利用蚁群算法识别及预测碳酸盐岩裂缝的方法探讨
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教育部新世纪优秀人才支持计划(NCET-04-0911);四川省重点学科建设项目(SZD0414)


Recognition and prediction of fracture by using antcolony algorithm analysis in carbonate reservoir
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    摘要:

    针对东部某碳酸盐岩油藏裂缝发育,在开发后期油藏内部裂缝系统油水分布复杂,储层有利发育区的确定存在多解性的情况,采用蚁群算法对该区裂缝进行识别和预测。笔者参照岩心和成像测井资料对蚂蚁参数值调优,系统描述了研究区裂缝的空间展布特征,即裂缝体系呈网状结构,NW向、NNE向及近EW向的三组裂缝簇占绝对优势。再利用钻井漏失量、生产资料及成像测井对裂缝预测结果进行可靠性分析,表明利用蚁群算法识别及预测裂缝的方法切实可行,能为分析剩余油分布规律提供技术支持。蚁群算法作为一种新兴的仿生学算法,在利用地震资料定量预测裂缝方面有较大的发展潜力。

    Abstract:

    A fracture-developed carbonate oil field in the east has a complicate oil and gas distribution in facture network at late development stage, which leads to multiplicity of favorable reservoir estimation. In view of problems above, ant colony algorithm is adapted to recognition and predict facture in this field. The ant parameters are optimized on the basis of core and image log data, and the spatial distribution feature of fracture is described, as a reticulate structure with three dominant clusters of fracture (NW, NNE & NE). Drilling leakage, production data and image log are then used for reliability analysis of fracture predict, which presents that ant colony algorithm is a practicable methodology to recognition fracture and provides a support for remaining oil distribution analysis. As a booming bionic algorithm, ant colony algorithm has great potential for quantitative fracture predict with seismic materials.

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吴斌,唐洪,王素荣,王兴志,徐立明,徐正华.利用蚁群算法识别及预测碳酸盐岩裂缝的方法探讨[J].重庆大学学报,2012,35(9):131-138.

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  • 在线发布日期: 2012-10-11
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