摘要
Abstract
The intricate tectonic evolution,coupled with diverse sedimentary environments and high-ly variable lithologies,results in strongly heterogeneous reservoirs in the Santai Formation of the Zhanhua sag,making reservoir quality prediction particularly challenging.This highlights the critical importance of accurately identifying and classifying diagenetic facies for effective reservoir evaluation.Traditional super-vised learning methods for whole-well diagenetic facies identification are limited by the availability of train-ing samples and thus often impractical in data-scarce scenarios.To address this,an unsupervised learning approach constrained by single-factor analysis is proposed for the logging-based identification of diagenetic facies.By integrating parameters such as apparent compaction rate,cement content,porosity,and frac-ture density,four diagenetic facies were identified:dense compaction facies,carbonate-cemented facies,dissolution-fracture facies,and weakly dissolved facies with unstable components.Four diagenesis-sensi-tive logging curves—GR(natural gamma),AC(acoustic travel time),DEN(density),and RD(deep lateral resistivity)—were selected as inputs for the unsupervised clustering algorithm.The clustering ran-ges of these curves were individually constrained to establish a reliable correlation between logging re-sponses and diagenetic facies.Calibration of logging facies with diagenetic facies was conducted using core data,including thin-section analysis of cast samples,allowing for regional-scale identification and classifi-cation of diagenetic facies.The results show that the dissolution-fracture facies and weakly dissolved facies with unstable components represent the most favorable diagenetic facies for reservoir development.These are associated with relatively high porosity and permeability and are mainly distributed in coarse-grained rocks such as conglomeratic sandstones and,while the weakly dissolved facies with unstable components are mainly distributed in fine-grained rocks like siltstone.The accuracy of the proposed method was valida-ted through comparisons with blind wells,demonstrating its effectiveness in non-cored intervals.This ap-proach provides a novel method for predicting reservoir diagenetic facies and offers practical implications for the evaluation and prediction of high-quality reservoir zones in data-limited settings.关键词
济阳坳陷/沾化凹陷/三台组/成岩相测井识别/单因素约束/无监督学习Key words
Jiyang Depression/Zhanhua sag/Santai Formation/diagenetic facies logging identifi-cation/single factor constraints/unsupervised learning分类
能源科技
