Systems and methods for training a well model to predict material loss for a pipe string within a borehole

A system for training a well model to predict material loss for a pipe string having a wall thickness and located within a borehole, the system comprising:

• a logging tool operable to measure a wall thickness of a first pipe string at locations axially along the first pipe string;
• and
• a processor in electronic communication with the logging tool, the processor programmed to:
• receive, from the logging tool, wall thickness measurements of a first pipe string at locations axially along the first pipe string at a first time;
• receive, from the logging tool, wall thickness measurements of the first pipe string at the locations at a second time;
• train a first well model based on an ML algorithm to predict a predicted amount of material loss in the future for the first pipe string at a selected location using the wall thickness measurements at the first and second times and well operating condition information related to the first pipe string;
• determine an amount of actual material loss for the first pipe string at the locations based on the wall thickness measurements;
• receive input from a user relating to user-defined color-coded categories based on the amount of actual material loss for the pipe string;
• categorize the locations into the user-defined color-coded categories at the respective locations;
• and
• generate a graphical user interface showing a visual representation of the amount of actual material loss for the first pipe string using the user-defined color-coded categories on a display.

A non-transitory computer-readable medium comprising instructions which, when executed by a processor, enables the processor to perform a method for training a well model to predict material loss for a pipe string having a wall thickness and located within a borehole, the method comprising:

• receiving wall thickness measurements of a first pipe string at locations axially along the first pipe string at a first time;
• receiving wall thickness measurements of the first pipe string at the locations at a second time;
• training a first well model based on an ML algorithm to predict a predicted amount of material loss in the future for the first pipe string at a selected location using the wall thickness measurements at the first and second times and well operating condition information related to the first pipe string;
• determining an amount of actual material loss for the first pipe string at the locations based on the wall thickness measurements;
• receiving input from a user relating to user-defined color-coded categories based on the amount of actual material loss for the pipe string;
• categorizing the locations into the user-defined color-coded categories at the respective locations;
• and
• generating a graphical user interface showing a visual representation of the amount of actual material loss for the first pipe string using the user-defined color-coded categories on a display.