Bayesian Based Approach for Hydraulic Flow Unit Identification and Permeability Prediction: A Field Case Application in a Tight Carbonate Reservoir

Abstract

Abstract Flow zonation and permeability estimation is a common problem in reservoir characterization; usually, integration of openhole log data with conventional and special core analysis solves the latter. We present a Bayesian based method for identifying hydraulic flow units in uncored wells using the theory of Hydraulic Flow Units (HFU) and subsequently compute permeability using wireline log data. First, we use the F-test and the Akaike's criteria coupled with a nonlinear optimization scheme based on the probability plot to determine the optimal number of HFU present in the core dataset with the regression match giving the pertinent statistical parameters of each flow unit. Second, we cluster core data into its respective HFU by using the Bayes' rule. Finally, we apply an inversion algorithm based on Bayesian inference to predict permeability using only wireline data. We illustrate the application of the procedure with a carbonate reservoir having extensive core data. The results showed the Bayesian-based clustering and inversion technique delivered permeability estimates in agreement with core data as well as with results obtained from pressure transient analysis. Among the applications of the workflow presented are better productivity index assessments, enhanced petrophysical evaluations, and improved reservoir simulation models. Coupling of Nonlinear optimization with Bayesian inference proves a robust way for performing data clustering providing unbiased estimations