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Quantitative Classification and Prediction of Diagenetic Facies in Tight Gas Sandstone Reservoirs via Unsupervised and Supervised Machine Learning Models: Ledong Area, Yinggehai Basin

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Abstract

Tight gas sandstone reservoirs are commonly characterized by complex diagenetic processes, resulting in time-consuming analysis for manual classification and prediction of diagenetic facies types with inherent preference and accuracy limitations. This work explores the feasibility of automatically classifying and predicting diagenetic facies types in tight gas sandstone reservoirs using unsupervised and supervised machine learning models. Firstly, petrophysical and petrological parameters, including porosity, permeability, bulk density, cement content, plane porosity, intergranular volume, and dissolution pore volume were used as input to identify diagenetic facies types automatically through an unsupervised machine learning model based on K-means algorithm. The classified diagenetic facies types exhibited significant diagenetic differences as shown in rock thin sections. Subsequently, with the identified diagenetic facies type as the learning target and logging series data as input, three types of supervised machine learning models were employed to predict the single-well vertical profile of diagenetic facies type distribution. The results indicated that the XGBoost model had optimal performance with the accuracy of 0.87. This work provides a complete automated workflow from basic petrophysical data to complex diagenetic facies profiles based on supervised and unsupervised machine learning models for diagenetic facies type analysis.

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Notes

  1. * 1 mD = 1 millidarcy = 9.869233−16 m2.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 42002171), Joint Funds of the National Natural Science Foundation of China (No. U19B2007), China Postdoctoral Science Foundation (and Special Foundation) (Nos. 2020TQ0299, 2020M682520), and Postdoctoral Innovation Science Foundation of Hubei Province of China. Grateful acknowledgment is extended to the China Scholarship Council for funding Chen's research. Zhanjiang Branch of CNOOC is appreciated for the support of experiments and samples.

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Authors and Affiliations

  1. Key Laboratory of Tectonics and Petroleum Resources, China University of Geosciences, Ministry of Education, Wuhan, 430074, China

    Xiaobo Zhao, Xiaojun Chen, Wen Chen, Yue Yao, Hua Wang & Guangqing Yao

  2. Department of Chemical Engineering, School of Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Xiaojun Chen

  3. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, 450001, China

    Mingqian Liu

  4. Hainan Branch of CNOOC Ltd, Haikou, 570311, China

    Hui Zhang

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  1. Xiaobo Zhao
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Correspondence to Xiaojun Chen.

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Appendix

Appendix

Parameter Setting of K-Means Clustering Model

Parameter setting of K-means clustering model: Algorithm = auto, copy_x = True, init = k-means +  + , max_iter = 300, n_init = 10, n_jobs = deprecated, precompute_distances = deprecated, random_state = 42, tol = 0.0001, verbose = 0.

Parameter Setting of SVM Model

Parameter setting of SVM model: C = 1.0, cache_size = 200, class_weight = None, coef0 = 0.0, decision_function_shape = 'ovr', degree = 3, gamma = 'auto', kernel = 'linear', max_iter = − 1, probability = False, random_state = 2, shrinking = True, tol = 0.001, verbose = False.

Parameter Setting of GBDT Model

Parameter setting of GBDT model: Loss = 'deviance', learning_rate = 0.1, n_estimators = 160, subsample = 1, min_samples_split = 2, min_samples_leaf = 1, max_depth = 3, init = None, random_state = 2, max_features = None, verbose = 0, max_leaf_nodes = None, warm_start = False.

Parameter Setting of XGBoost Model

Parameter setting of XGBoost model: max_depth = 8, eval_metric = ['logloss','auc','error'], learning_rate = 0.1, n_estimators = 200, objective = 'multi: softmax', nthread = − 1, gamma = 0, min_child_weight = 1, max_delta_step = 0, subsample = 0.5, colsample_bytree = 0.8, colsample_bylevel = 1, reg_alpha = 0, eg_lambda = 1, seed = 1440.

Petrological and Petrophysical Parameters and Calculated Diagenetic Strength of Samples

See Table 9.

Table 9 Petrological parameters, petrophysical parameters, and calculated diagenetic strength (partial)
Full size table

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Zhao, X., Chen, X., Chen, W. et al. Quantitative Classification and Prediction of Diagenetic Facies in Tight Gas Sandstone Reservoirs via Unsupervised and Supervised Machine Learning Models: Ledong Area, Yinggehai Basin. Nat Resour Res 32, 2685–2710 (2023). https://doi.org/10.1007/s11053-023-10252-x

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