一种基于τ模型算法的岩相建模方法及应用

doi:10.13809/j.cnki.cn32-1825/te.2023.02.009

摘要/Abstract

摘要：

基于地震反演约束的三维储层建模技术将地震数据和测井数据有效结合，既体现了测井数据的垂向分辨率，又考虑了反演数据体反映的储层横向变化特征，是目前油藏分析的主流方法。但在传统的地震数据与测井数据结合算法中，两者对最终岩相的约束权重难于控制，且将两者假设为相互完全独立或条件独立，会在最终结果中出现各种不一致性，例如预测砂岩概率值大于1。该文引入τ模型算法，并将其进行改进，使其在数据相互依赖的复杂情况下，也可确保结果的一致性。同时将该算法应用于尼日尔D油田的岩相建模中，并利用迭代优化算法确定相关系数，使得砂体概率预测平均误差减少30 %，有效提高了砂体预测精度。

关键词: 地震反演, 储层建模, 数据整合, 条件概率, 条件独立

Abstract:

The 3D reservoir modeling technology based on seismic inversion constraints effectively combines seismic data and logging data, which not only reflects the high vertical resolution of logging data, but also considers the lateral variation characteristics of reservoirs reflected by inversion data. It is currently the mainstream method of reservoir analysis. However, in the traditional combination of seismic data and logging data, the weight of the constraints on the final lithofacies is difficult to control. And the traditional full independence or conditional independence hypotheses lead to seismic data and logging data combination algorithms are rough, they may result in inconsistencies such as probability values greater than 1 if the each conditional probabilities are valued independently one from another. An alternative combination algorithm, model τ, is proposed which is not only simple, but also can ensure consistency of results in the presence of complex data interdependencies. At the same time, the algorithm is applied to the lithofacies modeling of D oilfield in Niger, and the average error of sand body probability is reduced by 30 %, which effectively improves the prediction accuracy of the sand body.

Key words: seismic inversion, reservoir modeling, data integration, conditional probability, conditional independence

中图分类号:

TE19

雷诚,崔炳凯,翟光华,叶禹,徐庆岩,张瑾琳. 一种基于τ模型算法的岩相建模方法及应用[J]. 油气藏评价与开发, 2023, 13(2): 206-214.

LEI Cheng,CUI Bingkai,ZHAI Guanghua,YE Yu,XU Qingyan,ZHANG Jinlin. A lithofacies modeling method based on a new integrated algorithm and its application[J]. Reservoir Evaluation and Development, 2023, 13(2): 206-214.

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