MACHINE LEARNING OF GEOLOGY BY PROBABILISTIC INTEGRATION OF LOCAL CONSTRAINTS

Systems and methods include a computer-implemented method: Seismic data is gathered for a reservoir with unknown fractured and unfractured areas. A structural model is generated. A geomechanical model is built. Geomechanically-estimated fractured areas are determined using the geomechanical model, including: areas where fractures are not likely to exist based on a likelihood lower than a first threshold likelihood, areas where fractures are likely to exist based on a likelihood greater than a second threshold likelihood, and areas where fracturing is unknown based on a likelihood between the first threshold likelihood and the second threshold likelihood. Machine learning-based estimates of a likelihood of a fracture of each area of the reservoir are determined using machine learning based on mathematical calculations of the seismic data. Fractured and unfractured areas are determined based on where fractures are likely to exist or not using the geomechanically-estimated fractured areas, machine learning-based likelihoods, and Bayes' Rule.