Article Collection: The Application of Artificial Intelligence (AI) in Petroleum Geology

1. Coupled optimization framework of CO2-WAG injection for enhanced oil recovery and storage: Integrating MLP with evolutionary and metaheuristic algorithms

Din, Shahab Ud; Xue, Liang; Ning, Fu-Long; Guo, Dong-Dong; Liao, Qin-Zhuo; Zeb, Hussan

Pet. Sci. 23(7), 4441-4460.

Keywords: CO2 sequestration; Deep neural network; Coupled optimization; Reptile search algorithm; CO2-WAG

2. Application of the AutoMix hybrid model for in-situ stress prediction in shale gas reservoirs

Yuan, Hang; Sun, Yu-Ping; Xiong, Wei; Niu, Wen-Te; Cheng, Qian

Pet. Sci. 23(7), 4394-4413.

Keywords: Unconventional reservoirs; In-situ stress prediction; Machine learning; Ensemble modeling; Logging data

3. Integration of multi-source geological engineering data for fracturing parameter optimization model

Li, Jie; Xiu, Quan-Zhen; He, Xiao-Dong; Li, Gen-Sheng; Li, Chang; Xu, Mao-Ya; Zhou, Tian-Xiang; Tian, Shou-Ceng; Wang, Tian-Yu

Pet. Sci. 23(7), 4053-4064.

Keywords: Fracturing parameter optimization; Fractured horizontal well; Production prediction; Deep learning; Shale oil; Logging data

4. TransUNet framework improved computed tomography image segmentation for core pore evolution

Zhou, Shao-Hua; Liu, Tian-Bao; Zhao, Yu; Yin, Shao-Hao; Liu, Zhi-Yu; Liu, Ji-Jun; Du, Ling-Wei; Zhao, Yue-Tong; Shi, Wei-Guang

Pet. Sci. 23(6), 3682-3697.

Keywords: Synchrotron radiation CT; Image segmentation; TransUNet; Argillaceous microporous network; Alkaline flooding porosity correction

5. A facies-constrained flow-network model for fast history matching and production optimization of polymer flooding reservoirs

Qin, Guo-Yu; Yan, Xia; Zhang, Kai; Zhang, Li-Ming; Zhang, Qi; Zhang, Ming-Xin; Wang, Chen-Yang

Pet. Sci. 23(6), 3408-3438.

Keywords: Physics-based data-driven model; Sedimentary facies constraints; Polymer flooding; History matching; Production optimization

6. Genesis of lamina combinations and intelligent well-logging interpretation in the upper Xiaganchaigou Formation, Yingxi area, Qaidam Basin, China

Fu, Jia-Lin; Yue, Da-Li; Wang, Wu-Rong; Wu, Kun-Yu; Wang, Han; Jiang, Ying-Hai; Zhang, Shu-Qi; Xu, Zi-Mo; Li, Wei

Pet. Sci. 23(6), 3091-3109.

Keywords: Lamina combination identification; Transformer intelligent recognition; Sedimentary origin; Qaidam Basin; E-3(2) segment

7. Reinterpretation of volcanic and hydrothermal influence on organic matter accumulation and paleo-depositional environment in a lacustrine shale system: Insight from interpretable machine learning model

Wang, Enze; Ma, Xiaoxiao; Li, Maowen; Fu, Yingxiao; Qian, Menhui; Cao, Tingting; Li, Zhiming; Feng, Yue; Jin, Zhijun; Li, Tong

Pet. Sci. 23(6), 3037-3058.

Keywords: Lacustrine shale; Paleoenvironment reconstruction; Interpretable machine leaning; Volcanic and hydrothermal activity; Organic matter enrichment mechanisms; Ordos Basin

8. Collaborative optimization of well operations and adjustment strategies in waterflooding reservoirs using an enhanced adaptive differential evolution algorithm

Zhang, Xian-Min; Yang, Jian-Gang; Feng, Qi-Hong; Hou, Ya-Wei; Zhang, Lei

Pet. Sci. 23(5), 2735-2757.

Keywords: Waterflooding reservoir; Collaborative optimization; Enhanced adaptive differential evolution; algorithm; Logarithmic spiral search; Shannon entropy

9. Techno-economic co-optimization of CO2 enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework

Wen, Shu-Qin; Wei, Bing; You, Jun-Yu; Leng, Nan-Jiang; Ampomah, William

Pet. Sci. 23(5), 2639-2654.

Keywords: CO2 enhanced oil recovery; Multi-objective optimization; Improved non-dominated sorting genetic; algorithm II; Unconventional oil reservoir

10. Boosted organic carbon accumulation in the Late Cretaceous Songliao Basin during marine incursion events

Wu, Ke-Rui; Sun, Yu; Yang, Da-Ming; Fang, Fu-Li; Yan, Bai-Quan; Zhou, Jie; Yu, Tao

Pet. Sci. 23(4), 1804-1816.

Keywords: Organic carbon accumulation; Marine incursion events; Qingshankou Formation; Songliao Basin; Lacustrine shale oil and gas; Paleolake

11. An interpretable attention-guided generative adversarial network framework with dual-domain learning for multi-condition constrained sedimentary facies modeling

Liu, Lei; Li, Wei; Gao, Jian; Yue, Da-Li; Wu, De-Gang; Wang, Wu-Rong; Lin, Jin; Li, Zhi-Bo; Zhong, Qian; Hou, Jia-Gen

Pet. Sci. 23(4), 1754-1772.

Keywords: Sedimentary facies models; Attention-guided generative adversarial network; Interpretable framework; Sedimentary patterns; Multi-condition modeling

12. A machine learning method for evaluating shale gas production based on the TCN-PgInformer model

Zhang, Hao-Yu; Wu, Wen-Sheng; Chen, Zhang-Xin; Liu, Benjieming

Pet. Sci. 23(2), 643-655.

Keywords: Shale production forecasting; Informer; TCN; Machine learning; Daily gas production

13. Grain boundary strength in methane hydrates

Xu, Ke; Ma, Rui; Qu, Yong-Xiao; Zhang, Zhi-Sen; Xu, Jian-Bin; Wu, Jian-Yang

Pet. Sci. 22(12), 5268-5276.

Keywords: Methane hydrate; Grain boundary; Mechanical properties; Molecular dynamic; Machine learning

14. Mechanism-guided data-driven model for optimized completion design

Hu, Shi-Meng; Sheng, Mao; Liu, Bing-Bing; Li, Jie; Tian, Shou-Ceng; He, Xiao-Dong; Li, Gen-Sheng

Pet. Sci. 22(12), 5068-5083.

Keywords: Unconventional reservoirs; Intelligent fracturing; Completion design optimization; Mechanical specific energy; Dynamic programming; Multi-objective optimization; Stage and cluster optimization

15. Intelligent identification method for dissolution vugs in karst reservoirs of carbonate rocks using electrical image logs: The Dengying Formation reservoir in the Gaoshiti-Moxi block, Sichuan Basin

Zhu, Peng; Ma, Tong; Yin, Lu; Xie, Dan; Xu, Cai-Hua; Xu, Qin; Liu, Tian-Yu

Pet. Sci. 22(11), 4446-4461.

Keywords: Deep learning prior; Electrical image logs; Blank strip filling; Image segmentation; Vug parameter calculation

16. A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage

Li, Juan; Fargalla, Mandella Ali M.; Yan, Wei; Zhang, Zi-Xu; Zhang, Wei; Zou, Zi-Chen; Qing, Tang; Yang, Tao; Tan, Chao-Dong; Li, Guang-Cong

Pet. Sci. 22(10), 4157-4173.

Keywords: Casing damage; Machine learning; Feature selection; Sand production; Boosting algorithm

17. An unsupervised intelligent warning model for drilling kick risk based on multi-temporal feature coupling

Zhou, De-Tao; Zhu, Zhao-Peng; Pan, Tao; Song, Xian-Zhi; Xiao, Shi-Jie; Li, Gen-Sheng; Zhang, Cheng-Kai; Zhou, Chen-Zhan; Zhang, Zi-Yue

Pet. Sci. 22(9), 3613-3626.

Keywords: Kick warning; Graph autoencoder; Field engineer operations; False alarms; Weighted dynamic time warping

18. Chemometric differentiation of oil families in the Mahu sag, Junggar Basin, NW China Cai, Hang-Xin; Jin, Jun; Li, Er-Ting; Zhang, Zhong-Da; Yu, Shuang; Pan, Chang-Chun

Pet. Sci. 22(9), 3530-3547.

Keywords: Oil source assessment; Chemometric analysis; Carbon isotopes of individual n -alkanes; Biomarkers; Polynuclear aromatic hydrocarbons (PAH); Junggar Basin

19. Study on S-wave velocity prediction in shale reservoirs based on explainable 2D-CNN under physical constraints

Li, Zhi-Jun; Deng, Shao-Gui; Hong, Yu-Zhen; Wei, Zhou-Tuo; Cai, Lian-Yun

Pet. Sci. 22(8), 3247-3265.

Keywords: S -wave velocity prediction; Petrophysical model; Class activation mapping technique; Explainable results

20. A large-scale, high-quality dataset for lithology identification: Construction and applications

Li, Jia-Yu; Tang, Ji-Zhou; Zhao, Xian-Zheng; Fan, Bo; Jiang, Wen-Ya; Song, Shun-Yao; Li, Jian-Bing; Chen, Kai-Da; Zhao, Zheng-Guang

Pet. Sci. 22(8), 3207-3228.

Keywords: Geoenergy exploration; Lithology identification; Lithology dataset; Artificial intelligence; Deep learning; Drill core

21. Multidimensional data-driven porous media reconstruction: Inversion from 1D/2D pore parameters to 3D real pores

Chi, Peng; Sun, Jian-Meng; Zhang, Ran; Yan, Wei-Chao; Dong, Huai-Min; Cui, Li-Kai; Cui, Rui-Kang; Luo, Xin

Pet. Sci. 22(7), 2777-2793.

Keywords: 3D digital rock; Pore network model; 1D/2D pore parameters; Pore structure; Generative adversarial network

22. Pore network modeling of gas-water two-phase flow in deformed multi-scale fracture-porous media

Wang, Dai-Gang; Ma, Yu-Shan; Hu, Zhe; Wu, Tong; Hou, Ji-Rui; Jiang, Zhen-Chang; Qi, Xin-Xuan; Song, Kao-Ping; Liu, Fang-Zhou

Pet. Sci. 22(5), 2096-2108.

Keywords: Ultra-deep reservoir; In-situ stress loading; U -Net fully convolutional neural network; CT scanning; Microstructure deformation; Pore-scale fluid flow

23. Deep learning-based upscaling for CO2 injection into saline aquifers

Wang, Yan-Ji; Jin, Yan; Lin, Bo-Tao; Pang, Hui-Wen

Pet. Sci. 22(4), 1712-1735.

Keywords: Artificial intelligence; Carbon storage; Subsurface flow simulation; Upscaling; Deep learning

24. Morpho-dynamics in fan deltas: Effect of topography on flow transformation, facies distribution and graded profile evolution, a case study in XLG fan delta

Sun, Hong-Wei; Li, Shun-Li; Li, Pan; Wei, Chao-Fan; Wu, Zhan-Teng; Hai, Long-Jv

Pet. Sci. 22(3), 1021-1040.

Keywords: Fan delta; Flow transformation; Facies distribution; Lobe stacking; Graded profile; Base level

25. Predicting the productivity of fractured horizontal wells using few-shot learning

Wang, Sen; Ge, Wen; Zhang, Yu-Long; Feng, Qi-Hong; Qin, Yong; Yue, Ling-Feng; Mahuyu, Renatus; Zhang, Jing

Pet. Sci. 22(2), 787-804.

Keywords: Fractured horizontal well; Machine learning; SMOTE; Few-shot learning; Prediction; Optimization

26. Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

Davoodi, Shadfar; Thanh, Hung Vo; Wood, David A.; Mehrad, Mohammad; Muravyov, Sergey V.; Rukavishnikov, Valeriy S.

Pet. Sci. 22(1), 296-323.

Keywords: Hybrid machine learning; Least-squares support vector machine; Grey wolf optimization; Feature selection; Carbon dioxide storage; Enhanced oil recovery

27. Effect of preprocessing on performances of machine learning-based mineral composition analysis on gas hydrate sediments, Ulleung Basin, East Sea

Jin, Hongkeun; Park, Ju Young; Park, Sun Young; Son, Byeong-Kook; Min, Baehyun; Lee, Kyungbook

Pet. Sci. 22(1), 151-162.

Keywords: Sample-based preprocessing; X-ray diffraction (XRD); Machine learning; Mineral composition; Gas hydrate (GH); Ulleung basin

28. Machine learning-based grayscale analyses for lithofacies identification of the Shahejie formation, Bohai Bay Basin, China

Wang, Yu-Fan; Xu, Shang; Hao, Fang; Liu, Hui-Min; Hu, Qin-Hong; Xi, Ke-Lai; Yang, Dong

Pet. Sci. 22(1), 42-54.

Keywords: Shale; Machine learning; Absolute grayscale; Relative amplitude; Grayscale phase model; Lithofacies identification

29. Multiscale characterization of the Albian-Cenomanian reservoir system behavior: A case study from the North East Abu Gharadig Basin, North Western Desert, Egypt

Rashad, Ola; El-Barkooky, Ahmed Niazy; El-Araby, Abd El-Moneim; El-Tonbary, Mohamed

Pet. Sci. 21(6), 3909-3936.

Keywords: NEAG 2 field; Reservoir characterization; Machine learning; Neural network analysis; Reservoir flow units; Pressure analysis; And drive mechanisms

30. A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Liang, Bin; Liu, Jiang; Kang, Li-Xia; Jiang, Ke; You, Jun-Yu; Jeong, Hoonyoung; Meng, Zhan

Pet. Sci. 21(5), 3326-3339.

Keywords: Production forecasting; Shale gas; BiLSTM-RF-MPA model; Nonstationary production time series; Deep learning

31. Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

Zhang, Rui; Cai, Bao-Ping; Yang, Chao; Zhou, Yu-Ming; Liu, Yong-Hong; Qi, Xin-Yang

Pet. Sci. 21(4), 2758-2768.

Keywords: Abnormal sensor; Combinatorial algorithm; Fault identification; Subsea production control system

32. An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

Xue, Liang; Xu, Shuai; Nie, Jie; Qin, Ji; Han, Jiang-Xia; Liu, Yue-Tian; Liao, Qin-Zhuo

Pet. Sci. 21(4), 2475-2484.

Keywords: Shale gas; Global sensitivity; Convolutional neural network; Data-driven

33. Evolution of pore systems in low-maturity oil shales during thermal upgrading-Quantified by dynamic SEM and machine learning

Liu, Jun; Bai, Xue; Elsworth, Derek

Pet. Sci. 21(3), 1739-1750.

Keywords: Low-maturity oil shale; Pore elongation; Organic matter pyrolysis; In-situ thermal upgrading; Scanning electron microscopy (SEM); Machine learning

34. Dynamic interwell connectivity analysis of multi-layer water fl ooding reservoirs based on an improved graph neural network

Huang, Zhao-Qin; Wang, Zhao-Xu; Hu, Hui-Fang; Zhang, Shi-Ming; Liang, Yong -Xing; Guo, Qi; Yao, Jun

Pet. Sci. 21(2), 1062-1080.

Keywords: Graph neural network; Dynamic interwell connectivity; Production -injection splitting; Attention mechanism; Multi -layer reservoir

35. Intelligent geochemical interpretation of mass chromatograms: Based on convolution neural network

Su, Kai -Ming; Lu, Jun -Gang; Yu, Jian; Lu, Zi-Xing; Chen, Shi-Jia

Pet. Sci. 21(2), 752-764.

Keywords: Organic geochemistry; Biomarker; Mass chromatographic analysis; Automated interpretation; Convolution neural network; Machine learning

36. Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method

Wang, Hao-Chen; Zhang, Kai; Chen, Nancy; Zhou, Wen-Sheng; Liu, Chen; Wang, Ji-Fu; Zhang, Li -Ming; Yu, Zhi-Gang; Cui, Shi-Ti; Yang, Mei-Chun

Pet. Sci. 21(1), 716-728.

Keywords: Production forecasting; Multiple patterns; Few -shot learning; Transfer learning

37. Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

He, Yun-Peng; Cheng, Hai -Bo; Zeng, Peng; Zang, Chuan-Zhi; Dong, Qing-Wei; Wan, Guang-Xi; Dong, Xiao-Ting

Pet. Sci. 21(1), 641-653.

Keywords: Sucker -rod pumping system; Dynamometer card; Working condition recognition; Deep learning; Time -frequency signature; Time -frequency signature matrix

38. A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Deng, Song; Pan, Hao-Yu; Wang, Hai-Ge; Xu, Shou-Kun; Yan, Xiao-Peng; Li, Chao-Wei; Peng, Ming -Guo; Peng, Hao-Ping; Shi, Lin; Cui, Meng; Zhao, Fei

Pet. Sci. 21(1), 535-550.

Keywords: Pore pressure; Grey wolf optimization; Multilayer perceptron; Effective stress; Machine learning