基于贝叶斯理论的天然气水合物储层弹性—电性数据联合反演

Joint inversion of elastic-electrical data for gas hydrate reservoirs based on Bayesian theory

单一的声波或电阻率数据,一般通过简单线性或对数线性岩石物理模型预测天然气水合物储层参数。但声波数据或电阻率数据与储层参数之间存在敏感性差异,使用数据源不同,储层参数预测结果可能会存在很大差异。针对此问题,本文提出在贝叶斯理论框架下基于非线性的简化三相方程和改进Archie公式的声波—电性数据联合反演方法,同时预测天然气水合物饱和度、孔隙度和泥质含量,并可评估预测参数的不确定性。应用合成弹性—声波—电性数据分别进行单一弹性、电性数据试验,以及联合声波—电性数据反演模型试验,并将这些反演算法应用于实际测井数据。反演结果表明,本文提出的联合反演方法能预测出可靠的储层参数,且能有效降低因敏感性、噪声等问题产生的不确定性。

A single set of sonic velocity or resistivity data are usually used to estimate hydrate reservoir parameters based on simple linear or logarithmic linear rock physics.However,elastic data and electrical data have different sensitivity of reservoir parameters;the estimation based on different data may generate different values of reservoir parameters.In order to solve this problem,we propose a new method of elastic-electrical joint inversion based on a nonlinear simplified three-phrase equation and a modified Archie equation under the frame of Bayesian theory,with which we can simultaneously predict gas hydrate saturation,porosity,clay content,and their corresponding uncertainty.The feasibility of the joint inversion method is verified by both synthetic data tests and real well logging data tests.The results indicate that the joint inversion of elastic-electrical data can not only produce reliable reservoir parameter estimations,but also reduce effectively the uncertainty caused by different sensitivity and noise.