Machine learning of geology by probabilistic integration of local constraints

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• gathering seismic data for a reservoir with unknown fractured and unfractured areas;
• generating a structural model based on the seismic data for the reservoir;
• building a geomechanical model based on the structural model;
• determining, using the geomechanical model, geomechanically-estimated fractured areas 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;
• determining, using machine learning based on mathematical calculations of the seismic data, machine learning-based estimates of a likelihood of a fracture of each area of the reservoir, wherein a likelihood lower than a third threshold likelihood indicates an unfractured area, and wherein a likelihood greater than a fourth threshold likelihood indicates a fractured area;
• and
• determining, using the geomechanically-estimated fractured areas, the machine learning-based likelihoods, and Bayes' Rule, the fractured and unfractured areas based on where fractures are likely to exist and not exist.

A computer-implemented system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising:
• gathering seismic data for a reservoir with unknown fractured and unfractured areas;
• generating a structural model based on the seismic data for the reservoir;
• building a geomechanical model based on the structural model;
• determining, using the geomechanical model, geomechanically-estimated fractured areas 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;
• determining, using machine learning based on mathematical calculations of the seismic data, machine learning-based estimates of a likelihood of a fracture of each area of the reservoir, wherein a likelihood lower than a third threshold likelihood indicates an unfractured area, and wherein a likelihood greater than a fourth threshold likelihood indicates a fractured area;
• and
• determining, using the geomechanically-estimated fractured areas, the machine learning-based likelihoods, and Bayes' Rule, the fractured and unfractured areas based on where fractures are likely to exist and not exist.