Quantitative Characterization of Porosity Partitioning from NMR Logs in Siliciclastic Reservoirs - Ecuador

Abstract

The "Oriente" basin is located in eastern Ecuador between the Andes Mountains and the Amazon rainforest. In 2012, daily oil production reached 505,000 barrels. The three main oil-bearing Cretaceous formations in the basin are the Hollin, T and U formations. Results from recent extensive coring of the U and Hollin formations showed that the pore size significantly affects oil saturation and production. Therefore, understanding pore size distribution can greatly enhance the success of a well. It is a major challenge to characterize and classify reservoir type and heterogeneity in reservoirs with pore-size variations using only well log data. We used core data from three wells in the U and Hollin formations to validate a new nuclear magnetic resonance (NMR) spectral analysis technique, applied in the echo domain, to estimate the pore-size distribution. In certain carbonate reservoirs in the Middle East, the distribution of pore size classes can be accurately determined by fitting the NMR pulse echoe. The method was blindly tested on three siliciclastic wells from the Oriente basin, and the results were compared with pore-size analysis from mercury-injection and capillary-pressure data. Additionally, a multi-mineral petrophysical model was built for each eall from log measurements, omitting the core data. The porosity derived from the multi-mineral model was used as a porosity input to guide the time-domain inversion of the NMR echo trains. The inversion solves for continuous logs of the porosity, attributed to three pore families, representing the range of pore-body sizes from small to medium to large. After completing the log-based classification into three pore families, the resulting porosity logs were compared to the analysis of core samples for several oilfields. For all formations and in all fields, the core-analysis inversion data was in good agreement with the time-domain NMR inversion results. These results were used to select optimum intervals to be completed and to predict production in the studied fields.