software platform
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28. Interpretation and characterization of rate of penetration intelligent prediction model
Pei, Zhi-Jun; Song, Xian-Zhi; Wang, Hai-Tao; Shi, Yi-Qi; Tian, Shou-Ceng; Li, Gen-Sheng
Pet. Sci. 21(1), 582-596.
Keywords: Fully connected neural network; Explainable artificial intelligence; Rate of penetration; ReLU active function; Deep learning; Machine learning
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29. Multi-surrogate framework with an adaptive selection mechanism for production optimization
Wang, Jia-Lin; Zhang, Li -Ming; Zhang, Kai; Wang, Jian; Zhou, Jian-Ping; Peng, Wen-Feng; Yin, Fa-Liang; Zhong, Chao; Yan, Xia; Liu, Pi -Yang; Zhang, Hua-Qing; Yang, Yong-Fei; Sun, Hai
Pet. Sci. 21(1), 366-383.
Keywords: Production optimization; Multi -surrogate models; Multi -evolutionary algorithms; Dimension reduction; Broad learning system
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30. A hybrid physics-informed data-driven neural network for CO2 storage in depleted shale reservoirs
Wang, Yan-Wei; Dai, Zhen-Xue; Wang, Gui-Sheng; Chen, Li; Xia, Yu-Zhou; Zhou, Yu-Hao
Pet. Sci. 21(1), 286-301.
Keywords: Deep learning; Physics-informed data-driven neural; network; Depleted shale reservoirs; CO2 storage; Transport mechanisms
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