Static Reservoir Modeling Comparing Inverse Distance Weighting to Kriging Interpolation Algorithm in Volumetric Estimation. Case Study: Gullfaks Field

Understanding and characterizing the behaviour of the subsurface by combining it with a suitable statistical method gives a higher level of confidence in the reservoir model produced. Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling. The most widely used interpolation algorithm, kriging, with enough well data is the best linear unbiased estimator. This research sought to compare the applicability and competitiveness of inverse distance weighting (IDW) method using power index of 1, 2 and 4 to kriging when there is sparse data, due to time and budget constraints, to calculate hydrocarbon volumes in a fluvial-deltaic reservoir. Interpolation results, estimated from descriptive statistics, were insignificant and showed similar prediction accuracy and consistency but IDW with power index of 1 indicated the least error estimation and higher accuracy. The assessment of hydrocarbon volume calculations also showed a marginal difference below 0.08 between IDW power index of 1 and kriging in the reservoir zones. Reservoir segments cross-validation and correlation analysis results indicate IDW to have no significant difference to kriging with absolute errors of 3% for recoverable oil and 0.7% for recoverable gas. Grid upscaling, which usually causes a loss of geological features and extreme porosity values, did not impact the results but rather complemented the robustness of IDW in both fine and coarse grid upscale. With IDW exhibiting least errors and higher accuracy, the volumetric and statistical results confirm that when there are fewer well data in a fluvial-deltaic reservoir, the suitable spatial interpolation choice should be IDW method with a power index of 1.