BUILDING A MODEL FOR RESERVOIR CHARACTERISATION IN GIS USING MACHINE LEARNING
The authors have tried to build a 3D model for reservoir characterization. The model is planned in such a way to accommodate multiple wells with their Petro-physical data spatially using different grids and then integrating the data to determine the reservoir characteristics for unknown locations in 3D. Initially, the model is planned using well log data of Equinor Volve field (central part of North Sea). Computational analysis for reservoir characterization was conducted in GIS type platform using ML approach integrating with MATLAB and PYTHON plugins. The model provides an opportunity to determine reservoir characteristics at desired X, Y, Z coordinate. However, there remain important challenges of deciding the size of the 3D grid, vis a vis availability of data, assigning the data to grid cell, assigning weights to each populated grid, and ascertainment of the model to relate a surface between known grid cell, and checking the accuracy of a fit surface from various directions in 3D. On analysis of the grid data for wells, it came out that for few places the values are more homogeneous while at other, they are abruptly changing. Various methods of reservoir characterization have been referred to which use a different technique of data evaluation at unknown points. Once the grids were populated with known data, unknown grid locations were ascertained with interpolation such as nearest neighbour and linear method. Initially, interpolation was tried to be made in X-Y, X-Z, Y-Z plane and then at a plane in any direction in 3D. Multi interpolations have been used in the model that enables authors to view a desired surface in the reservoir to suggest the best possible direction of drilling to hit the correct pay zones. Even though uncertainty will be encountered but authors have strived to suggest a probable way to proceed from the available data.