Reservoir Characterization and Prediction Modeling Using Statistical Techniques

Abstract

Reservoir characterization and prediction modeling have long been among the more challenging tasks in geothermal reservoir engineering. The main reason is the presence of fractures and faults, which control the mass and heat transport in the subsurface. In this work, the applicability of using statistical methods for reservoir characterization as well as prediction modeling was explored. Three methods were analyzed and applied on a synthetic library of fracture networks. First, the Alternate Conditional Expectation (ACE) algorithm was used to estimate well-to-well connectivity between injection and production wells using tracer return and temperature data. The results obtained with tracer data were in good agreement with tracer transit times, for 80.5% of the fracture networks the ACE connectivity was within ±0.05 of the connectivity implied by transit time, while temperature data showed much less correlation to connectivity with the ratio reduced to 58.3%. Second, k-means clustering was applied where fractures of similar character were grouped together and interwell connectivity and thermal behavior estimated. The method displayed potential but main limitations were deciding on number of clusters and the growing complexity with added producers. Third, preliminary results using direct forecasting with Canonical Functional Component Analysis (CFCA) were presented. A significant reduction in the predicted range of thermal responses for production wells was obtained but introducing more complex data is likely to cause data-prediction relationships to become less linear.