Data Mining for Petroleum Geology

In petroleum geology, exploration and production wells are often analysed using image logs, because they provide a visual representation of the borehole surface and they are fundamental to retrieve information on bedding and rocks characteristics. Aim of this Ph.D. work was to define and implement a suite of automatic and semi-automatic tools for interpretation of image logs and large datasets of subsurface data coming from geological exploration. This led to the development of I2AM (Intelligent Image Analysis and Mapping), a semi-automatic system that exploits image processing algorithms and artificial intelligence techniques to analyse and classify borehole images. More in detail, the objectives of the I2AM approach are: (1) to automatically extract rock properties information from all the different types of data recorded/measured in the wells, and visual features from image logs in particular; (2) to identify clusters along the wells that have similar characteristics; (3) to predict class distribution over new wells in the same area. The main benefits of this approach are the ability to manage and use a large amount of subsurface data simultaneously. Moreover, the automatic identification of similar portions of wells by hierarchical clustering saves a lot of time for the geologist (since he analyses only the previously identified clusters). The interpretation time reduces from days to hours and subjectivity errors are avoided. Moreover, chosen clusters are the input for supervised learning methods which learn a classification that can be applied to new wells. Finally, the learned models can also be studied for a cluster characterization, in a descriptive approach.