基于构造约束联合概率反演的油藏参数表征方法

对于油藏参数预测及其不确定性评价，前人的方法均为多步骤反演，很难考虑各个环节的不确定性。为此，提出基于构造约束联合概率反演的油藏参数表征方法。首先通过统计井资料得到岩相依赖储层弹性参数和物性参数混合高斯联合先验分布，在岩石物理参数敏感性分析基础上建立储层弹性参数和物性参数高斯联合先验分布；利用地质构造约束最小二乘井插值将构造信息和井信息整合到反演框架，基于贝叶斯理论推导得到同时表征储层弹性参数、物性参数、岩相后验概率分布的解析表达式。与传统方法相比，新方法通过同时反演策略降低误差累积，提高了储层参数及其不确定性信息预测的准确性；另外，新方法引入构造信息和井信息提高了反演结果的横向连续性及分辨率。为验证新方法的有效性，对M区实际数据集通过条件井和盲井测试，对比、分析了无构造约束多步方法与新方法的反演结果。结果表明：基于线性化模型且服从高斯分布假设，新方法获得了较好的反演效果，得到的岩相后验概率较无构造约束多步方法更准确，客观表征了不确定性，为油藏表征、评价提供了有利依据。

Reservoir parameter characterization method based on joint probability inversion with structural constraints

Current methods for the reservoir parameter prediction and the uncertainty evaluation all use multi-step inversion, which makes it difficult to consider the uncertainty in each step. To this end, a reservoir parameter characterization method based on the joint probability inversion with structural constraints is proposed. The mixed Gaussian joint prior distribution associated with reservoir elastic parameters and physical parameters is first obtained based on well logs, followed by the single Gaussian one according to the sensitivity analysis of petrophysical parameters. Then, the geological structural information and well information are integrated into the inversion process through the least-squares well log interpolation with structural constraints. Finally, the analytical expressions of elastic parameters, physical parameters, and facies are defined by the Bayesian posterior distribution. Compared with traditional methods, the proposed method reduces cumulative errors and improves the accuracy of the prediction of reservoir parameters and uncertainty. The introduction of structural information and well information improves the lateral continuity and resolution of the inversion results. The conditional and blind well tests are carried out according to the actual data in Area M to verify the method. Also, we compare and analyze the difference between the inversion results of the new method and the multi-step one without constraints. The results show that under the assumption of linearized and Gaussian distribution, the new method achieves better inversion results with more accurate posterior probabilities. It objectively characterizes the uncertainties and provides a favorable basis for reservoir characterization and evaluation.