Application of multi-threshold BIRCH clustering to facies-controlled porosity estimation
(2.Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited, SINOPEC, Dongying, Shandong, China 257000)
【Abstract】In view of the importance of lithofacies and porosity study in hydrocarbon exploration, we present an approach of multi-threshold BIRCH clustering (M-BIRCH) for lithofacies classification, based on which we estimate porosity using ridge regression. The heuristic initial threshold is set in terms of wave impedance distribution, and the number of thresholds is increased dynamically according to inter-clustering volume inconsistency. Global Agglomerative clustering is then employed for lithofacies classification. For each lithofacies, a modified ridge regression algorithm is used to predict porosity based on well porosity. Model tests show that M-BIRCH exhibits good robustness and computational efficiency for accurate lithofacies classification. A field data test shows that porosity could be estimated accurately using this method.
【Keywords】 lithofacies; multi-threshold; BIRCH clustering; ridge regression; porosity;
 Wang J, Cao Y C, Liu K Y, et al. Identification of sedimentary-diagenetic facies and reservoir porosity and permeability prediction: An example from the Eocene beach-bar sandstone in the Dongying Depression, China [J]. Marine and Petroleum Geology, 2017, 82: 69–84.
 Miller K, Vanorio T, Yang S, et al. A scale-consistent method for imaging porosity and micrite in dual-porosity carbonate rocks [J]. Geophysics, 2019, 84 (3): MR115–MR127.
 Álvarez P, Rangel J, Martinez M. Mapping porosity distribution in a vuggy carbonate reservoir integrating seismic attributes with borehole image logs through a supervised facies analysis [C]. SEG Technical Program Expanded Abstracts, 2009, 28: 1880–1884.
 Ashraf U, Zhu P M, Yasin Q, et al. Classification of reservoir facies using well log and 3D seismic attributes for prospect evaluation and field development: A case study of Sawan gas field, Pakistan [J]. Journal of Petroleum Science and Engineering, 2019, 175: 338–351.
 Xu Y, Yang H, Liu Y, et al. Application of seismic facies and attributes analysis on the identification of Permian igneous rock [J]. International Journal of Mining Science and Technology, 2012, 22(4): 471–475.
 ZHU Chao, LIU Zhanguo, YANG Shaoyong, et al. Lacustrine carbonate reservoir prediction in Yingxi, Qaidam Basin with the facies-constrained and segmented-frequency-band inversion [J]. Oil Geophysical Prospecting, 2018, 53 (4): 832–841 (in Chinese).
 JING Yongquan, LUAN Dongxiao, ZHANG Yuqing, et al. Fluvial facies inter-bedded sand reservoir prediction with seismic multi-attributes [J]. Oil Geophysical Prospecting, 2018, 53 (5): 1049–1058 (in Chinese).
 de Figueiredo L P, Grana D, Santos M, et al. Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance,porosity and lithofacies [J]. Journal of Computational Physics, 2017, 336: 128–142.
 LI Jinlei, CHEN Zuqing, WANG Liangjun, et al. Application of facies-controlled technique to bioclastic shoal reservoir prediction in less well zones [J]. Lithologic Reservoirs, 2017, 29 (3): 110–117 (in Chinese).
 WANG Hailong, QU Yongqiang, ZHANG Qiaofeng, et al. Tight-sand reservoir prediction in the deep Shahezi formation, Songliao Basin [J]. Oil Geophysical Prospecting, 2017, 52 (S2): 129–134 (in Chinese).
 He J H, Ding W L, Jiang Z X, et al. Logging identification and characteristic analysis of the lacustrine organic-rich shale lithofacies: A case study from the Es3l shale in the Jiyang Depression, Bohai Bay Basin, Eastern China [J]. Journal of Petroleum Science and Engineering, 2016, 145: 238–255.
 Saggaf M M, Nebrija E L. A fuzzy logic approach for the estimation of facies from wire-line logs [J]. AAPG Bulletin, 2003, 87 (7): 1223–1240.
 Wang G C, Carr T R, Ju Y W, et al. Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin [J]. Computers & Geosciences, 2014, 64: 52–60.
 ZHANG Yang, QIU Longwei, LI Ji, et al. Sedimentary facies analysis based on cluster of seismic attributes by fuzzy C-means algorithm [J]. Journal of China University of Petroleum (Edition of Natural Science), 2015, 39 (4): 53–61 (in Chinese).
 PANG Rui, WEI Jia. Seismic facies identification using K-means clustering [C]. Proceedings of the 24th Annual Meeting of the Chinese Geophysical Society, 2008 (in Chinese).
 Irani R, Nasimi R. Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir [J]. Expert Systems with Applications, 2011, 38 (8): 9862–9866.
 Tahmasebi P, Javadpour F, Sahimi M.Data mining and machine learning for identifying sweet spots in shale reservoirs [J]. Expert Systems with Applications, 2017, 88: 435–447.
 Saffarzadeh S, Shadizadeh S R. Reservoir rock permeability prediction using support vector regression in an Iranian oil field [J]. Journal of Geophysics and Engineering, 2012, 9 (3): 336–344.
 Pratama H. Machine learning: using optimized KNN (K-Nearest Neighbors) to predict the facies classifications [C]. The 13th SEGJ International Symposium, 2018, 538–541.
 Baziar S, Shahripour H B, Tadayoni M, et al. Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study [J]. Neural Computing and Applications, 2018, 30 (4): 1171–1185.
 SONG Jianguo, GAO Qiangshan, LI Zhe. Application of random forests for regression to seismic reservoir prediction [J]. Oil Geophysical Prospecting, 2016, 51 (6): 1202–1211 (in Chinese).
 Zhang T, Ramakrishnan R, Livny M. BIRCH: a new data clustering algorithm and its applications [J]. Data Mining and Knowledge Discovery, 1997, 1 (2): 141–182.
 SHAO Fengjing, ZHANG Bin, YU Zhongqing. BIRCH clustering algorithm with muti-threshold [J]. Computer Engineering and Applications, 2004, 40 (12): 174–176 (in Chinese).
 Olson C F. Parallel algorithms for hierarchical clustering [J]. Parallel Computing, 1995, 21 (8): 1313–1325.
 WEI Xiang. Improved BIRCH clustering algorithm based on density [J]. Computer Engineering and Applications, 2013, 49 (10): 201–205 (in Chinese).
 LI Hang. Statistical Learning Method [M]. Tsinghua University Press, Beijing, 2012, 21–23 (in Chinese).
 Hoerl A E, Kennard R W. Ridge regression: biased estimation for nonorthogonal problems [J]. Technometrics, 1970, 12 (1): 55–67.
 WANG Qi, LENG Linfeng, CHANG Yonglian. Improvement of ridge regression and principal component regression in stock index tracking [J]. Journal of Chongqing University of Technology (Natural Science), 2018 (1): 212–221 (in Chinese).
 Lee J, Mukerji T. The Stanford VI-E reservoir: A synthetic data set for joint seismic-EM time-lapse monitoring algorithms [C]. 25th Annual Report, Stanford Center for Reservoir Forecasting, Stanford University, Stanford, CA, 2012.
 Steinley D. Properties of the Hubert–Arabie adjusted Rand index [J]. Psychological Methods, 2004, 9 (3): 386–396.
 ZHANG Changkai, JIANG Xiudi, ZHU Zhenyu, et al. Attributes selection and reservoir prediction based on support vector machine [J]. Oil Geophysical Prospecting, 2012, 47 (2): 282–285 (in Chinese).