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In the oil and gas industry, rig reports are essential for monitoring the performance and operations of drilling rigs. These reports contain a vast amount of unstructured data, including comments from rig personnel regarding rig activities such as drilling progress, equipment maintenance, process interruptions, and safety observations (Figure 1). In blog #1, we discussed a scalable solution that can extract data from rig reports and convert unstructured PDF reports into structured databases. In this blog, we will discuss how to use the extracted data from these reports for machine learning (ML) purposes—specifically, rig operation code conversion from one operator’s code to another operator’s code using text classification.
The purpose of logging daily rig operations is twofold: first, to have a record of what operations have been performed, and second, to learn from historical data to improve future rig operations and optimize rig performance, reduce downtime and carbon emissions, and improve process safety. To analyze daily rig operational data, various operations are classified into primary and secondary codes, which are specific to each individual oil company. In the case of joint venture programs, oil companies need to convert rig operation codes from each other daily. Traditionally, these operation codes are converted manually, which is a tedious, time-consuming, and costly task and can lead to delays in identifying and addressing critical issues that could impact rig operations. A self-adaptive ML model to perform automated code conversion from one operator to another can transform the way oil companies traditionally perform the same task. In this blog post, we propose an intelligent rig operations classification solution built on Amazon Web Services (AWS).