Permeability Estimation Using Hydraulic Flow Units in a Central Arabia Reservoir

Abstract

Abstract Knowledge of permeability is critical to developing an effective reservoir description. Permeability data can be obtained from well tests, cores or logs. Normally, using well log data to derive estimates of permeability is the lowest cost method. To estimate permeability, we can use values of porosity, pore size distribution, and water saturation from logging data and established correlations. One benefit of using wireline log data to estimate permeability is that it can provide a continuous permeability profile throughout a particular interval. This paper will focus on the evaluation of formation permeability for a sandstone reservoir in Central Arabia from well log data using the concept of Hydraulic Flow Units (HFU). Cluster analysis is used to identify the hydraulic flow units. We have developed a new clustering technique that is unbiased and easy to apply. Moreover, a procedure for determining the optimal number of clusters that should be used in the HFU technique will be introduced. In this procedure, the sum of errors squared method was used as criterion for determining the required number of HFU's to describe the reservoir. In our work, the statistically derived hydraulic flow units were compared with the core description made at the well site by a geologist. The grain size classes from core description match very well with the statistically derived clusters from the HFU method. Our results indicate that hydraulic flow units correspond to different rock types in this Central Arabian Reservoir. Of course, direct measurement of rock properties using cores is the ideal method to determine HFU's. However, because the costs to cut and analyze cores are so high, few core measurements are routinely available. Hence, it is crucial to extend the flow unit determination to the un-cored intervals and wells. The relationship between core flow units and well log data was established by non-parametric regression in cored wells, and then was used as a tool to extend the flow units prediction to un-cored intervals and wells. Permeability estimation using the HFU method was extended to un-cored wells by implementing the Alternating Conditional Expectation (ACE) algorithm. ACE provides a data-driven approach for identifying the functional forms for the well log variables involved in the correlation. The reservoir porosity vs. permeability relationship was represented with single equation by using the different HFU's as indictor variables. Permeability profiles generated by HFU's using well log data agree with core data.