Improve the Reliability of 3d Geological Model for Terrigenous Sandstone Reservoir, F Block, Dh Field, Nam Con Son Basin by Using Gaussian Random Function Simulation (GRFS) and Hydraulic Flow Units (HFU) Integrated Artificial Neural Network (ANN)

The geological model includes: structural modeling, facies modeling and property modeling. In development stage of field, due to the complication of containing layer, the reservoir rock distributions and the parametric model must be built in more detail so that the lithological physical characteristics of the stratum are reasonably presented. Therefore, it is still simulating the reservoir according to the litho-facies including containing rock and non-contain rock but dividing the reservoir rock into different types of HFU (Hydraulic Flow Units) by the method of ANN (Artificial Neural Network), based on their porosity properties (Core-sample analysis results) were used in the facies modeling step to reflect more clearly the connection of the oil bodies, as well as the heterogeneity of the containing layer. 

Accordingly, the facies model, the random models of the porosity, permeability and water saturation built for Terrigenous Sandstone Reservoir, F Block, Dh Field, Nam Con Son Basin all show similarities of the general trends in the reservoir. The reservoirs of field DH has good quality, reflected in porosity, high NTG, and low water saturation. The process of checking the accuracy of the model is conducted by comparing data from the probability model and input data, ensuring that it does not exceed the allowable limit (<10%).

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.