System for controlling performance of salt water disposal wells

A data processing system for monitoring and managing day to day performance of a network of salt water disposal wells to prevent operational faults from occurring, the data processing system comprising:

• one or more sensors;
• at least one processor;
• and
• a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• performing a well measurement to generate salt-water disposal (SWD) well data;
• retrieving a machine learning model that is trained using labeled SWD data, the labeled SWD data representing one or more SWD well faults causing respective data signatures in the SWD data, each respective data signature being associated with a corresponding label identifying the SWD well fault for a particular SWD well;
• inputting the SWD well data generated in real-time based on SWD well operation, into the machine learning model;
• generating, by the machine learning model based on the inputting, a classification output representing a predicted well injection pressure or injection rate of the particular SWD well, the predicted well injection pressure or the predicted injection rate being outside a range of values representing normal operation of the particular SWD well;
• and
• generating, based on the prediction, control data for changing operation of the particular SWD well.

One or more non-transitory computer readable media storing instructions for monitoring and managing day to day performance of a network of salt water disposal wells to prevent operational faults from occurring, the instructions, when executed by at least one processor, configured to cause the at least one processor to perform operations comprising:

• performing a well measurement to generate salt-water disposal (SWD) well data;
• retrieving a machine learning model that is trained using labeled SWD data, the
• labeled SWD data representing one or more SWD well faults causing respective data signatures in the SWD data, each respective data signature being associated with a corresponding label identifying the SWD well fault for a particular SWD well;
• inputting the SWD well data generated in real-time based on SWD well operation, into the machine learning model;
• generating, by the machine learning model based on the inputting, a classification output representing a predicted well injection pressure or injection rate of the particular SWD well, the predicted well injection pressure or the predicted injection rate being outside a range of values representing normal operation of the particular SWD well;
• and
• generating, based on the prediction, control data for changing operation of the particular SWD well.