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基于地质统计学反演的薄层砂体预测 被引量:1

Prediction of thin sand body based on geostatistical inversion
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摘要 油田开发后期,薄层砂体是下步产能挖潜的主力.以大庆长垣背斜北部过渡带萨尔图油层为例,基于地质统计学反演预测三角洲前缘亚相厚度3~5m薄层砂体,应用测井信息与地震数据,在约束稀疏脉冲反演的基础上,以地震数据为硬约束开展反演计算.结果表明:4~5 m厚度的砂体反演预测结果符合率达到95%,3~4 m厚度的砂体符合率达到90%,反演结果与实际地下储层发育情况基本一致.地质统计学反演结合测井信息垂向分辨率与地震数据的横向分辨率,能够较准确预测5 m以下薄层砂体展布规律,为油气田开发后期油气剩余储量计算、挖潜政策的制定和开发方案调整等工作奠定基础. The thin sand body reservoir at late development stage of oilfields is the main force in further tapping production potential.The study drawing on the case of the Saertu oil layer in the northern transition zone of Daqing placaticline anticline zone is focused on the prediction of 3-5 m thin sand body reservoir in the Delta front deposits using geostatistical inversion.The study achieves the inversion calculation thanks to the use of well logging information and seismic data,the combination of geological knowledge available,and the use of data as hard constraints,based on constrained sparse pulse inversion.The results prove that 4-5 m thick sand body inversion prediction provides the consistency rate of 95% and 4-5 m thick sand body offers the consistency rate of 90%,suggesting the consistency between the inversion results and actual development in the underground reservoir situation.It follows that geostatistical inversion working by the combination of vertical resolution of well information and lateral resolution of seismic data enables an accurate prediction of the law behind the distribution of thin sand bodies below 5 m and thus may lay the groundwork for the calculation of oil and gas reserve in the late stage of oil and gas fields,the formulation of stable production policy,and the adjustment of development plans.
作者 丛琳 王伟方 CONG Lin WANG Weifang(School of Geosciences, Northeast Petroleum University, Daqing 163318, China)
出处 《黑龙江科技大学学报》 CAS 2016年第3期284-288,共5页 Journal of Heilongjiang University of Science And Technology
基金 国家高技术研究发展计划项目(2013AA064903) 国家科技重大专项(2011ZX05006-005)
关键词 薄层砂体 地质统计学反演 变差函数 储层预测 thin sand body geostatistical inversion variation function reservoir prediction
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