The popularity of the conjugation of two or more artificial intelligent (AI) models to design a single model for the exploration of hydrocarbon reservoirs has been increased in recent years. In this research, we have successfully predicted shear wave velocity (Vs) with a higher degree of accuracy through the integration of statistical and AI models using petrophysical data in a mixed carbonate–siliciclastic heterogeneous reservoir. In the designed code for the multi-model, first multivariate linear regression (MLR) is used to select the more relevant input variables from petrophysical data using weight coefficients of a suggested function. The most influential petrophysical data (Vp, NPHI, RHOB) are passed to ant colony optimization (ACOR) for training and establishing initial connection weights and biases of a back propagation (BP) algorithm. Afterward, the BP training algorithm is used for the final weights and the acceptable prediction of shear wave velocity. This novel methodology is illustrated by using a case study from the mixed carbonate–siliciclastic reservoir from one of Iran’s oilfields. The results show that the proposed integrated modeling can sufficiently improve the performance of the estimation of shear wave velocity and is a method applicable to mixed heterogeneous intervals with complicated diagenetic overprints. Furthermore, the predicted Vs from this model is well correlated with lithology, facies, and diagenesis variations in the formation. Meanwhile, the developed AI multi-model can serve as an effective approach to the estimation of rock elastic properties. More accurate prediction of rock elastic properties in a number of wells can reduce the uncertainty of exploration and save plenty of time and cost for oil industries

Keywords