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Transitional shale reservoir quality evaluation based on Random Forest algorithm—a case study of the Shanxi Formation, eastern Ordos Basin, China

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Abstract

The Ordos Basin, characterized by its abundant transitional shale gas resources, plays a significant role in Chinese oil and gas exploration industry. However, the complex sedimentary environment and lithofacies combination of transitional shale make it highly challenging for reservoir quality evaluation. Acknowledging the rapid development of artificial intelligence, particularly the extensive use of machine learning in geology, this study proposes a new approach to assess the quality of transitional shale reservoirs through the utilization of the Random Forest algorithm (RF). Firstly, the lithology identification chart and reservoir quality evaluation standard were established using data and logging curves, and the relevant datasets were constructed. Four logging curves (Acoustic curve (AC), Compensated Neutron Log (CNL), Density curve (DEN), Gamma Ray (GR)), which serve as input variables to reflect reservoir characteristics, were carefully selected, while reservoir quality classification was used as the output results. Subsequently, the RF model was constructed and trained using this dataset. By analyzing the confusion matrix, it was observed that the RF model achieved an impressive accuracy level of approximately 90%. The study confirmed RF's superiority through comparisons with five methods: Factor analysis, Bayesian discriminant analysis, Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT). The comparison results revealed that the RF model exhibited high reliability and practical efficiency. Additionally, the RF model is utilized to predict the thickness of Type I reservoirs in the study area. The results demonstrated remarkable success in confirming production data, further emphasizing the proficiency of the RF within the field of machine learning for evaluating transitional shale reservoirs. This method presents a valuable tool for assessing transitional shale reservoirs.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Abbreviations

P1s:

Shanxi Formation

RF:

Random Forest algorithm

GMM:

Gaussian Mixture Model KNN:K-Nearest Neighbors

GBDT:

Gradient Boosting Decision Tree

AC:

Acoustic curve

CNL:

Compensated Neutron Log

DEN:

Density curve

GR:

Gamma Ray

TOC :

Total organic content

PNNs:

Probabilistic Neural Networks

ANN:

Artificial Neural Network

SVR:

Support Vector Machine

BMA:

Bayesian Model Averaging

References

  • Abdulraheem A, Sabakhy E, Ahmed M et al (2007) Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic. SPE Middle East Oil and Gas Show and Conference, Bahrain. SPE-105350. https://doi.org/10.2523/105350-MS

  • Adewale A, Sun Y (2021) Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach. Interpretation 9(3):T735–T745

    Article  Google Scholar 

  • Al-Anazi A, Gates I (2011) Support-vector regression for permeability prediction in a heterogeneous reservoir: a comparative study. SPE Reserv Eval Eng 13(03):485–495

    Article  Google Scholar 

  • Alifu H, Vuillaume J, Johnson B et al (2020) Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1/-2 (SAR/optical), Landsat 8 (thermal) and digital elevation data. Geomorphology 369:107365

    Article  Google Scholar 

  • Al-Mudhafer W (2014) Using generalized linear regression of multiple attributes for modeling and prediction the formation permeability in sandstone reservoir. Offshore Technol Conf. OTC-25158-MS. Houston, Texas. https://doi.org/10.4043/25158-MS

  • Al-Mudhafar W (2017) Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms. J Pet Explor Prod Technol 7(4):1023–1033

    Article  Google Scholar 

  • Al-Mudhafar W (2019) Bayesian and LASSO regressions for comparative permeability modeling of sandstone reservoirs. Nat Resour Res 28(1):47–62

    Article  Google Scholar 

  • Al-Mudhafar W (2019) Integrating lithofacies and well logging data into smooth generalized additive model for improved permeability estimation: Zubair formation, South Rumaila oil field. Mar Geophys Res 40:315–332

    Article  Google Scholar 

  • Al-Mudhafar W, Abbas M, Wood D (2022) Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs. Mar Petrol Geol 145:105886

    Article  Google Scholar 

  • Al-Mudhafar W, Bondarenko M Integrating K-means clustering analysis and generalized additive model for efficient reservoir characterization. In 77th EAGE, Conference (2015) and Exhibition 1, 1–6. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214-4609.201413024

  • Cai G, Jiang Y, Li X et al (2022) Comparison of characteristics of Transitional and Marine Organicrich Shale reservoirs. Acta Sedimentol Sin 40(4):1030–1042

    Google Scholar 

  • Cao T, Deng M, Xiao J et al (2023) Reservoir characteristics of marine–continental transitional shale and gas-bearing mechanism: understanding based on comparison with marine shale reservoir. Nat Gas Geosci 8(3):169–185

    Google Scholar 

  • Chang Q, Ruan Z, Yu B et al (2024) Data-Driven classification and logging prediction of Mudrock lithofacies using machine learning: Shale Oil reservoirs in the Eocene Shahejie formation, Bonan Sag, Bohai Bay Basin, Eastern China. Minerals 14(4). https://doi.org/10.3390/min14040370

  • Chen C, Qu D, Wang M et al (2016) Prediction method of gas content in marine mud shale at JSB area in southeast Sichuan basin. Geophys Prospect Pet 55(4):597–605

    CAS  Google Scholar 

  • Chen Y, Wang Y, Guo M et al (2020) Differential enrichment mechanism of organic matters in the marine-continental transitional shale in northeastern Ordos Basin, China: control of sedimentary environments. J Nat Gas Sci Eng 83:103625

    Article  CAS  Google Scholar 

  • Chen Y, Zhao L, Pan J et al (2021) Deep carbonate reservoir characterisation using multi-seismic attributes via machine learning with physical constraints. J Geophys Eng 18(5):761–775

    Article  Google Scholar 

  • Cheng B, Xu T, Luo S et al (2022) Method and practice of deep favorable shale reservoirs prediction based on machine learning. Petrol Explor Dev + 49(5):1056–1068

    Article  Google Scholar 

  • Cui Y, Wang G, Stuart J et al (2017) Prediction of diagenetic facies using well logs–A case study from the upper Triassic Yanchang Formation, Ordos Basin, China. Mar Petrol Geol 81:50–65

    Article  CAS  Google Scholar 

  • Dev A, Eden R (2019) Formation lithology classification using scalable gradient boosted decision trees. Comput Ch Eng 128:392–404

  • Dias P, Lunga D (2022) Embedding ethics and trustworthiness for sustainable AI in Earth sciences: Where do we begin? IGARSS 4639–4642. https://doi.org/10.1109/IGARSS46834.2022.9883030

  • Fu J, Dong G, Zhou X et al (2021) Research progress of petroleum geology and exploration technology in Ordos Basin. China Pet Explor 26(3):19–40

    Google Scholar 

  • Fu J, Zhao H, Dong G et al (2023) Discovery and prospect of oil and gas exploration in new areas of Ordos Basin. Nat Gas Geosci 34(8):1289–1304

    Google Scholar 

  • Gao W, Zhao J (2024) Deep-time temperature field simulation of hot dry rock: a deep learning method in both time and space dimensions. Geothermics 119:102978

    Article  Google Scholar 

  • Gu Y, Cai G, Li S et al (2023) Pore structure and controlling factors of different lithofacies in transitional shale: a case study of the shanxi formation shan23 submember, eastern ordos basin. Acta Sedimentol Sin 41(1):318–332

    Google Scholar 

  • Gu Y, Li X, Qi L et al (2022) Sedimentology and geochemistry of the lower permian shanxi formation shan23 submember transitional shale, eastern ordos basin, North China. Front Earth Sci 10. https://doi.org/10.3389/feart.2022.859845

  • Hao R, Huang W, Bo J et al (2024) Fractal Characteristics and Main Controlling Factors of High-Quality Tight Sandstone Reservoirs in the Southeastern Ordos Basin. J Earth Sci 35(2):631–641

  • Handhal A, Al-Abadi A, Chafeet H et al (2020) Prediction of total organic carbon at rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms. Mar Petrol Geol 116:104347

    Article  CAS  Google Scholar 

  • Ho T (1995) Random decision forest. In Proceedings of the 3rd International Conference on Document Analysis and Recognition .https://doi.org/10.1109/ICDAR.1995.598994

  • Huang R, Liu S, Qi R et al (2021) Deep learning 3D sparse inversion of gravity data. JGR Solid Earth 126(11):e2021JB022476

    Article  Google Scholar 

  • Huang Y, Wang G, Zhang Y et al (2023) Logging evaluation of pore structure and reservoir quality in shale oil reservoir: the Fengcheng formation in Mahu Sag, Junggar Basin, China. Mar Petrol Geol 156:106454

    Article  CAS  Google Scholar 

  • Jiang D, Chen H, Xing J et al (2023) A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network. Geoenergy Sci Eng 226:211721

    Article  CAS  Google Scholar 

  • Jiang Y, Wen S, Cai G et al (2023b) Lithologic assemblage characteristics and shale gas exploration potential of transitional shale in the Ordos Basin. Nat Gas Ind 43(4):62–75

  • Jiang F, Chen X, Wang P, et al (2024b) Genesis and Accumulation of Paleo-Oil Reservoir in Dabei Area, Kuqa Depression, Northwest China: Implications for Tight-Gas Accumulation. J Earth Sci 35(2): 655–665.

  • Jiao F, Wen S, Liu X et al (2023) Research progress in exploration theory and technology of transitional shale gas in the ordos Basin. Nat Gas Ind 43(4):11–23

    Google Scholar 

  • Kamali M, Davoodi S, Ghorbani H et al (2022) Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Mar Petrol Geol 139:105597

    Article  Google Scholar 

  • Karpatne A, Ebert-Uphoff I, Ravela S et al (2019) Machine learning for the geosciences: challenges and opportunities. IEEE Trans Knowl Data Eng 31(8):1544–1554

    Article  Google Scholar 

  • Kuang L, Dong D, He W et al (2020) Geological characteristics and development potential of transitional shale gas in the east margin of the Ordos Basin. NW China. Petrol Explor Dev+ 47(3):471–482

    Article  Google Scholar 

  • Kuter S (2021) Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sens Environ 255:112294

    Article  Google Scholar 

  • Lacentre P, Carrica P (2003) A method to estimate permeability on uncored wells based on well logs and core data. SPE Latin American and Caribbean Petroleum Engineering Conference, Port-of-Spain, Trinidad and Tobago. SPE-81058. https://doi.org/10.2523/81058-MS

  • Li G, Qin Y, Wu M et al (2019) The pore structure of the transitional shale in the Taiyuan formation, Linxing area, Ordos Basin. J Petrol Sci Eng 181:106183

    Article  CAS  Google Scholar 

  • Li K, Xi K, Cao Y et al (2021a) Chlorite authigenesis and its impact on reservoir quality in tight sandstone reservoirs of the Triassic Yanchang formation, southwestern Ordos basin, China. J Petrol Sci Eng 205:108843

  • Li K, Xi Y, Su Z et al (2021b) Research on reservoir lithology prediction method based on convolutional recurrent neural network. Comput Electr Eng 95:107404

  • Liu J, Liu J (2021) An intelligent approach for reservoir quality evaluation in tight sandstone reservoir using gradient boosting decision tree algorithm-A case study of the Yanchang Formation, mid-eastern Ordos Basin, China. Mar Petrol Geol 126:104939

    Article  Google Scholar 

  • Lubo-Robles D, Devegowda D, Jayaram V et al (2022) Quantifying the sensitivity of seismic facies classification to seismic attribute selection: an explainable machine-learning study. Interpretation 10(3):SE41–SE69

    Article  Google Scholar 

  • Ma B, Huang T, Zou D et al (2018) A seismic-based quantitative method to predict gas content of marine shales: examples from changning area, southern sichuan basin. Nat Gas Explor Dev 41(1):23–29

    Google Scholar 

  • Maskey M, Ramachandran R, Gurung I et al (2022) Artificial intelligence vis-à-vis data systems. IGARSS 5081–5084https://doi.org/10.1109/IGARSS46834.2022.9883626

  • Mathisen T, Lee S, Datta-Gupta A (2003) Improved permeability estimates in carbonate reservoirs using elec-trofacies characterization: a case study of the North Robertson Unit, West Texas. SPE Reserv Evaluation Eng 6(3):176–184

    Article  CAS  Google Scholar 

  • McCreery E (2017) Al-Mudhafar W (2017) Geostatistical classification of lithology using partitioning algorithms on well log data-a case study in forest hill oil field, East Texas Basin[C]//79th EAGE Conference and Exhibition 2017. European Association of Geoscientists and Engineers 1:1–5

    Google Scholar 

  • Muhammad A, Peimin Z, Ren J et al (2024) Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models. Geomech Gophys Geo 10(1). https://doi.org/10.1007/s40948-024-00787-5

  • Pastick N, Jorgenson M, Wylie B et al (2015) Distribution of near-surface permafrost in Alaska: estimates of present and future conditions. Remote Sens Environ 168:301–315

    Article  Google Scholar 

  • Perez H, Datta-Gupta A, Mishra S (2005) The role of electrofacies, lithofacies, and hydraulic flow units in permeability predictions from well logs: a comparative analysis using classification trees. SPE Reserv Evaluation Eng 8(2):143–155

    Article  CAS  Google Scholar 

  • Qian K, Ning J, Liu X et al (2019) A rock physics driven bayesian inversion for TOC in the Fuling Shale gas reservoir. Mar Petrol Geol 102:886–898

    Article  CAS  Google Scholar 

  • Qiu Z, Song D, Zhang L et al (2021) The geochemical and pore characteristics of a typical marine–continental-transitional gas shale: a case study of the Permian Shanxi Formation on the eastern margin of the Ordos Basin. Energy Rep 7:3726–3736

    Article  Google Scholar 

  • Ramdani A, Chandra V, Finkbeiner T et al (2023) Multi-scale geophysical characterization of microporous carbonate reservoirs utilizing machine learning techniques: an analog case study from an upper jubaila formation outcrop, Saudi Arabia. Mar Petrol Geol 152:106234

    Article  Google Scholar 

  • Rodrigues M, Heather B, Matos M (2023) Seismic identification of carbonate reservoir sweet spots using unsupervised machine learning: a case study from Brazil deep water Aptian pre-salt data. Mar Petrol Geol 151:106119

    Google Scholar 

  • Saporetti C, Fonseca D, Oliveira L et al (2022) Hybrid machine learning models for estimating total organic carbon from mineral constituents in core samples of shale gas fields. Mar Petrol Geol 143:105783

    Article  CAS  Google Scholar 

  • Shalaby R, Jumat N, Lai D et al (2019) 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. J Pet Sci Eng 176:369–380

    Article  CAS  Google Scholar 

  • Sunwoo H, Hyunjoong K (2021) Optimal feature set size in Random Forest Regression. Appl Sci 11(8):3428–3428

    Article  Google Scholar 

  • Tariq Z, Aljawad M, Hasan A et al (2021) A systematic review of data science and machine learning applications to the oil and gas industry. J Pet Explor Prod Technol 1:1–36

    Google Scholar 

  • Wang S, Wang G, Huang L et al (2021) Logging evaluation of lamina structure and reservoir quality in shale oil reservoir of fengcheng formation in Mahu Sag, China. Mar Petrol Geol 133:105299

    Article  Google Scholar 

  • Wood A (2019) Lithofacies and stratigraphy prediction methodology exploiting an optimized nearest-neighbour algorithm to mine well-log data. Mar Petrol Geol 110:347–367

    Article  Google Scholar 

  • Wood D (2022a) Carbonate/siliciclastic lithofacies classification aided by well-log derivative, voatility and sequence boundary attributes combined with machine learning. Earth Sci Inf 3:1699–1721

    Article  Google Scholar 

  • Wood D (2022b) Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Adv Geo-Energy Res 6(1):69–85

  • Wood D (2022) Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence. Artif Intell Geosci 3:132–147

    Google Scholar 

  • Wu H, Xiong L, Ge Z et al (2019) Fine characterization and target window optimization of high-quality shale gas reservoirs in the Weiyuan area, Sichuan Basin. Nat Gas Ind B 6(5):463–471

  • Xiao Z, Wang J (2006) Image classification algorithm based on PCA and GMM. Computer Engineering and Design 11:1951–1953

  • Yan D, Huang W, Li A et al (2013) Preliminary analysis of marine-continental shale gas accumulation conditions and favorable areas in the upper paleozoic ordos basin. J Northeast Petroleum Univ 37(5):1–9

    CAS  Google Scholar 

  • Yang X, Guo S (2021) Reservoirs characteristics and environments evolution of lower permian transitional shale in the Southern North China Basin: implications for shale gas exploration. J Nat Gas Sci Eng 96:104282

    Article  CAS  Google Scholar 

  • Yerramilli S, Yerramilli R, Vedanti N et al (2013) Integrated reservoir characterization of an unconventional reservoir using 3D seismic and well log data: a case study of Balol Field, India. SEG Annual Meeting Houston, Texas:5258

    Google Scholar 

  • Yu Z, Wang Z, Adenutsi C (2023) Genesis of authigenic clay minerals and their impacts on reservoir quality in tight conglomerate reservoirs of the triassic baikouquan formation in the Mahu Sag, Junggar Basin, Western China. Mar Petrol Geol 143:106041

    Article  Google Scholar 

  • Zhai S, Geng S, Li C et al (2024) An improved convolutional neural network for predicting porous media permeability from rock thin sections. Gas Sci Eng 127:205365

    Article  Google Scholar 

  • Zhang J, Liu S, Li J et al (2017) Identification of sedimentary facies with well logs: an indirect approach with multinomial logistic regression and artificial neural network. Arab J Geosci 10:247

    Article  Google Scholar 

  • Zhang L, Dong D, Qiu Z et al (2021) Sedimentology and geochemistry of Carboniferous-Permian Marine-continental transitional shales in the eastern Ordos Basin, North China. Palaeogeogr Palaeoclimatol Palaeoecol 571:110389

    Article  Google Scholar 

  • Zhang H, Wu W, Wu H (2023) TOC prediction using a gradient boosting decision tree method: a case study of shale reservoirs in Qinshui Basin. Geoenergy Sci Eng 221:111271

    Article  Google Scholar 

  • Zhang Q, Qiu Z, Zhang L et al (2022a) Characteristics and controlling factors of transitional shale gas reservoirs: an example from Permian Shanxi Formation, Daning-Jixian block, Ordos Basin, China. Nat Gas Geosci 7(3):147–157

  • Zhang Q, Qiu Z, Zhao Q et al. (2022b) Composition effect on the Pore structure of Transitional Shale: a case study of the Permian Shanxi Formation in the Daning-Jixian Block at the Eastern Margin of the Ordos Basin. Front Earth Sci 9. https://doi.org/10.3389/feart.2021.802713

  • Zhang Q, Xiong W, Li X et al (2023) Discussion on Transitional Shale Gas Accumulation conditions from the Perspective of Source-Reservoir-Caprock Controlling Hydrocarbon: examples from Permian Shanxi Formation and Taiyuan Formation in the Eastern Margin of Ordos Basin, NW China. Energies 16(9):3710

    Article  CAS  Google Scholar 

  • Zhang Y, Long M, Chen K et al (2023b) Skilful nowcasting of extreme precipitation with NowcastNet. Nature 619(7970):526–532

  • Zhao J, Shen C, Ren L et al (2017) Quantitative prediction of gas contents in different occurrence states of shale reservoirs: a case study of the Jiaoshiba Shale gasfield in the Sichuan basin. Nat Gas Ind 37(4):27–33

    Google Scholar 

  • Zhao B, Li R, Qin X et al (2021) Geochemical characteristics and mechanism of organic matter accumulation of marine-continental transitional shale of the lower permian Shanxi Formation, southeastern Ordos Basin, north China. J Petrol Sci Eng 205:108815

    Article  CAS  Google Scholar 

  • Zhao J, Fan Y, Ge X et al (2023) An intelligent identification method of interlayers in deep clastic rock–An example of Donghe Sandstone in Hade Oilfield, Tarim Basin. Mar Petrol Geol 156:106419

    Article  Google Scholar 

  • Zhao T, Wang S, Ouyang C et al (2024) Artificial intelligence for geoscience: Progress, challenges, and perspectives. Innovation 5(5):100691

    Google Scholar 

  • Zhou X, Zhang Z, Zhang C et al (2017) Complex lithologic identification based on rough set-random forest algorism. Petroleum Geol Oilfield Dev Daqing 36(6):127–133

    Google Scholar 

  • Zhu L, Zhang C, Zhang C et al (2018) Application of Multiboost-KELM algorithm to alleviate the collinearity of log curves for evaluating the abundance of organic matter in marine mud shale reservoirs: a case study in Sichuan Basin, China. Acta Geophys 66:983–1000

    Article  Google Scholar 

  • Zou C, Yang Z, Zhang G, et al (2023) Theory, Technology and Practice of Unconventional Petroleum Geology. J Earth Sci 34(4): 951–965

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Acknowledgements

 We thank Jiang Chong of China University of Petroleum (Beijing) for his useful suggestions and opinions. We thank the editors and anonymous reviewers who refereed this paper for their valuable comments.

Funding

This work is supported by the National Natural Science Foundation of China (No. 42222209), the Scientific Research and Technological Development Programs of CNPC (No. 2023ZZ0801, 2024DJ8701).

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WL G, Q Z: Writing the main manuscript text, WL G, Q Z, JT Z: Assessing and preparing the data for the models, WL K, GY C, TQ Q: Constructing models, and assessing their performances. HJP, WY L, YG Y, YF Z: Reading and approving the final manuscript. Q Z, JT Z, Z Q: Applying methods to the constructed models, developing machine learning models, assessing the models, and writing the text. Reading and approving the final manuscript.

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Correspondence to Qin Zhang or Zhen Qiu.

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Gao, W., Zhang, Q., Zhao, J. et al. Transitional shale reservoir quality evaluation based on Random Forest algorithm—a case study of the Shanxi Formation, eastern Ordos Basin, China. Earth Sci Inform 18, 157 (2025). https://doi.org/10.1007/s12145-024-01515-z

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