Estimation of formation water saturation (Sw) using log data is an important approach in the oil exploration and characterization of a hydrocarbon reservoir. Therefore, it seems that the proper prediction/simulation of Sw is essential. The first objective of this study More
Estimation of formation water saturation (Sw) using log data is an important approach in the oil exploration and characterization of a hydrocarbon reservoir. Therefore, it seems that the proper prediction/simulation of Sw is essential. The first objective of this study was to develop a predictive model for Sw estimation based on hybrid cluster analysis with piecewise nonlinear regression, and after that, using the developed model, Sw was simulated by the Monte Carlo simulation (MCS). In order to achieve objectives of this study, a group of 909 data points was used for model construction and 302 data points were employed for assessment of model. The obtained results of MCS modeling indicated that this approach is capable of simulating Sw ranges with a good level of accuracy. The mean of simulated Sw by MCS was obtained as 0.28 m, while this value was achieved as 0.29 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that RHOB is the most influential parameter on Sw among all model inputs. It is noticeable that the proposed hybrid cluster analysis with piecewise nonlinear regression and MCS models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.
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