Interpolation of Geological Properties Based on Deep Seismic Attributes

Conventional geostatistical methods of spatial interpolation (e.g., inverse distance weighting or kriging) are based on spatial distance and become inaccurate in regions far from sample wells or in the case of geological discontinuities. It looks more natural to estimate geological properties using similarity, not by distance, but by geological conditions, which, in turn, can be estimated using seismic surveys. In this research, we use machine learning to derive seismic attributes that describe the local geological environment and apply interpolation in the resulting attribute space to propagate geological properties from the sample wells to the whole reservoir model. In this representation, geologically similar regions become close in the attribute space even when they are separated in the initial spatial 3D domain. Using an open synthetic dataset, we demonstrate that this approach improves propagation of various geological properties compared to the conventional spatial method and remains effective even with a small number of sample wells. The main advantage of this approach is that, while preserving the simplicity and transparency of geostatistical methods, it enables the use of seismic data without preliminary interpretation to obtain geologically consistent and quick estimates of geological properties in the 3D domain far from sample wells.