Key Parameters Prediction of Shale Reservoir Based on Deep-Learning Model: A Case Study of Jurassic Da’anzhai Member in Sichuan Basin

Abstract

As an essential unconventional oil and gas resource, shale oil is of great significance to energy replacement and socio-economic development. Total organic carbon (TOC) and pyrolyzed hydrocarbon (S₁), as key parameters for hydrocarbon reservoir evaluation, are important guides for practical exploration. Usually, the high precision determination of TOC and S₁ requires sample collection and laboratory analysis, but is often compromised due to the cost and the limitation of coring continuity. With the advent of the digital age, increasingly intelligent methods are being employed in this field, such as Δlog R, support vector regression (SVR), and backpropagation neural network (BPNN). However, Δlog R has low performance, SVR does wrong in feature extraction, and BPNN is prone to local optimum. The coefficients of determination (R²) for TOC prediction using the three methods registered values of 0.25, 0.69, and 0.74. In contrast, the R² values for S₁ prediction were 0.23, 0.54, and 0.58. Thus, a low-cost, intelligent, and high-precision method to predict TOC and S₁ is needed. This paper proposes a new model for predicting TOC and S₁ in shale reservoirs based on an improved Deep learning network model (Encoder-ECA) based on the Transformer. With nearly 3 000 rock samples selected from the Jurassic Da’anzhai Member of the Sichuan Basin, China, and sedimentary facies variations in the study area, our results show that the Encoder-ECA model achieves an R² of 0.86 for TOC content prediction and an R² of 0.82 for S₁ content prediction. In addition, the Encoder-ECA model was successfully applied to the recently implemented exploratory well evaluations in the study area, and the prediction results were used to optimize the sweet spot section, with a combined daily production of 22.4 mcf of oil and 33 600 mcf of gas. Simultaneously, data from different basins will be utilized to validate the applicability range of the model. This research demonstrates the great potential of deep learning technology in unconventional resource evaluation. It confirms the application of the Encoder-ECA model in the exploration practice of lacustrine facies shale oil and gas.

Keywords

machine learning / sedimentary facies / logging curve / unconventional oil and gas / energy resources

References

[1] Amiri B H, Telmadarreie A, Shayesteh Met al. . Estimating Total Organic Carbon Content and Source Rock Evaluation, Applying Δlog R and Neural Network Methods: Ahwaz and Marun Oilfields, SW of Iran. Petroleum Science and Technology. 2011, 29(16): 1691-1704.

[2] Arthur J D, Bohm B, Layne M. Hydraulic Fracturing Considerations for Natural Gas Wells of the Marcellus Shale. The Ground Water Protection Council 2008 Annual Forum. September 21–24, 2008, Cincinnati, Ohio. 2009495959

[3] Carpentier B, Huc A Y, Bessereau G. Wireline Logging and Source Rocks: Estimations of Organic Carbon Content by CARBOLOG Method. The Log Analyst. 1991, 32: 279-297

[4] Chen L, Lin W B, Chen Pet al. . Porosity Prediction from Well Logs Using back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China. Journal of Earth Science. 2021, 32(4): 828-838.

[5] Chen S J, Zhang H X, Lu J Get al. . Controlling Factors of Jurassic Da’anzhai Member Tight Oil Accumulation and High Production in Central Sichuan Basin, SW China. Petroleum Exploration and Development. 2015, 42(2): 206-214.

[6] Cheng D W, Zhang Z J, Hong H Tet al. . Sequence Structure, Sedimentary Evolution and Their Controlling Factors of the Jurassic Lianggaoshan Formation in the East Sichuan Basin, SW China. Petroleum Exploration and Development. 2023, 50(2): 293-305.

[7] Dang W, Nie H K, Zhang J Cet al. . Pore-Scale Mechanisms and Characterization of Light Oil Storage in Shale Nanopores: New Method and Insights. Geoscience Frontiers. 2022, 13(5): 101424.

[8] Dao T, Fu D Y, Ermon Set al. . Flashattention: Fast and Memory-Efficient Exact Attention with IO-Awareness. Advances in Neural Information Processing Systems. 2022, 35: 16344-16359.

[9] De Silva P N K, Simons S J R, Stevens Pet al. . A Comparison of North American Shale Plays with Emerging Non-Marine Shale Plays in Australia. Marine and Petroleum Geology. 2015, 67: 16-29.

[10] Deng S G, Wang Y, Hu Y Yet al. . Integrated Petrophysical Log Characterization for Tight Carbonate Reservoir Effectiveness: A Case Study from the Longgang Area, Sichuan Basin, China. Petroleum Science. 2013, 103336-346.

[11] Feng D J. Geological Characteristics and Exploration Direction of Continental Shale Gas in Jurassic Da’anzhai Member, Sichuan Basin. Petroleum Geology & Experiment. 2022, 44(2): 219-230(in Chinese with English Abstract)

[12] Gehring J, Auli M, Grangier Det al. . Convolutional Sequence to Sequence Learning. ICML’17: Proceedings of the 34th International Conference on Machine Learning. 2017, 70: 1243-1252

[13] Gromet L P, Haskin L A, Korotev R Let al. . The “North American Shale Composite”: Its Compilation, Major and Trace Element Characteristics. Geochimica et Cosmochimica Acta. 1984, 48(12): 2469-2482.

[14] Guan Q Q. Prediction Method of Total Organic Carbon in Shale Oil Reservoir Based on PCA-CNN Model. Petroleum Geology and Recovery Efficiency. 2023, 29(6): 49-57(in Chinese with English Abstract)

[15] Guo T L. Evaluation of Highly Thermally Mature Shale-Gas Reservoirs in Complex Structural Parts of the Sichuan Basin. Journal of Earth Science. 2013, 24(6): 863-873.

[16] Guo X G, He W, Yang Set al. . Evaluation and Application of Key Technologies of “Sweet Area” of Shale Oil in Junggar Basin: Case Study of Permian Lucaogou Formation in Jimusar Depression. Natural Gas Geoscience. 2019, 30(8): 1168-1179(in Chinese with English Abstract)

[17] Guo X S, Zhao Y Q, Zhang W Tet al. . Accumulation Conditions and Controlling Factors for the Enrichment of Shale Oil And Gas in the Jurassic Qianfoya Formation, Yuanba area, Sichuan Basin. Petroleum Geology & Experiment. 2021, 43(5): 749-757(in Chinese with English Abstract)

[18] Hackley P C, Cardott B J. Application of Organic Petrography in North American Shale Petroleum Systems: A Review. International Journal of Coal Geology. 2016, 163: 8-51.

[19] Han K, Wang Y H, Chen H Tet al. . A Survey on Vision Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023, 45(1): 87-110.

[20] Han W Z, Zhao X Z, Jin F Met al. . Sweet Spot Evaluation and Exploration Practice of Lacustrine Shale Oil of the Second Member of Kongdian Formation in Cangdong Sag, Bohai Bay Basin. Petroleum Exploration and Development. 2021, 48(4): 900-910.

[21] Hood A C C M, Gutjahr C C M, Heacock R L. Organic Metamorphism and the Generation of Petroleum. AAPG Bulletin. 1975, 59(6): 986-996

[22] Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification. 2003

[23] Hu H T, Liu C, Lu S F. The Method and Application of Using Generalized- ΔLGR Technology to Predict the Organic Carbon Content of Continental Deep Source Rocks. Acta Geologica Sinica—English Edition. 2015, 89(Suppl. 1): 393-394.

[24] Huang D, Li Y, Wei Tet al. . Geological Characteristics and Exploration and Development Potential of Lacustrine Shale in the Jurassic Da’ anzhai Member, Sichuan Basin. 2016, Chengdu, China Petroleum Society Natural Gas Professional Committee, Sichuan Petroleum Society(in Chinese with English Abstract)

[25] Jacobi D, Gladkikh M, LeCompte Bet al. . Integrated Petrophysical Evaluation of Shale Gas Reservoirs. CIPC/ SPE Gas Technology Symposium 2008 Joint Conference. June 16 – 19, 2008, Calgary, Alberta, Canada. 2008SPE 114925-MS

[26] Jia C Z, Zheng M, Zhang Y F. Unconventional Hydrocarbon Resources in China and the Prospect of Exploration and Development. Petroleum Exploration and Development. 2012, 39(2): 139-146.

[27] Jia C Z, Zou C N, Yang Zet al. . Significant Progress of Continental Petroleum Geological Theory in Basins of Central and Western China. Petroleum Exploration and Development. 2018, 45(4): 573-588.

[28] Kadkhodaie-Ilkhchi A, Rahimpour-Bonab H, Rezaee M. A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: An Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran. Computers & Geosciences. 2009, 35(3): 459-474.

[29] Kamali M R, Mirshady A A. Total Organic Carbon Content Determined from Well Logs Using ΔLog R and Neuro Fuzzy Techniques. Journal of Petroleum Science and Engineering. 2004, 45(3/4): 141-148.

[30] Kitaev, N., Kaiser, t., Levskaya, A., 2020. Reformer: The Efficient Transformer. arXiv Preprint, arXiv: 2001.04451

[31] Lai J, Su Y, Xiao Let al. . Application of Geophysical Well Logs in Solving Geologic Issues: Past, Present and Future Prospect. Geoscience Frontiers. 2024, 15(3): 101779.

[32] Lee J, Lumley D E, Lim U Y. Improving Total Organic Carbon Estimation for Unconventional Shale Reservoirs Using Shapley Value Regression and Deep Machine Learning Methods. AAPG Bulletin. 2022, 106112297-2314.

[33] Li D H, Li J Z, Zhang Bet al. . Formation Characteristics and Resource Potential of Jurassic Tight Oil in Sichuan Basin. Petroleum Research. 2017, 2(4): 301-314.

[34] Li G X, Zhang B, Wu K Yet al. . Low Organic Matter Abundance and Highly Efficient Hydrocarbon Generation of Saline Source Rock in the Qaidam Basin, NW China. Petroleum Exploration and Development. 2023, 50(5): 1030-1044.

[35] Li M, Ma X, Jiang Qet al. . Enlightenment from Formation Conditions and Enrichment Characteristics of Marine Shale Oil in North America. Petroleum Geology and Recovery Efficiency. 2019, 26(1): 13-28(in Chinese with English Abstract)

[36] Li Y J, Feng Y Y, Liu Het al. . Geological Characteristics and Resource Potential of Lacustrine Shale Gas in the Sichuan Basin, SW China. Petroleum Exploration and Development. 2013, 40(4): 454-460.

[37] Liu B, He S L, Meng L Det al. . Sealing Mechanisms in Volcanic Faulted Reservoirs in Xujiaweizi Extension, Northern Songliao Basin, Northeastern China. AAPG Bulletin. 2021, 105(8): 1721-1743.

[38] Liu X Y, Zhou H L, Guo K Ket al. . Quantitative Characterization of Shale Gas Reservoir Properties Based on BiLSTM with Attention Mechanism. Geoscience Frontiers. 2023, 14(4): 101567.

[39] Liu X Z, Tian Z, Chen C. Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization. Geofluids. 2021, 2021: 6155663.

[40] Liu X, Lei Y H, Luo X Ret al. . TOC Determination of Zhangjiatan Shale of Yanchang Formation, Ordos Basin, China, Using Support Vector Regression and Well Logs. Earth Science Informatics. 2021, 1421033-1045.

[41] Lu S F, Huang W B, Chen F Wet al. . Classification and Evaluation Criteria of Shale Oil and Gas Resources: Discussion and Application. Petroleum Exploration and Development. 2012, 392268-276.

[42] Mahmoud A A A, Elkatatny S, Mahmoud Met al. . Determination of the Total Organic Carbon (TOC) Based on Conventional Well Logs Using Artificial Neural Network. International Journal of Coal Geology. 2017, 179: 72-80.

[43] Meng Z P, Guo Y S, Liu W. Relationship between Organic Carbon Content of Shale Gas Reservoir and Logging Parameters and Its Prediction Model. Journal of China Coal Society. 2015, 40(2): 247-253(in Chinese with English Abstract)

[44] Mkono C N, Shen C B, Mulashani A Ket al. . Deep Learning Integrated Approach for Hydrocarbon Source Rock Evaluation and Geochemical Indicators Prediction in the Jurassic–Paleogene of the Mandawa Basin, SE Tanzania. Energy. 2023, 284: 129232.

[45] Mulashani A K, Shen C B, Asante-Okyere Set al. . Group Method of Data Handling (GMDH) Neural Network for Estimating Total Organic Carbon (TOC) and Hydrocarbon Potential Distribution (S₁, S₂) Using Well Logs. Natural Resources Research. 2021, 30(5): 3605-3622.

[46] Ogala J E. Hydrocarbon Potential of the Upper Cretaceous Coal and Shale Units in the Anambra Basin, Southeastern Nigeria. Petroleum & Coal. 2011, 53135-44(in Chinese with English Abstract)

[47] Ouadfeul S A, Aliouane L. Total Organic Carbon Prediction in Shale Gas Reservoirs from Well Logs Data Using the Multilayer Perceptron Neural Network with Levenberg Marquardt Training Algorithm: Application to Barnett Shale. Arabian Journal for Science and Engineering. 2015, 40(11): 3345-3349.

[48] Pang X Q, Jia C Z, Chen J Qet al. . A Unified Model for the Formation and Distribution of both Conventional and Unconventional Hydrocarbon Reservoirs. Geoscience Frontiers. 2021, 12(2): 695-711.

[49] Passey Q R, Creaney S, Kulla J Bet al. . A Practical Model for Organic Richness from Porosity and Resistivity Logs. AAPG Bulletin. 1990, 74(12): 1777-1794

[50] Passey Q, Bohacs K, Esch Wet al. . From Oil-Prone Source Rock to Gas-Producing Shale Reservoir: Geologic and Petrophysical Characterization of Unconventional Shale-Gas Reservoirs. Proceedings of International Oil and Gas Conference and Exhibition in China. June 8–10, 2010. 2010

[51] Press W H, Teukolsky S A. Savitzky-Golay Smoothing Filters. Computers in Physics. 1990, 4(6): 669-672.

[52] Rong J, Zheng Z Y, Luo X Ret al. . Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example. Geofluids. 2021, 2021: 6794213.

[53] Rui J W, Zhang H B, Zhang D Let al. . Total Organic Carbon Content Prediction Based on Support-Vector-Regression Machine with Particle Swarm Optimization. Journal of Petroleum Science and Engineering. 2019, 180699-706.

[54] Rumelhart D E, Hinton G E, Williams R J. Learning Representations by Back-Propagating Errors. Nature. 1986, 323(6088): 533-536.

[55] Salama D M, El-Gohary N M. Semantic Text Classification for Supporting Automated Compliance Checking in Construction. Journal of Computing in Civil Engineering. 2016, 30: 04014106.

[56] Sayid M A, Yao Z-G, Li R Xet al. . Hydrocarbon Generation and Retention Potential of Chang 7 Organic-Rich Shale in the Ordos Basin, China. Petroleum Geoscience. 2023, 29(2): petgeo2022-008.

[57] Shalaby M R, Jumat N, Lai Det al. . Integrated TOC Prediction and Source Rock Characterization Using Machine Learning, Well Logs and Geochemical Analysis: Case Study from the Jurassic Source Rocks in Shams Field, NW Desert, Egypt. Journal of Petroleum Science and Engineering. 2019, 176: 369-380.

[58] Shalaby M R, Malik O A, Lai Det al. . Thermal Maturity and TOC Prediction Using Machine Learning Techniques: Case Study from the Cretaceous–Paleocene Source Rock, Taranaki Basin, New Zealand. Journal of Petroleum Exploration and Production Technology. 2020, 1062175-2193.

[59] Shi X, Wang J, Liu Get al. . Application of Extreme Learning Machine and Neural Networks in Total Organic Carbon Content Prediction in Organic Shale with Wire Line Logs. Journal of Natural Gas Science and Engineering. 2016, 33: 687-702.

[60] Sondergeld C, Newsham K, Comisky Jet al. . Petrophysical Considerations in Evaluating and Producing Shale Gas Resources. Proceedings of SPE Unconventional Gas Conference. February 23–25, 2010. 2010

[61] Song M S, Liu H M, Wang Yet al. . Enrichment Rules and Exploration Practices of Paleogene Shale Oil in Jiyang Depression, Bohai Bay Basin, China. Petroleum Exploration and Development. 2020, 47(2): 242-253.

[62] Su K M, Lu J G, Zhang G Wet al. . Origin of Natural Gas in Jurassic Da’ anzhai Member in the Western Part of Central Sichuan Basin, China. Journal of Petroleum Science and Engineering. 2018, 167: 890-899.

[63] Sun L D, Fang C L, Li Fet al. . Petroleum Exploration and Development Practices of Sedimentary Basins in China and Research Progress of Sedimentology. Petroleum Exploration and Development. 2010, 37(4): 385-396.

[64] Tan M J, Liu Q, Zhang S Y. A Dynamic Adaptive Radial Basis Function Approach for Total Organic Carbon Content Prediction in Organic Shale. Geophysics. 2013, 78(6): D445-D459.

[65] Tan M J, Song X D, Yang Xet al. . Support-Vector-Regression Machine Technology for Total Organic Carbon Content Prediction from Wireline Logs in Organic Shale: A Comparative Study. Journal of Natural Gas Science and Engineering. 2015, 26: 792-802.

[66] Tay Y, Dehghani M, Bahri Det al. . Efficient Transformers: A Survey. ACM Computing Surveys. 2022, 55(6): 109

[67] Tian Z P, Song X M, Wang Y Jet al. . Classification of Lacustrine Tight Limestone Considering Matrix Pores or Fractures: A Case Study of Da’anzhai Member of Jurassic Ziliujing Formation in Central Sichuan Basin, SW China. Petroleum Exploration and Development. 2017, 442234-246.

[68] Vapnik V N. The Nature of Statistical Learning Theory. 1999, New York, Springer

[69] Vaswani A, Shazeer N, Parmar Net al. . Attention is all You Need. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, Red Hook, NY, Curran Associates Inc.60006100

[70] Wang H J, Wu W, Chen Tet al. . An Improved Neural Network for TOC, S₁ and S₂ Estimation Based on Conventional Well Logs. Journal of Petroleum Science and Engineering. 2019, 176: 664-678.

[71] Wang P W, Chen Z H, Pang X Qet al. . Revised Models for Determining TOC in Shale Play: Example from Devonian Duvernay Shale, Western Canada Sedimentary Basin. Marine and Petroleum Geology. 2016, 70: 304-319.

[72] Wang P, Peng S P, He T H. A Novel Approach to Total Organic Carbon Content Prediction in Shale Gas Reservoirs with Well Logs Data, Tonghua Basin, China. Journal of Natural Gas Science and Engineering. 2018, 55: 1-15.

[73] Wang X N, Li J R, Jiang W Qet al. . Characteristics, Current Exploration Practices, and Prospects of Continental Shale Oil in China. Advances in Geo-Energy Research. 2022, 6(6): 454-459.

[74] Wang X Y, Xie R H, Wang T Yet al. . Total Organic Carbon Content Prediction of Source Rocks with Conventional Well Log Data Based on Regression Committee Machine. Arabian Journal of Geosciences. 2021, 14(15): 1547.

[75] Wu Z J, Li T F, Ji Set al. . Gas Generation from Coal and Coal-Measure Mudstone Source Rocks of the Xujiahe Formation in the Western Sichuan Depression, Sichuan Basin. Journal of Earth Science. 2023, 3441012-1025.

[76] Xia W C, Zhang N, Yuan X Pet al. . Cenozoic Qaidam Basin, China: A Stronger Tectonic Inversed, Extensional Rifted Basin. AAPG Bulletin. 2001, 85(4): 715-736

[77] Xie H, Yu B, Zeng Qet al. . TOC Logging Interpretation Method and Its Application to Yanchang Formation Shales, the Ordos Basin. Oil and Gas Geology. 2013, 34: 731-736(in Chinese with English Abstract)

[78] Xu Q L, Liu B, Ma Y Set al. . Geological and Geochemical Characterization of Lacustrine Shale: A Case Study of the Jurassic Da’ anzhai Member Shale in the Central Sichuan Basin, Southwest China. Journal of Natural Gas Science and Engineering. 2017, 47: 124-139.

[79] Yang G, Huang D, Huang P Het al. . Control Factors of High and Stable Production of Jurassic Da’anzhai Member Tight Oil in Central Sichuan Basin, SW China. Petroleum Exploration and Development. 2017, 44(5): 866-875.

[80] Yang Y M, Huang D, Yang Get al. . Geological Conditions to Form Lacustrine Facies Shale Oil and Gas of Jurassic Da’ anzhai Member in Sichuan Basin and Exploration Directions. Natural Gas Exploration and Development. 2019, 42(2): 1-12(in Chinese with English Abstract)

[81] Yang Y R, Yang X H, Shi W Zet al. . Recognition and Prediction of Source Rocks of the Madingo Formation in the Lower Congo Basin. Journal of Earth Science. 2023, 34(1): 232-241.

[82] Yang Z, Zou C N, Wu S Tet al. . Characteristics, Types, and Prospects of Geological Sweet Sections in Giant Continental Shale Oil Provinces in China. Journal of Earth Science. 2022, 33(5): 1260-1277.

[83] Yergin D. The Quest: Energy, Security and the Remaking of the Modern World. 2012

[84] Yu H Y, Rezaee R, Wang Z Let al. . A New Method for TOC Estimation in Tight Shale Gas Reservoirs. International Journal of Coal Geology. 2017, 179: 269-277.

[85] Yuan Y J, Rezaee R, Verrall Met al. . Pore Characterization and Clay Bound Water Assessment in Shale with a Combination of NMR and Low-Pressure Nitrogen Gas Adsorption. International Journal of Coal Geology. 2018, 194: 11-21.

[86] Zhang H Y, Wu W S, Wu H. TOC Prediction Using a Gradient Boosting Decision Tree Method: A Case Study of Shale Reservoirs in Qinshui Basin. Geoenergy Science and Engineering. 2023, 221: 111271.

[87] Zhang R, Tang L X. Oil-Bearing Evaluation of Different Lithofacies in Da’anzhai Member, Central Sichuan Basin: Implications for Shale Oil Development. Petroleum Science and Technology. 2023, 41(15): 1477-1491.

[88] Zhang R, Zhang B J, Wang M Let al. . Paleo-Sedimentary Environment and Lithofacies of Jurassic Da’anzhai Member in the Central Sichuan Basin—A Case Study of Well Ren’an 1. Frontiers in Earth Science. 2023, 10: 1052734.

[89] Zhang Y, Du G, Liu Get al. . Basic Characteristics and Exploration Direction of Lacustrine Shale Oil and Gas in Da’anzhai Member of Jurassic in Sichuan Basin. Geology of China. 2022, 49151-65(in Chinese with English Abstract)

[90] Zhao H, Jiang L, Jia Jet al. . Point Transformer. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2021Oct. 10–17, 2021

[91] Zhao P Q, Ma H L, Rasouli Vet al. . An Improved Model for Estimating the TOC in Shale Formations. Marine and Petroleum Geology. 2017, 83: 174-183.

[92] Zhao X Z, Zhou L H, Pu X Get al. . Theories, Technologies and Practices of Lacustrine Shale Oil Exploration and Development: A Case Study of Paleogene Kongdian Formation in Cangdong Sag, Bohai Bay Basin, China. Petroleum Exploration and Development. 2022, 493707-718.

[93] Zheng D Y, Wu S X, Hou M C. Fully Connected Deep Network: An Improved Method to Predict TOC of Shale Reservoirs from Well Logs. Marine and Petroleum Geology. 2021, 132: 105205.

[94] Zheng Z Y, Zuo Y H, Wen H Get al. . Natural Gas Characteristics and Gas-Source Comparisons of the Lower Triassic Jialingjiang Formation, Eastern Sichuan Basin. Geoenergy Science and Engineering. 2023, 221111165.

[95] Zhou D H, Sun C X, Liu Z Bet al. . Geological Characteristics of Continental Shale Gas Reservoir in the Jurassic Da’anzhai Member in the Northeastern Sichuan Basin. China Petroleum Exploration. 2020, 25(5): 32-42(in Chinese with English Abstract)

[96] Zhu L Q, Zhang C, Zhang C Met al. . Prediction of Total Organic Carbon Content in Shale Reservoir Based on a New Integrated Hybrid Neural Network and Conventional Well Logging Curves. Journal of Geophysics and Engineering. 2018, 1531050-1061.

[97] Zhu L Q, Zhang C, Zhang C Met al. . A New and Reliable Dual Model- and Data-Driven TOC Prediction Concept: a TOC Logging Evaluation Method Using Multiple Overlapping Methods Integrated with Semi-Supervised Deep Learning. Journal of Petroleum Science and Engineering. 2020, 188: 106944.

[98] Zhu L Q, Zhou X Q, Liu W Net al. . Total Organic Carbon Content Logging Prediction Based on Machine Learning: A Brief Review. Energy Geoscience. 2023, 4(2): 100098.

[99] Zou C N, Dong D Z, Wang S Jet al. . Geological Characteristics and Resource Potential of Shale Gas in China. Petroleum Exploration and Development. 2010, 376641-653.

[100] Zou C N, Tao S Z, Yang Zet al. . Development of Petroleum Geology in China: Discussion on Continuous Petroleum Accumulation. Journal of Earth Science. 2013, 245796-803.

[101] Zou C N, Yang Z, Zhang G Set al. . Theory, Technology and Practice of Unconventional Petroleum Geology. Journal of Earth Science. 2023, 34(4): 951-965.

[102] Zou C N, Zhai G M, Zhang G Yet al. . Formation, Distribution, Potential and Prediction of Global Conventional and Unconventional Hydrocarbon Resources. Petroleum Exploration and Development. 2015, 42(1): 14-28.

RIGHTS & PERMISSIONS

China University of Geosciences (Wuhan) and Springer-Verlag GmbH Germany, Part of Springer Nature