|
1. Enhancement of NMR relaxation inversion: A review on pretreatment denoising
Guo, Jiang-Feng; Zhao, Yong-Jie; Xie, Ran-Hong; Xiao, Li-Zhi; Rui, Zhen-Hua; Luo, Si-Hui; Jin, Guo-Wen; Yan, Pei-Yuan; Xiao, Dan
Pet. Sci. 23(7), 3947-3971.
Keywords: Nuclear magnetic resonance; Signal-to-noise ratio; Denoising; Spectrum inversion; Artificial intelligence
|
|
2. Progressive pseudo-label self-supervised waveform inversion and imaging based on multiscale strategies: A marine data case application
Li, Wen-Da; Zhang, Hao; Huo, Shou-Dong; Han, Jian-Guang
Pet. Sci. 23(7), 3932-3946.
Keywords: Machine learning; Full waveform inversion; Migration imaging
|
|
3. Closed-loop porosity inversion method with well-seismic joint constraints based on wavelet transform
Chen, Teng-Fei; Zhang, Guang-Zhi; Hao, Hong-Jian; Chang, De-Kuan; Gao, Jian-Hu; Gao, Gang
Pet. Sci. 23(7), 3892-3905.
Keywords: Porosity inversion; Kernel density estimation; Wavelet transform; Closed-loop neural network; Constraints of seismic and well logging
|
|
4. A DBOKS-based hybrid machine learning method for carbonate rock classification
Li, Wen-Chuang; Zhao, Zhong-Xiang; He, You-Bin; Wu, Lian-Hua; Zhang, Yu-Qing
Pet. Sci. 23(7), 3854-3874.
Keywords: Lithology classification; Carbonate rocks; DBOKS algorithm; HMM framework; Machine learning
|
|
5. Multivariate data-driven fracture identification and distribution pattern in tight sandstone reservoirs using an improved CNN-Attention-BiLSTM: A case study of the Permian Lower Shihezi Formation in the Hangjinqi area, Ordos Basin, China
Liang, Bao-Yu; Zeng, Lian-Bo; Dong, Shao-Qun; Tan, Xue-Qun; Ren, Ji-Bo; Li, Hong-Tao; Yang, Zi-Yi; Liu, Shi-Qiang; Wang, Zhen
Pet. Sci. 23(7), 3834-3853.
Keywords: Fracture identification; Tight reservoirs; Full waveform sonic logs; Conventional logs; Deep learning
|
|
6. A novel interpretable machine learning framework for predicting gas-bearing properties of tight sandstone reservoirs
Cao, Liu; Jiang, Fu-Jie; Chen, Zhang-Xing; Huo, Li-Na; Feng, Run-Hai; Chen, Di; Wang, Meng-Yang; Li, Jian; Gao, Yang; Liu, Ben-Jie-Ming; Ma, Yong; Wang, Xiao-Juan; Jin, Zhi-Min; Zhang, Ao-Bo
Pet. Sci. 23(7), 3805-3833.
Keywords: Tight sandstone; Gas-bearing property prediction; Interpretable machine learning framework; Semi-quantitative prediction
|
|
7. Multiparameter Bayesian full-waveform inversion with uncertainty quantification based on regularized inverse scattering theory for elastic transversely isotropic media
Ye, Wen-Rui; Huang, Xing-Guo
Pet. Sci. 23(6), 3180-3212.
Keywords: Inverse scattering theory; Full waveform inversion; Anisotropy; Multi-parameter inversion; Uncertainty quantification; Regularization term; Randomized singular value decomposition
|
|
8. Artificial intelligence in petroleum and natural gas prospect evaluation and prediction
Wang, Qiao-Chu; Chen, Dong-Xia; Li, Sha; Chen, Yu-He; Chen, Xiang; Wang, Mao-Sen; Yang, Zai-Quan; Wang, Fu-Wei
Pet. Sci. 23(7), 3110-3135.
Keywords: Artificial intelligence; Machine learning; Deep learning; Petroleum prospect; Geological facies; Petroleum exploration and exploitation
|
|
9. A data augmentation method for lacustrine shale lithofacies classification based on a conditional diffusion probabilistic model: A case study from the Dongying Depression, Bohai Bay Basin, China
Li, Gui-Ang; Lin, Cheng-Yan; Dong, Chun-Mei; Ren, Li-Hua; Ma, Peng-Jie; Wu, Yu-Qi; Zhang, Guo-Yin; Du, Xin-Yu; Zhao, Zi-Ru
Pet. Sci. 23(6), 3017-3036.
Keywords: Lacustrine shale; Lithofacies prediction; Conditional diffusion probabilistic model; Data augmentation; Class imbalance
|
|
10. A machine learning-driven interpretative framework for reconstructing hydrocarbon evolution in hybrid petroleum systems
Tao, Ke-Yu; Cao, Jian; Wang, Yu-Ce; Ma, Wan-Yun
Pet. Sci. 23(5), 2587-2598.
Keywords: Machine learning; UMAP; Geochemistry; Hydrocarbons; Hybrid petroleum system; Junggar Basin
|
|
11. An anisotropy-dynamic ant colony optimization with probabilistic fracture uncertainty quantification for sub-seismic fault detection
Li, Shi-Chang; Zhao, Yang; Xian, Cheng-Gang; Qiao, Qi-Ya; Zhu, Fu-Yu; Liang, Xing; Zhang, Jie-Hui; Yan, Lan-Lan
Pet. Sci. 23(5), 2527-2544.
Keywords: Ant colony optimization; Fracture detection; Azimuthal anisotropy; Hidden Markov Model (HMM); Uncertainty quantification
|
|
12. Semi-supervised learning for AVO inversion with bidirectional spatial feature constraints
Liu, Ying-Tian; Li, Yong; Peng, Jun-Heng; Xie, Jian-Yong; Chen, Xian-Qiong
Pet. Sci. 23(5), 2501-2526.
Keywords: AVO inversion; Deep learning; Spatial feature constraints; Semi-supervised learning
|
|
13. Adaptive weight strategy for frequency-decomposed seismic attribute fusion in predicting of complex sand body distributions
Wu, Hong-Li; Wu, Sheng-He; Xu, Zhen-Hua; Liu, Ming-Cheng; Yang, Bo; Wu, De-Gang; Xie, Zi-Shi; Tang, Yu; Wan, Xiao-Long; Zhou, Xin-Ping
Pet. Sci. 23(5), 2367-2389.
Keywords: Reservoir heterogeneity; Sand thickness prediction; Frequency-decomposed attribute; intelligent fusion; Adaptive weighting; Deep neural network
|
|
14. Research on the intelligent characterization of interwell section architecture based on Bayesian expert systems
Wu, De-Gang; Wu, Sheng-He; Xu, Zhen-Hua; Liu, Lei; Liu, Ming-Cheng
Pet. Sci. 23(4), 1986-2001.
Keywords: Bayesian inference; Interwell architectural characterization; Expert system; Uncertainty evaluation
|
|
15. Multi-task U-net inversion of synthetic look-ahead logging-while-drilling data
Zhang, Shun; Zhang, Wen-Xiu; Chen, Wen-Xuan; Liang, Peng-Fei; Wang, Wen-Yang; Li, Xing-Han
Pet. Sci. 23(4), 1908-1928.
Keywords: Multi-task U -net; Look-ahead; Anisotropy; Multiple components; Inversion
|
|
16. Deep reparameterization for full waveform inversion: Architecture benchmarking, robust inversion, and multiphysics extension
Liu, Feng; Li, Ya-Xing; Su, Rui; Huang, Jian-Ping; Bai, Lei
Pet. Sci. 23(4), 1890-1907.
Keywords: Deep reparameterization; Full waveform inversion; Multiparameter inversion; Sparse acquisition; Network architecture search
|
|
17. Lateral constrained multi-trace seismic inversion based on deep learning
Zhang, Jian; Xue, Yi-Ran; Zhao, Xiao-Yan; Li, Jing-Ye
Pet. Sci. 23(3), 1220-1232.
Keywords: Seismic inversion; Multi-trace constraints; Deep learning; Physics-guided strategy
|
|
18. Irregularly seismic data interpolation based on deep learning with integrated channel-spatial attention mechanism
Ma, Chao; Huang, Jian-Ping; Qiao, Zi-Xuan; Li, San-Fu; Duan, Wen-Sheng; Lei, Gang-Lin
Pet. Sci. 23(3), 1182-1196.
Keywords: Seismic data; Deep learning; Irregular sampling; Channel-spatial attention mechanism; Interpolation
|
|
19. Joint geostatistical seismic inversion of elastic and petrophysical properties using stochastic co-simulation models based on parametric copulas
Vazquez-Ramirez, Daniel; Diaz-Viera, Martin A.; Valle-Garcia, Rauldel
Pet. Sci. 23(2), 608-625.
Keywords: Geostatistical seismic inversion; Bayesian inference; Joint probability distribution function; Parametric copula; Petrophysical simulation; Seismic property simulation
|
|
20. Fast beam migration capable of dealing with intersecting events
Chang, Zhi-Miao; Han, Fu-Xing; Gao, Zheng-Hui; Sun, Zhang-Qing; Huo, Shou-Dong; Li, Gang; Zhang, Ming
Pet. Sci. 23(1), 190-204.
Keywords: Fast beam migration (FBM); Intersecting events; Multimodal optimization method; High-dimensional computing problem; Neighborhood crowding differential evolution (NCDE) algorithm
|
|
21. Joint physics and model-guided pre-stack seismic inversion using double dual neural network
Zhang, Jian; Sun, Hui; Huang, Xing-Guo; Han, Li; Li, Yan-Song
Pet. Sci. 23(1), 157-177.
Keywords: Pre-stack seismic inversion; Double dual neural network; Physics guided; Model guided
|
|
22. Automated labeling and segmentation based on segment anything model: Quantitative analysis of bubbles in gas-liquid flow
Shi, Jia-Bin; You, Li-Jun; Dang, Jia-Chen; Wang, Yi-Jun; Gong, Wei; Peng, Bo
Pet. Sci. 22(12), 5212-5227.
Keywords: Dispersed phases; Bubble segmentation; Microfluidic system; Segment anything model; Gas-liquid flow; Artificial intelligence
|
|
23. A stacking ensemble approach for pore pressure prediction in real-time during drilling based on mud log data
Zhang, Dong-Yang; Ma, Tian-Shou; Liu, Yang; Zhang, De-Cheng
Pet. Sci. 22(12), 5047-5067.
Keywords: Pore pressure; Stacking ensemble approach; Machine learning; Real-time; Mud log
|
|
24. ThinGPT: describing sedimentary rock thin section images with a multimodal large language model
Luo, Xin; Sun, Jian-Meng; Chi, Peng; Zhang, Ran; Cui, Rui-Kang; Ci, Xing-Hua; Liu, Wei
Pet. Sci. 22(12), 5020-5033.
Keywords: Rock thin section description; Large language model; Contrastive language-image pre-training; Generative Pre-trained
|
|
25. LLMs-guided parameters prediction of tight sandstone reservoirs
Wu, Juan; Luo, Ren-Ze; Luo, Lei; Lei, Can-Ru; Chen, Xing-Ting
Pet. Sci. 22(12), 5005-5019.
Keywords: Large language models; Tight sandstone reservoirs; Cross-modal alignment; Data augmentation; Petrophysical parameters prediction
|
|
26. Joint PP and PS seismic inversion using predicted PS waves from deep learning
Fu, Xin; Zhang, Feng; Cao, Dan-Ping
Pet. Sci. 22(11), 4573-4583.
Keywords: Joint inversion; Deep learning; PP waves; cGAN; Shear wave prediction
|
|
27. Physic-guided multi-azimuth multi-type seismic attributes fusion for multiscale fault characterization
Song, Lei; Yin, Xing-Yao; Shi, Ying; Lang, Kun; Zhou, Hao; Xiang, Wei
Pet. Sci. 22(11), 4492-4503.
Keywords: Fault characterization; Multi-azimuth seismic coherence; Multi-azimuth seismic curvature; Data fusion; Deep learning; Physic-guided neural network
|
|
28. Incremental dimensionality reduction for efficiently solving Bayesian inverse problems
Li, Qing-Qing; Yu, Bo; Xu, Jia-Liang; Wang, Ning; Wang, Shi-Chao; Zhou, Hui
Pet. Sci. 22(10), 4102-4116.
Keywords: Dimension reduction; Seismic inversion; Discrete cosine transform
|
|
29. Direct inversion of 3D seismic reservoir parameters based on dual learning networks
Zhang, Yang; Yang, Hao
Pet. Sci. 22(10), 4037-4051.
Keywords: Seismic inversion; Rock physics; Deep learning; Tight sand; Reservoir predict
|
|
30. Coaly source rock evaluation using well logs: The Jurassic Kezilenuer Formation in Kuqa Depression, Tarim Basin, China
Zhao, Fei; Lai, Jin; Xia, Zong-Li; Wang, Zhong-Rui; Li, Ling; Wang, Bin; Xiao, Lu; Su, Yang; Wang, Gui-Wen
Pet. Sci. 22(9), 3599-3612.
Keywords: Source rock; Well logs; Kuqa Depression; Kezilenuer formation; Machine learning
|
|
31. Accurate reconstruction method of virtual shot records in passive source time-lapse monitoring based on SBA network
Wu, Ying-He; Pan, Shu-Lin; Chen, Kai; Chen, Yao-Jie; Liu, Da-Wei; Qin, Zi-Yu; Yi, Sheng-Bo; Liu, Ze-Yang
Pet. Sci. 22(9), 3548-3564.
Keywords: Passive source virtual shot reconstruction; Passive source time-lapse monitoring; SUNet-BiLSTM-attention network
|
|
32. An intelligent drilling guide algorithm design framework based on highly interactive learning mechanism
Zhao, Yi; Zhu, Dan-Dan; Wang, Fei; Dai, Xin-Ping; Jiao, Hui-Shen; Zhou, Zi-Jie
Pet. Sci. 22(8), 3333-3343.
Keywords: Highly interactive decision algorithm; Borehole guidance; Intelligent control method; Reinforcement learning; Rapid perception; Well drilling simulation
|
|
33. Identification and distribution patterns of the ultra-deep small-scale strike-slip faults based on convolutional neural network in Tarim Basin, NW China
Li, Hao; Han, Jun; Huang, Cheng; Zeng, Lian-Bo; Lin, Bo; Yao, Ying-Tao; Song, Yi-Chen
Pet. Sci. 22(8), 3152-3167.
Keywords: Small-scale strike-slip faults; Convolutional neural network; Fault label; Isolated fracture-vug system; Distribution patterns
|
|
34. A novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling data
Chen, Xu-Yue; Weng, Cheng-Kai; Tao, Lin; Yang, Jin; Gao, De-Li; Li, Jun
Pet. Sci. 22(7), 2868-2883.
Keywords: Formation pore pressure; Prediction ahead of the drill bit; Seismic and logging-while-drilling data; Machine learning; Model interpretation
|
|
35. Diffraction classification imaging using coordinate attention enhanced DenseNet
Sheng, Tong-Jie; Zhao, Jing-Tao; Peng, Su-Ping; Chen, Zong-Nan; Yang, Jie
Pet. Sci. 22(6), 2353-2383.
Keywords: Diffraction imaging; Diffraction classification; Azimuth-dip angle image matrix; Coordinate attention; DenseNet
|
|
36. Porosity prediction based on improved structural modeling deep learning method guided by petrophysical information
Tao, Bo-Cheng; Zhou, Huai-Lai; Wu, Wen-Yue; Zhang, Gan; Liu, Bing; Liu, Xing-Ye
Pet. Sci. 22(6), 2325-2338.
Keywords: Porosity prediction; Deep learning; Improved structural modeling; Petrophysical information
|
|
37. Extracting useful information from sparsely logged wellbores for improved rock typing of heterogeneous reservoir characterization using well-log attributes, feature influence and optimization
Wood, David A.
Pet. Sci. 22(6), 2307-2311.
Keywords: Petrophysical/geomechanical rock typing; Log attribute calculations; Heterogeneous reservoir characterization; Core-well-log-seismic integration; Feature selection influences
|
|
38. An EDCC-EMD analysis-based network for DAS VSP data denoising in frequency domain
Tang, Huan-Huan; Cheng, Shi-Jun; Li, Wu-Qun; Mao, Wei-Jian
Pet. Sci. 22(5), 1929-1945.
Keywords: DAS; Random noise; Coupling noise; EDCC-EMD; Deep learning
|
|
39. A novel drilling parameter optimization method based on big data of drilling
Peng, Chi; Zhang, Hong-Lin; Fu, Jian-Hong; Su, Yu; Li, Qing-Feng; Yue, Tian-Qi
Pet. Sci. 22(4), 1596-1610.
Keywords: Rate of penetration; Machine learning; Drilling parameter; Clustering analysis; Optimization
|
|
40. Spectral graph convolution networks for microbialite lithology identification based on conventional well logs
Li, Ke-Ran; Song, Jin-Min; Wang, Han; Yan, Hai-Jun; Liu, Shu-Gen; Lan, Yang; Jin, Xin; Ren, Jia-Xin; Zhao, Ling-Li; Tian, Li-Zhou; Deng, Hao-Shuang; Chen, Wei
Pet. Sci. 22(4), 1513-1533.
Keywords: Graph convolution network; Mirobialite; Lithology forecasting; Well log
|
|
41. Intermediate-high frequency dielectric permittivity of oil-wet rock and the wettability characterization
Zhao, Pei-Qiang; Chen, Yu; Hou, Yu-Ting; Chen, Xiu-Ling; Duan, Wei; Ke, Shi-Zhen
Pet. Sci. 22(4), 1485-1496.
Keywords: Wettability; Dielectric permittivity; NMR T 2 spectra; Simulated annealing
|
|
42. Adaptive subtraction with 3D U-net and 3D data windows to suppress seismic multiples
Huang, Jin-Qiang; Fu, Li-Yun; Ma, Jia-Hui; Du, Xing-Zhong; Li, Zhong-Xiao; Sun, Ke-Yi
Pet. Sci. 22(3), 1125-1139.
Keywords: Adaptive subtraction; 3D U-net; 3D data windows; Transfer learning; Multiple suppression
|
|
43. An integrated method of data-driven and mechanism models for formation evaluation with logs
Kang, Meng-Lu; Zhou, Jun; Zhang, Juan; Xiao, Li-Zhi; Liao, Guang-Zhi; Shao, Rong-Bo; Luo, Gang
Pet. Sci. 22(3), 1110-1124.
Keywords: Well log; Reservoir evaluation; Label scarcity; Mechanism model; Data-driven model; Physically informed model; Self-supervised learning; Machine learning
|
|
44. Self-supervised simultaneous deblending and interpolation of incomplete blended data using a multistep blind-trace U-Net
Wang, Ben-Feng; Lin, Shi-Cong; Chen, Xin-Yi
Pet. Sci. 22(3), 1098-1109.
Keywords: Blind-trace U -Net; Self-supervised learning; Simultaneous deblending and interpolation; Multi-step processing
|
|
45. Research on multi-wave joint elastic modulus inversion based on improved quantum particle swarm optimization
Wang, Peng-Qi; Liu, Xing-Ye; Li, Qing-Chun; Feng, Yi-Fan; Yang, Tao; Zhou, Xia-Wan; He, Xu-Kun
Pet. Sci. 22(2), 670-683.
Keywords: Young's modulus; PP-PS joint inversion; Exact Zoeppritz; Pre-stack inversion; QPSO
|
|
46. Generalizable data driven full waveform inversion for complex structures and severe topographies
Saadat, Mahdi; Hashemi, Hosein; Nabi-Bidhendi, Majid
Pet. Sci. 21(6), 4025-4033.
Keywords: Deep learning; Generalization; Full waveform inversion; Data-driven inversion; Complex structure
|
|
47. Multi-task learning for seismic elastic parameter inversion with the lateral constraint of angle-gather difference
Wang, Pu; Cui, Yi-An; Zhou, Lin; Li, Jing-Ye; Pan, Xin-Peng; Sun, Ya; Liu, Jian-Xin
Pet. Sci. 21(6), 4001-4009.
Keywords: Seismic inversion; Multi-task learning network; Angle gathers; Lateral accuracy; Elastic parameter
|
|
48. SeisResoDiff: Seismic resolution enhancement based on a diffusion model
Zhang, Hao-Ran; Liu, Yang; Sun, Yu-Hang; Chen, Gui
Pet. Sci. 21(5), 3166-3188.
Keywords: Seismic resolution enhancement; Diffusion model; High resolution; Reservoir characterization; Deep learning; Seismic data processing
|
|
49. Seismic modeling by combining the fi nite-difference scheme with the numerical dispersion suppression neural network
Yan, Hong-Yong
Pet. Sci. 21(5), 3157-3165.
Keywords: Finite difference; Seismic modeling; Numerical dispersion suppression; Computational accuracy; Computational efficiency
|
|
50. Complementary testing and machine learning techniques for the characterization and prediction of middle Permian tight gas sandstone reservoir quality in the northeastern Ordos Basin, China
Wang, Zi-Yi; Lu, Shuang-Fang; Zhou, Neng-Wu; Liu, Yan-Cheng; Lin, Li-Ming; Shang, Ya-Xin; Wang, Jun; Xiao, Guang-Shun
Pet. Sci. 21(5), 2946-2968.
Keywords: Diagenetic facies; Reservoir quality; Wireline log prediction; Machine learning techniques; Tight gas sandstones
|
|
51. Application of sparse S transform network with knowledge distillation in seismic attenuation delineation
Liu, Nai-Hao; Zhang, Yu-Xin; Yang, Yang; Liu, Rong-Chang; Gao, Jing-Huai; Zhang, Nan
Pet. Sci. 21(4), 2345-2355.
Keywords: S transform; Deep learning; Knowledge distillation; Transfer learning; Seismic attenuation delineation
|
|
52. Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction
Yang, Jiu-Qiang; Lin, Nian-Tian; Zhang, Kai; Cui, Yan; Fu, Chao; Zhang, Dong
Pet. Sci. 21(4), 2329-2344.
Keywords: Multicomponent seismic data; Deep learning; Adaptive particle swarm optimization; Convolutional neural network; Least squares support vector machine; Feature optimization; Gas-bearing distribution prediction
|
|
53. A hybrid WUDT-NAFnet for simultaneous source data deblending
Ke, Chao-Fan; Zu, Shao-Huan; Cao, Jun-Xing; Jiang, Xu-Dong; Li, Chao; Liu, Xing-Ye
Pet. Sci. 21(3), 1649-1659.
Keywords: Simultaneous-source; Deblending; Deep learning; Transformer
|
|
54. Identification of reservoir types in deep carbonates based on mixed-kernel machine learning using geophysical logging data
Shi, Jin-Xiong; Zhao, Xiang -Yuan; Zeng, Lian-Bo; Zhang, Yun-Zhao; Zhu, Zheng-Ping; Dong, Shao-Qun
Pet. Sci. 21(3), 1632-1648.
Keywords: Reservoir type identification; Geophysical logging data; Kernel Fisher discriminant analysis; Mixed kernel function; Deep carbonates
|
|
55. Probabilistic seismic inversion based on physics-guided deep mixture density network
Sun, Qian-Hao; Zong, Zhao-Yun; Li, Xin
Pet. Sci. 21(3), 1611-1631.
Keywords: Deep learning; Probabilistic inversion; Deep mixture density network
|
|
56. A regression approach for seismic first-break picking
Yuan, Huan; Yuan, San-Yi; Wu, Jie; Sang, Wen-Jing; Zhao, Yu -He
Pet. Sci. 21(3), 1584-1596.
Keywords: First -break picking; Low signal-to-noise ratio; Regression; BiLSTM; Traveltime; Geometry; Noisy seismic data
|
|
57. An improved deep dilated convolutional neural network for seismic facies interpretation
Yang, Na-Xia; Li, Guo-Fa; Li, Ting-Hui; Zhao, Dong-Feng; Gu, Wei-Wei
Pet. Sci. 21(3), 1569-1583.
Keywords: Seismic facies interpretation; Dilated convolution; Spatial pyramid pooling; Internal feature maps; Compound loss function
|
|
58. Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records
Pang, Hui -Wen; Wang, Han-Qing; Xiao, Yi-Tian; Jin, Yan; Lu, Yun-Hu; Fan, Yong-Dong; Nie, Zhen
Pet. Sci. 21(2), 1241-1256.
Keywords: Lost circulation; Risk prediction; Machine learning; Seismic attributes; Mud loss records
|
|
59. A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm
Yan, Tie; Xu, Rui; Sun, Shi-Hui; Hou, Zhao-Kai; Feng, Jin-Yu
Pet. Sci. 21(2), 1135-1148.
Keywords: Intelligent drilling; Closed -loop drilling; Lithology identification; Random forest algorithm; Feature extraction
|
|
60. Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification
Fernandes, Fabio Junior Damasceno; Teixeira, Leonardo; Freire, Antonio Fernando Menezes; Lupinacci, Wagner Moreira
Pet. Sci. 21(2), 918-935.
Keywords: Stochastic inversion; Bayesian classification; Porosity modeling; Carbonate reservoirs; Igneous rocks
|
|
61. Automatic velocity picking based on optimal key points tracking algorithm
Wang, Yong-Hao; Lu, Wen-Kai; Jin, Song-Bai; Li, Yang; Li, Yu-Xuan; Gu, Xiao-Feng
Pet. Sci. 21(2), 903-917.
Keywords: Velocity picking; Multi -object tracking; Density clustering; Kalman filter
|
|
62. An adaptive physics-informed deep learning method for pore pressure prediction using seismic data
Zhang, Xin; Lu, Yun-Hu; Jin, Yan; Chen, Mian; Zhou, Bo
Pet. Sci. 21(2), 885-902.
Keywords: Pore pressure prediction; Seismic data; 1D convolution pyramid pooling; Adaptive physics-informed loss function; High generalization capability
|
|
63. Geophysical prediction of organic matter abundance in source rocks based on geochemical analysis: A case study of southwestern Bozhong Sag, Bohai Sea, China
Wang, Xiang; Liu, Guang-Di; Wang, Xiao-Lin; Ma, Jin-Feng; Wang, Zhen-Liang; Wang, Fei-Long; Song, Ze-Zhang
Pet. Sci. 21(1), 31-53.
Keywords: Total organic carbon (TOC); Residual hydrocarbon generation potential; Geophysical prediction; Seismic attribute; Bozhong Sag; Bohai Bay Basin; (S2)
|
