Global model for failure prediction for artificial lift systems

A computer-readable medium having computer-executable instructions stored thereon which, when executed by a computing system, cause the computing system to perform a method for predicting failures in an artificial lift system, the method comprising:

• extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems;
• identifying pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure;
• forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system;
• and
• predicting one or more failures in an artificial lift system based on the learning model.

A system for predicting failures in an artificial lift system, the system comprising:

• a processor;
• a memory communicatively connected to the processor and storing computer-executable instructions that, when executed by the processor, cause the system to:
• receive a dataset of time-sampled data from each of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems;
• receive data labels from a user, the data labels defining one or more types of failures of artificial lift systems;
• identify pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure included in the one or more types of failures;
• generate a learning model by a multi-class support vector machine based on the labeled data, the learning model including one or more identified pre-failure signatures and predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system.