Method and system for predicting a drill string stuck pipe event

A computer system comprising:

• a processor;
• a memory coupled to the processor;
• wherein the memory storing a program that, when executed by the processor causes the program to:
• receive a plurality of drilling parameters from a drilling operation;
• apply at least a portion of the plurality of drilling parameters to a first machine-learning algorithm, the first machine-learning algorithm predicting a first probability of occurrence of the future stuck pipe event;
• apply at least a portion of the plurality of drilling parameters to a second machine-learning algorithm distinct from the first machine-learning algorithm, the second machine-learning algorithm predicting a second probability of occurrence of the future stuck pipe event;
• apply at least a portion of the plurality of drilling parameters to a third machine-learning algorithm distinct from the first and second machine-learning algorithms, the third machine-learning algorithm predicting a third probability of occurrence of the future stuck pipe event;
• combine the first, second, and third probabilities, the combing creates the combined probability;
• and
• provide an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.

A non-transitory computer-readable medium storing a program that, when executed by a processor, causes the processor to:

• receive a plurality of drilling parameters from a drilling operation;
• apply at least a portion of the plurality of drilling parameters to a neural network, the neural network predicting a first probability of occurrence of the future stuck pipe event;
• apply at least a portion of the plurality of drilling parameters to a decision tree, the decision tree predicting a second probability of occurrence of the further stuck pipe event;
• apply at least a portion of the plurality of drilling parameters to a support vector machine, the support vector machine predicting a third probability of occurrence of the further stuck pipe event;
• apply at least a portion of the plurality of drilling parameters to a Bayesian method, the Bayesian method predicting a fourth probability of occurrence of the further stuck pipe event;
• combine the first, second, third, and fourth probabilities, the combing creates the combined probability;
• and
• provide an indication of a likelihood of a future stuck pipe event to a drilling operator, the indication based on the combined probability.