Iranian Journal of Oil and Gas Science and Technology

Abstract Residual oil saturation (Sor) estimation is a critical component of reservoir development and enhanced oil recovery (EOR) projects. Traditionally, Sor is estimated using the Archie method, in which accuracy strongly depends on petrophysical exponents, including the tortuosity factor (a), cementation factor (m), and saturation exponent (n). However, the conventional method assumes a homogeneous rock formation, rendering it ineffective and unreliable in shaly sand reservoirs. Additionally, field-based determination of petrophysical exponents and Sor is often difficult and time-consuming. This study addresses these limitations by developing a Python-based application that integrates a three-dimensional (3-D) regression technique with the Newton–Raphson algorithm. The application was tested using well logs from eight wells and statistically validated against core Sor data from the FAS Field, Offshore Niger Delta. Results indicated that the tortuosity factor (a) ranged from 0.28 (FAS-06) to 2.73 (FAS-04), the cementation factor (m) varied from 0.43 (FAS-06) to 4.11 (FAS-04), and the saturation exponent (n) ranged from 0.71 (FAS-04) to 8.59 (FAS-05). Correspondingly, Sor ranged from 0.11 (FAS-05) to 0.99 (FAS-06). The percentage deviation of the computed Sor relative to the core data ranged from 5% (FAS-01) to 27% (FAS-02) for the 3-D regression method and from 3% (FAS-03) to 52% (FAS-02) for the Newton–Raphson technique. The results indicate that the 3-D regression method is more efficient and reliable for computing petrophysical exponents and Sor in the study area.