基于地震属性智能融合的湖相重力流沉积致密砂岩储层预测  

Prediction of tight sandstone of lacustrine gravity-flow reservoirs using intelligent fusion of seismic attributes

作　　者：万晓龙[1,2,3] 刘瑞璟 时建超 李伟[1,2] 麻书玮[4] 李桢[4] 李士祥 岳大力 吴胜和[1,2] WAN Xiaolong;LIU Ruijing;SHI Jianchao;LI Wei;MA Shuwei;LI Zhen;LI Shixiang;YUE Dali;WU Shenghe(State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum-Beijing,Beijing 102249,China;College of Geosciences,China University of Petroleum-Beijing,Beijing 102249,China;The 11th Oil Production Plant,PetroChina,Changqing Oilfeld Company,Xi’an 710018,China;Research Institute of Exploration and Development,PetroChina Changqing Oilfeld Company,Xi’an 710018,China)

机构地区：[1]中国石油大学(北京)油气资源与探测国家重点实验室,北京102249 [2]中国石油大学(北京)地球科学学院,北京102249 [3]中国石油长庆油田分公司第十一采油厂,西安710299 [4]中国石油长庆油田分公司勘探开发研究院,西安710018

出　　处：《石油科学通报》2023年第1期1-11,共11页Petroleum Science Bulletin

基　　金：中国石油天然气集团有限公司-中国石油大学(北京)战略合作科技专项项目(ZLZX2020-02);国家自然科学基金项目(41872107)联合资助。

摘　　要：湖相重力流是目前沉积学研究的热点与难点,也是致密油、页岩油富集的有利场所,鄂尔多斯盆地庆城油田三叠系延长组作为致密油、页岩油的典型代表已显示出巨大的勘探开发前景。然而,由于湖相重力流砂体分布认识不清,导致油田并未达到预期的开发效率。本文采用支持向量机(SVR)的机器学习方法,先优选频段再优选属性,建立分频属性与测井解释砂体厚度的非线性映射关系,实现了致密砂岩的定量预测。研究结果表明,低频地震属性适合预测厚层砂体,高频地震属性适合预测薄层砂体;采用机器学习的方法,将不同频率的地震属性智能融合,能够兼顾预测不同厚度砂体,既提高了地震属性的解释精度,又降低了地震解释的多解性,实现了砂体厚度的定量预测。检验结果显示,智能融合属性与砂体厚度的分布趋势与值域区间基本一致,智能融合属性预测砂体分布的可靠性明显提高,与测井解释砂体厚度的相关性由0.60提高至0.79,大多数井点处预测的砂体厚度误差小于5 m。继而,根据融合属性与测井解释,刻画了研究区的沉积微相展布特征:研究区目的层发育湖底扇沉积,细分为分支水道、朵叶主体、朵叶侧缘、滑塌体与朵叶间/水道间5种沉积微相;砂体主体呈扇形连片式沉积,厚度顺物源方向逐渐减薄;分支水道呈窄条带状树形分叉,下切发育于朵叶体之上;朵叶体沉积为研究区的沉积主体;滑塌体为湖底扇前端失稳滑塌形成的小规模孤立砂体,长轴方向多平行于湖底扇前端。研究成果对油田下一步高效开发具有重要意义。The study of lacustrine gravity-flow successions,which are regarded as an important reservoir unit of tight oil and shale oil,has been now a hotspot and also a challenge study work.The Triassic Yanchang Formation in Qingcheng oilfield in Ordos Basin,as a typical reservoir of tight oil and shale oil,shows great exploration and development prospects.However,this oilfield did not achieve the expected development efficiency,probably resulting from the poor understanding of the distribution of lacustrine gravity-flow sandbodies.In this work,proper frequency-decomposed seismic attributes were select relying on their correlation to sand thickness,and then fused using machine learning with a supervised algorithm of support vector machine(SVR).A nonlinear mapping relationship(i.e.,the trained SVR model)was established between the frequency-decomposed attributes and the thickness of sandbodies interpreted from well logs,and then the quantitative prediction of tight sandstone was realized through the application of the mapping relationship.The research indicates that:low-frequency seismic attributes are suitable for predicting thick sandbodies,while high-frequency seismic attributes are suitable for predicting thin sandbodies.Utilize advantages of seismic information of different frequencies,and consequently significantly reduces the uncertainty of seismic interpretation,and improves the prediction accuracy of sandbodies,and realizes the quantitative prediction of sandbodies.The test results show that the distribution trend and numerical range of intelligent fusion attrinbute are basically consistent with the sandbodies thickness interpreted by well logs,and the reliability of the sandbodies prediction by intelligent fusion attribute is significantly improved.The correlation between the intelligent fusion attribute and sandbodies thickness interpreted by well logs is improved from 0.6 to 0.79,and the prediction error of sandbodies thickness near the wells is less than 5 m.The geological interpretation indicates that the stu

关 键 词：地震属性 智能融合 储层预测 致密砂岩 湖相重力流 

分 类 号：P618.13[天文地球—矿床学]