Automatic Drilling Operations Coding and Classification Utilizing Text Recognition Machine Learning Algorithms

Digital transformation is becoming a major goal for oil and gas (O&G) companies, and one major component of digital transformation initiatives is utilizing artificial intelligence (AI) and machine learning (ML) algorithms/techniques to automate critical business processes for the sake of consistency and objectivity. Drilling operations classification and coding is always challenging due to the subjectivity of end users; however, using machine learning and big data to automate operations classification based on natural language operations descriptions provided by drilling personnel can ensure objective and consistent classification. In this paper, a new approach is introduced for using ML algorithms to classify drilling operations based on the operation description provided by rig personnel in their morning reports, with time broken down using natural language. The new ML predictive model learns from historical data how to define the proper coding of drilling operations based on operation description to minimize human interaction with the coding system for drilling operations. The approach utilizes a set of prediction models to predict the proper code combinations that classify the activity carried out on the rig floor. Each model of these proposed models will feed its prediction into the next model to define the next level of code predictions. The model was trained using around 800,000 records to define the coding patterns based on operation remarks and then predicted a multi-level operational coding for any given operational remarks. The model has been tested and validated with around 800,000 records from datasets from multiple years, and showed significate results when compared to recent reports and subject matter expert evaluations showing a very good level of consistency and level of accuracy of more than 80%. The result of this work is an ML classification model that can reduce daily morning report data entry by 40% and improve the operational classification quality and consistency significantly. This approach also improves internal processes such as services invoicing verification, bit selection and end-of-well reporting.