Patent ID: 11933159
Assignee: ASPENTECH CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC E  G | IPC E  G

Claim 16:
17. A non-transitory computer program product for estimating wax or hydrate deposition in an oil well, the computer program product comprising a computer-readable medium with computer code instructions stored thereon, the computer code instructions being configured, when executed by a processor, to cause an apparatus associated with the processor to:
generate a dataset by loading one or more sets of oil well historical sensor measurement data, each set of oil well historical sensor measurement data comprising measurements related to an attribute of the oil well, and one or more sets of oil well historical event data, each set of oil well historical event data comprising date and time duration related to events of the oil well;
cleanse the generated dataset by identifying and removing measurements that are invalid in quality for modeling wax or hydrate deposition in the oil well;
enrich the cleansed dataset by deriving one or more feature variables and corresponding values based on measurements of oil well sensor variables, whereby enriching adds the values of the one or more derived feature variables to the cleansed dataset;
identify groups of highly correlated inputs by performing cross-correlation analysis on the enriched dataset, each identified group of highly correlated inputs comprising one or more of: measurements of a subset of the sensor variables and values of derived feature variables in the cleansed and enriched dataset;
perform feature selection using one representative input from each identified group of highly correlated inputs and outputting results to a sub-dataset;
identify and label data clusters using a machine learning unsupervised data clustering algorithm, thereby generating a dataset containing normal data ranges and events data for model building;
remove highly correlated sensors for oil well operation state identification;
determine normal data ranges and state conditions from clustered data;
build partial least square (PLS) models for operation state identification;
identify and label events from the built PLS models and thresholds;
iteratively split the dataset into a training dataset and a testing dataset, build and train an oil well model using the training sub-dataset, evaluate quality of the model using testing sub-dataset, and store the model when a termination criterion is met;
execute the built and trained oil well model to estimate wax or hydrate deposition in the oil well based on real-time oil sensor data.