Hydraulic flow units' estimation from seismic data using artificial intelligence systems, an example from a gas reservoir in the Persian Gulf

In recent years, considering the reservoir pressure drop in productive wells, designing the optimal well trajectory for production and injection in enhanced oil recovery (EOR) plans requires to determine hydraulic flow unit (HFU) in the reservoir. HFUs can also be used for petrophysical zonation of reservoirs as well as permeability predictions in uncored intervals or zones with low quality core data of wells. In the present study we have tried to integrate 3D seismic data with well data in order to find a quantitative relationship between flow zone index (FZI) and seismic attributes through employing linear regression and artificial intelligence models. Using this approach, FZI can be predicted using the information which is propagated in the whole field and achievable in the early stage of field development and consequently a suitable HFUs model may be represented. To this end, the suitable attributes for FZI estimation were selected by stepwise linear regression from extracted acoustic impedance (AI) and sample based attributes. Afterward, three optimal intelligent systems including probabilistic neural network (PNN), multi-layer feed forward network (MLFN), and radial basis function networks (RBFN) were employed. The obtained results reveal that PNN is the most accurate estimator compared to MLFN, RBFN, and multi-attribute regression methods. In the final stage, PNN was applied to develop 3D hydraulic flow unit model for the reservoir section of the investigated carbonate gas field located in Persian Gulf.