Improved Resources and Reserves Estimation with the Combined Use of Scenario-Based Geological Models and Flow-Based Connectivity

Abstract

Adequate estimation of resources and reserves is critical for any prospect or field in the oil and gas industry. We propose improving the reliability of this estimation, first, by exploring the range of uncertainties through the use of scenario-based geological models; second, by ranking all generated models based on both static and dynamic responses; and, finally, by selecting few models representative of the uncertainty affecting the reservoir. In any field, the unknowns largely outnumber the known data, and, typically, some of these known data are biased due to preferential sampling. The classical approach controls the range of uncertainties based on multiple equiprobable realizations generated by using different random paths and by randomly sampling the data range. Our method improves that approach by considering additional realistic geological scenarios, which allows expanding the uncertainty space to sample from. A large number of models is generated as a result. Performing flow simulation on a large range of models would be computationally expensive. The common industry practice is to select models based on static volumetric measurements only. Our approach not only accounts for static volumes, but also adds computation of the flow-based connectivity using streamline simulation to account for the dynamic behavior of the models. A sensitivity analysis allows preserving only the most influential variables and discarding the negligible ones. Finally, static and dynamic responses from the remaining models are analyzed to identify scenarios with consistent low, mid, and high values for both responses. This results in the selection of few representative models that provide a greater assurance of recovering the probability distribution than would have been achieved by following common industry practices. Our methodology involves first enriching the uncertainty space by adding a dimension of geological scenarios. Then, it uses both static and dynamic responses to select a handful of representative models while still allowing adequate estimation of resources and reserves.