Flow zonation and permeability estimation is a common task in reservoir characterization. Typically, integration of openhole log data with a conventional and special core analysis solves this problem. We present a Bayesian-based method for identifying hydraulic flow units in uncored wells using the theory of hydraulic flow units (HFUs) and subsequently compute permeability using wireline log data.

We use a nonlinear optimization scheme on the basis of the probability plot to determine pertinent statistical parameters of each flow unit. Next, we couple these results with the F-test and the Akaike’s criteria with the purpose of establishing the optimal number of HFUs present in the core data set. Then, we allocate the core data into their respective HFUs using the Bayes’ theorem as clustering rule. Finally, we apply an inversion algorithm on the basis of Bayesian inference to predict permeability using only wireline data.

We illustrate the application of the procedure with a carbonate reservoir having extensive conventional core data. The results show that the Bayesian-based clustering and inversion technique delivers permeability estimates that agree with the core data and with the results obtained from a pressure transient analysis.

流动分区和渗透率估算是储层表征中的常见任务。通常，裸眼测井数据与常规和特殊岩心分析的集成解决了这个问题。我们提出了一种基于贝叶斯的方法，使用液压流量单位（HFU）的理论来识别无芯井中的液压流量单位，然后使用电缆测井数据来计算渗透率。

我们在概率图的基础上使用非线性优化方案来确定每个流量单元的相关统计参数。接下来，我们将这些结果与F检验和Akaike标准相结合，以建立核心数据集中存在的HFU的最佳数量。然后，我们使用贝叶斯定理作为聚类规则将核心数据分配到它们各自的HFU中。最后，我们基于贝叶斯推断应用反演算法，仅使用有线数据预测渗透率。

我们举例说明了该方法在碳酸盐岩储层中具有广泛常规岩心数据的应用。结果表明，基于贝叶斯的聚类和反演技术所提供的渗透率估算值与岩心数据以及从压力瞬态分析获得的结果相符。