The multiplicative inverse misfit correlation approach for depth correlation of porosity in reservoir modeling

Evaluating well-log and core-plug data to understand the heterogeneity of porosity in geologic formations is of utmost importance in reservoir studies. The well-log data and core-plug data are integrated in order to generate an accurate model describing the porosity distribution; however, these data exist at different scales and resolution, which necessitates scaling of one or both sets of the data. This study looked at the efficacy of using geostatistical techniques, in particular the likelihood method, to correlate data at different scales. The result was the development of a geostatistical scaling method combining variance, skewness, kurtosis and standard deviation by means of a misfit algorithm in conjunction with correlating the depth of the core-plug data within the well-log data through a scaling process in order to integrate porosity data. The geostatistical scaling method involves basic variogram models for scaling the computerized tomography (CT) plug data to well-log scale. Variance-based statistics were calculated within CT plug-size intervals, then a best fit for depth correlation determined. A new correlation algorithm, named the multiplicative inverse misfit correlation (MIMC) method, was formulated for accurate depth correlation. The application of the MIMC method identified the sampled depth enabling higher accuracy for correlations of core plugs or CT scans to the well-log depth and porosity. The MIMC method proved it has the capacity to correlate the depths of the CT data for each well, including depths within the determined uncertainty.