Halliburton Energy Services, Inc.

Geetha Gopakumar Nair

Ashwani Dev

Satyam Priyadarshy

Ajay Pratap Singh

Suresh Venugopal

Shashwat Verma

Janvi Nayan Acharya

Gurunath Venkatarama Subrahmanya Gandikota

Pradyumna Singh Rathore

Roxana Nielsen

Richa Choudhary

A method comprises receiving a time series of data values for a time window of each operational parameter of a number of operational parameters of equipment; calculating a time derivative feature that comprises a change of the data values of a first operational parameter of the number of operational parameters over the time window; and classifying, using a machine learning model and based on the time derivative feature, an operational mode of the equipment into different failure categories.

Machine learning based equipment failure prediction

The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

Drill bit repair type prediction using machine learning

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Katy, TX, United States of America

Geetha Gopakumar Nair