基于粒子群算法的直推测井资料反演

Abordán Armand
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引用次数: 3

摘要

针对直推测井资料的解释,提出了一种基于全局优化的层序反演方法。为了进一步提高反问题的超定率,将因子分析的结果纳入反演过程。直推测井数据集包括自然伽马射线强度、电阻率、体积密度、中子孔隙度和锥阻测井。首先,进行因子分析,估算沿钻孔分布的非饱和沉积物含水量。然后利用含水率因子分析信息进行区间反演,估算出粘土、砂石含量等剩余模型参数。利用物质平衡方程的反演结果推导出所研究地层的含气量。结果表明,因子分析辅助区间反演方法对模型参数的估计精度较高。该方法的另一个优点是,由于采用粒子群优化(PSO)技术求解逆问题,大大降低了反演过程对初始模型的依赖性。
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Particle Swarm Optimization Based Interval Inversion of Direct Push Logging Data
An interval inversion approach based on global optimization is suggested for the interpretation of direct-push logging data. To further increase the overdetermination ratio of the inverse problem, the result of factor analysis is incorporated into the inversion procedure. The direct-push logging dataset consists of natural gamma-ray intensity, electrical resistivity, bulk density, neutron-porosity and cone resistance logs. First, factor analysis is carried out to estimate the water content of unsaturated sediments distributed along the borehole. Then, interval inversion is done by utilizing the information of factor analysis on water content to estimate the remaining model parameters such as clay and sand content. Gas content of the studied formation is derived from the inversion results using the material balance equation. It is shown that the factor analysis assisted interval inversion procedure gives highly accurate estimation to the model parameters. As an added advantage of the hybrid method, the starting model dependence of the inversion procedure can be greatly reduced owing to the Particle Swarm Optimization (PSO) technique applied to solve the inverse problem.
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