New Correlations for Predicting Niger Delta Oil Reservoirs Recovery Factors

Abstract

Abstract The importance of estimating an asset's expected oil recovery factor (RF) cannot be overemphasized. It remains an essential step in asset valuation and can be achieved through several techniques including the use of correlations. This paper provides novel correlations for estimating RFs for oil reservoirs in the Greater Ughelli & Coastal Swamp depobelts of the Niger delta. Statistical multivariant regression method was employed in the development of the new RF correlations models using actual oil field data obtained in the early life of the fields in the Niger Delta. Eleven variables were gathered for each reservoir based on parameters identified to have influence or relationship with oil RF from literature. Data from 228 oil reservoirs were screened, only 69 oil reservoirs under water drive and solution gas drive in the Greater Ughelli depobelt and Coastal Swamp depobelt were found to have complete dataset valid for the study. The study showed that oil gravity and shrinkage factor are not statistically relevant in developing RF correlation for the Greater Ughelli depobelt under water drive and solution gas drive respectively. The developed correlations were validated with additional actual field data from the Niger Delta, and the new models' results were compared with those obtained from Guthrie & Greenberger industrywide correlation and Onolemhemhen et al model developed for the Niger Delta. The new models provided better predictability and reliable results. The new correlations had coefficient of determination (R2) of not less than 0.68 with the field data while those of Guthrie & Greenberger and Onolemhemhen et al. models had R2 values less than 0.11. The new correlations are recommended for quick valuation of primary oil recovery from oil assets within the Greater Ughelli and Coastal Swamp depobelts of the Niger Delta as they provide more reliable estimates of oil RF for fields within the region.