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盐系地层岩性油藏预测方法研究 被引量:8

Research on lithologic oil pool prediction in saliferous strata
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摘要 针对潜江凹陷岩性油藏储层与非储层之间波阻抗差异小,储层薄,波阻抗反演存在多解性且不能定量预测等问题,探讨了盐系地层岩性油藏预测的研究思路和配套技术。对潜江组盐系地层的岩石物理特征进行了分析,在此基础上提出了针对性的储层预测方法:①应用高分辨率层序地层学方法确定潜江凹陷岩性油藏的有利分布区;②通过基于模型的井约束稀疏脉冲反演获得的波阻抗数据体进行砂体描述;③利用地震约束测井反演方法,对自然伽马和密度等岩性敏感测井参数进行反演,得到拟测井参数反演数据体,在此基础上对砂体厚度进行预测,对储层进行定量评价;④利用多信息含油性预测方法对储层的含油性进行评价。在潜江凹陷广北油田,应用上述岩性油藏预测方法预测了储层发育区域,部署的广43-1井钻遇的某砂组与预测的厚度吻合,试获38.0 t/d的高产工业油流。 There exist some problems between reservoir and non-reservoir in Qianjiang depression, which are small difference in acoustic impedance, thin thickness of reservoir, existence of multi-solutions for acoustic impedance inversion and not able to predict quantitatively. Aiming at these problems, the study thoughts and supporting techniques for lithologic deposit prediction in saliferous strata are discussed. Rock property of the reservoir is analyzed. On the basis of this, the reservoir prediction methods are proposed;①high-resolution sequence stratigraphy method, which can be used to ascertain the favorable zone of lithologic deposit in Qianjiang depression;②well-restrained rarefaction impulse inversion, which can be used to obtain the acoustic impendence data for describing sand bodies;③seismic-restrained log inversion method, which can be used for the inversion of the lithologic-sensitive log parameters to obtain the pseudolog inversion data, predict the thickness of sand body, and quantitatively evaluate the reservoir;④multi-information oiliness prediction method, which can be used to evaluate oiliness of reservoir. The above mentioned methods have been used to predict the reservoir-developed region of Guangbei oilfield in Qianjiang depression, the thickness of one sand set of the deployed Guang 43-1 well coincides with the predicted value, obtaining an output as high as 38 t/d of oil flow in testing.
出处 《石油物探》 EI CSCD 2006年第5期476-481,共6页 Geophysical Prospecting For Petroleum
关键词 潜江凹陷 盐系地层 层序地层学 岩性油藏 储层预测 含油气性 Qianjiang depression saliferous strata sequence stratigraphy lithologic deposit reservoir prediction oiliness and gassiness
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