New Insights on Water Saturation Determination of Carbonate Reservoirs Using Artificial Intelligence Approach (AIA) and Conventional methods | ||
| Journal of Petroleum and Mining Engineering | ||
| Volume 25, Issue 1, August 2023, Pages 58-64 PDF (1.27 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jpme.2023.172751.1144 | ||
| Authors | ||
| ghareb hamada* 1; Abdelrigeeb A. ElKadi2; Abbas M. Alkhudafi2 | ||
| 1Petroleum Engineering dept. The American University of Kurdistan, Kuridistan, Iraq, | ||
| 2Petroleum Engineering Department, Faculty of Engineering, Hadhramout University, Yemen | ||
| Abstract | ||
| Carbonate reservoir rocks are considered heterogeneous and it is due to complex pores pattern caused by different diagenetic factors that are modifying the microstructures and matrix system. parameters and finally leading to significant petrophysical heterogeneity and anisotropy. Water saturation determination in carbonate reservoirs is crucial parameter to determine initial reserve of given an oil field. Water saturation determination using electrical measurements is based on Archie’s formula. Consequently, accuracy of Archie’s formula parameters affects seriously water saturation values. Determination of Archie’s parameters (a, m and n) is proceeded by three techniques conventional, CAPE and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting an accepted value of Archie’s parameters and consequently reliable water saturation values. This work focuses on calculation of water saturation using Archie’s formula. Different determination techniques of Archie’s parameters such as conventional technique, CAPE technique and 3-D technique have been tested and then water saturation was calculated using Archie’s formula with the calculated parameters (a, m and n). This study introduced parallel self-organizing neural network (PSONN) algorithm predict Archie’s parameters and determination of water saturation. Results have shown that predicted Arche’s parameters (a, m and n) are highly accepted with statistical analysis lower statistical error and higher correlation coefficient than conventional determination techniques. The developed PSONN algorithm used big number of measurement points from core plugs of carbonate reservoir rocks. PSONN algorithm provided reliable water saturation values. | ||
| Keywords | ||
| Water saturation; Artificial intelligence; PSONN; carbonate reservoir | ||
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