Seismic Prediction Methods for Tidal Flat Sand Bodies in the Shunbei Area of the Tarim Basin  

塔里木盆地顺北地区潮坪带砂体地震预测方法研究

作　　者：Zhi-peng Sun Rui-zhao Yang Jing-rui Chen Hao Zhang Shi-jie Zhang Peng-hui Yang Feng Geng 孙志朋;杨瑞召;陈井瑞;张昊;张诗杰;杨鹏辉;耿锋(华北理工大学应急管理与安全工程学院,唐山063000;中国矿业大学(北京)地球科学与测绘工程学院,北京100083;中国石化西北油田分公司,乌鲁木齐830011)

机构地区：[1]School of Emergency Management and Safety Engineering,North China University of Science and Technology,Tangshan 063000,China [2]College of Geoscience and Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China [3]Petroleum Exploration&Production Research Institute of Northwest Oilfield Company Sinopec,Urumqi 830011,China

出　　处：《Applied Geophysics》2025年第1期176-196,235,共22页应用地球物理(英文版)

基　　金：Collaborative Project Grant from the Exploration and Development Research Institute of SINOPEC Northwest Oilfi eld Company(Grant No.KY2021-S-104).

摘　　要：The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fine details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fine sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation imp塔里木盆地顺北地区潮坪带发现了大量致密砂岩油气区,需详细了解砂体分布特征以优化勘探部署。然而,单一方法难以有效预测砂体的分布。地震相分析可揭示砂体的宏观发育特征,但在微观细节刻画上存在不足。而单井沉积相可描述砂体的精细特征,却难以反映整体规律。本文以顺北地区柯坪塔格组下段为例,提出结合地震相与单井沉积微相的方法,通过无监督向量量化机获取地震相,以确定砂体的宏观分布;并结合主成分分析增强砂体细节刻画。利用三维地震数据、测井数据和地质解释结果进行精准构造解释,建立高精度地层展布框架,提高砂体预测精度。地震相分析揭示砂体的宏观分布,而单井岩心及测井曲线则明确潮道和砂坝的纵向发育特征。在不同沉积相带中,通过主成分分析技术优选出对砂体最敏感的地震属性。结果表明:潮下带均方根振幅属性、潮间带瞬时相位属性分别对砂体最为敏感,增强了砂体的识别精度及其轮廓和形状的刻画。该方法结合了地震相、单井沉积微相、机器学习方法提高了砂体刻画的精确性,为砂体预测提供了一种新的思路。

关 键 词：Shunbei Area Seismic Facies Vector Quantization PCA Sandstone prediction 

分 类 号：P31[天文地球—固体地球物理学]