Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim Basin. (January 2021)

• Record Type: Journal Article
• Authors: Zheng, Wenhao; Tian, Fei; Di, Qingyun; Xin, Wei; Cheng, Fuqi; Shan, Xiaocai
• Is Part Of: Marine and petroleum geology. Volume 123(2021)
• Publication Date: 2021-01
• Subjects: Paleokarst reservoirs -- Electrofacies -- PCA -- K-means -- LDA; Submarine geology -- Periodicals; Petroleum -- Geology -- Periodicals; Géologie sous-marine -- Périodiques; Pétrole -- Géologie -- Périodiques; Petroleum -- Geology; Submarine geology; Periodicals; Electronic journals; 551.468
• DOI: 10.1016/j.marpetgeo.2020.104720
• Languages: English
• ISSNs: 0264-8172

Abstract

Abstract: The paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar paleokarst reservoirs, which can improve exploration efficiency and save economic cost. Highlights: Machine learning methods on conventional logging data can recognize electrofacies. High-resolution cores and FMI calibrated the electrofacies recognition process. Electrofacies recognizations can reveal heterogeneous paleokarst characteristics.