Robust and Efficient Identification of Hydraulic Flow Units using Differential Evolution Optimization and Two-Stage Clustering Techniques

One essential process in reservoir characterization is the identification of hydraulic flow units (HFUs). It plays a critical role in determining hydrocarbon reserves and improving reservoir productivity. Flow zone indicator (FZI), determined from core data, is widely used to identify HFUs. One of the challenges in the FZI technique is that the number of HFUs is identified using qualitative methods and subjective estimation. This work proposes robust methods to identify the optimal HFUs using differential evolution (DE) and two-stage clustering. The first method tested in this work enumerates through a large number of HFUs scenarios using 10 clustering algorithms and different input parameters (number of clusters, minimum number of samples, etc.). The scenario with the largest average correlation coefficient is selected as optimum. The second method uses the DE algorithm to maximize the average correlation coefficient and hence obtain the optimal HFUs. The third method consists of two stages. The first stage uses the OPTICS clustering algorithm to determine the number of HFUs, while the second stage generates the desired clusters using the Gaussian mixture algorithm. Both iterative evaluation and DE optimization methods achieved the same clustering results. However, DE optimization resulted in 85% reduction in runtime due to the robust search capability of the DE algorithm which leads to the solution more efficiently. Furthermore, another significant reduction in runtime was achieved using the two-stage clustering method which yielded very close results. The proposed methods in this work provide unique and potential opportunity to improve the use of FZI data analysis to identify HFUs. This work uses the power of clustering and stochastic algorithms to support a critical process in reservoir characterization.