Geologic fault seal characterization

A system comprising:

• a processor;
• a memory accessible by the processor;
• and
• processor-executable instructions stored in the memory and executable to instruct the system to:
• stochastically generate at least fifty realizations for a subsurface geologic environment by sampling distributions for one or more parameters that characterize the subsurface geologic environment, wherein the at least fifty realizations represent different results for an actual, physical characteristic of the subsurface geologic environment, wherein the one or more parameters comprise pressure, temperature, flowrate, composition, or any combination thereof associated with the subsurface geologic environment, and wherein each of the at least fifty realizations is generated by:
• modeling petrophysical properties of the subsurface geologic environment based on the one or more parameters;
• performing a juxtaposition analysis of the subsurface geologic environment based on the one or more parameters;
• performing a fault clay content prediction for the subsurface geologic environment based on the one or more parameters;
• estimating a fault capillary threshold pressure for the subsurface geologic environment based on the one or more parameters;
• and
• computing a sealing capacity for the subsurface geologic environment based on the one or more parameters, wherein each of the at least fifty realizations is generated based on a parameter value for each of the one or more parameters for modeling the petrophysical properties, performing the juxtaposition analysis, performing the fault clay content prediction, estimating the fault capillary threshold pressure, and computing the sealing capacity, wherein the parameter value is different for at least two of:
• the modeling the petrophysical properties;
• the juxtaposition analysis;
• the fault clay content prediction;
• the estimation of the fault capillary threshold pressure;
• or
• the computation of the sealing capacity;
• rank the one or more parameters with respect to influence on the different results;
• generate one or more result predictions using a trained machine learning model for variations in values of at least a portion of the one or more parameters having a ranking above a threshold, wherein the trained machine learning model is trained using at least a portion of the at least fifty realizations and their corresponding different results, wherein the at least a portion of the at least fifty realizations correspond to the portion of the one or more parameters, and wherein the one or more result predictions comprise one or more indications of one or more physical objects being present in the subsurface geologic environment;
• and
• control a drill bit forming a borehole within the subsurface geologic environment based on the one or more result predictions.

One or more non-transitory computer-readable storage media comprising computer-executable instructions executable to instruct a computing system to:

• stochastically generate at least fifty realizations for a subsurface geologic environment by sampling distributions for one or more parameters that characterize the subsurface geologic environment, wherein the at least fifty realizations represent different results for an actual, physical characteristic of the subsurface geologic environment, wherein the one or more parameters comprise pressure, temperature, flowrate, composition, or any combination thereof associated with the subsurface geologic environment, and wherein each of the at least fifty realizations is generated by:
• modeling petrophysical properties of the subsurface geologic environment based on the one or more parameters;
• performing a juxtaposition analysis of the subsurface geologic environment based on the one or more parameters;
• performing a fault clay content prediction for the subsurface geologic environment based on the one or more parameters;
• estimating a fault capillary threshold pressure for the subsurface geologic environment based on the one or more parameters;
• and
• computing a sealing capacity for the subsurface geologic environment based on the one or more parameters, wherein each of the at least fifty realizations is generated based on a parameter value for each of the one or more parameters for modeling the petrophysical properties, performing the juxtaposition analysis, performing the fault clay content prediction, estimating the fault capillary threshold pressure, and computing the sealing capacity, wherein the parameter value is different for at least two of:
• the modeling the petrophysical properties;
• the juxtaposition analysis;
• the fault clay content prediction;
• the estimation of the fault capillary threshold pressure;
• or
• the computation of the sealing capacity;
• rank the one or more parameters with respect to influence on the different results;
• generate one or more result predictions using a trained machine learning model for variations in values of at least a portion of the one or more parameters having a ranking above a threshold, wherein the trained machine learning model is trained using at least a portion of the at least fifty realizations and their corresponding different results, wherein the at least a portion of the at least fifty realizations correspond to the portion of the one or more parameters, and wherein the one or more result predictions comprise indications of one or more physical objects being present in the subsurface geologic environment;
• and
• control a drill bit forming a borehole within the subsurface geologic environment based on the one or more result predictions.