Fahad A. Al-Ajmi

Permeability Estimation Using Hydraulic Flow Units in a Central Arabia Reservoir

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 HFUs 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 HFUs. 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 HFUs as indictor variables. Permeability profiles generated by HFUs using well log data agree with core data. A computer program was developed to perform hydraulic flow unit analysis. In the computer program, three main processing options were integrated, which are: ○ sensitivity runs are made to determine the optimal number of HFUs; ○ the analysis is then based on the optimal number of HFUs (or any user-defined number of HFUs); and ○ regression analysis is performed using the different HFUs as dummy variables to predict values permeability.

Navigating the Fog of Reservoir Uncertainties to Decision Makings with Advanced Mathematical Models in New Field Development

Reservoir development, most of the time coming at the heel of an exploration effort, faces enormous challenges in terms of uncertainties in all aspects of the event, especially with respect to the reservoir parameters. A survey by Bickel and Bratvold(1), highlighted the difficulty in the industry of making the connection from the uncertainty quantifications and analyses that are probabilistic to decisions that are deterministic. The survey also highlighted the observation that the decision making process has not improved in proportion with the industry’s capability pertaining to probability analyses.