The automatic interpretation of well logs has been the focus of research, especially in oil and gas industry, for more than 50 years and, aside from that, benefits from the fast developments of machine learning algorithms during the recent decades. Moreover, Bayesian inference is increasingly utilized to model geological data, enabling the consideration of all available information and a quantification of uncertainties. In order to combine unsupervised segmentation of well data with 3D geological modeling, a fully automated approach to directly create threedimensional structural models from raw well data is intended and, further, tested on synthetic data with different standard deviations.
For this purpose, unsupervised segmentation, which considers the statistical nature as well as the spatial correlation of the data, is combined with a zonation method that extracts interface information from clustered data by maximizing probabilities within continuous zones. This data is then screened to automatically obtain geological information and, furthermore, is inserted into the structural modeling algorithm, which is based on implicit potential-field interpolation while at the same time honoring the geological spatial continuity.
It is shown that unsupervised segmentation is capable of segmenting raw well logs and that the zonation appropriately determines boundaries between stratigraphic units. Model reconstruction demonstrates that the fully automated process is proffcient at recovering several common subsurface structures. Moreover, the implementation of a three-dimensional model in the segmentation process, filling the empty space between boreholes, reduces uncertainties in the geological modeling routine. The combination of unsupervised segmentation and 3D geological modeling, resulting in a fully automated process, taking all available information into consideration, is found to be a suitable method in order to build structural geological modeling directly from raw well logs.