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Endila develops AI drilling solution for KCA Deutag

Analysis of massive amounts of unstructured comments provides continuous opportunities to help operators identify root causes of NPT, invisible lost time.

KCA Deutag is on a mission to make drilling operations more efficient and cost-effective through the application of artificial intelligence (AI) technologies. The company’s latest machine learning tool uses natural language processing to enable bulk analysis of text-based drilling reports to illuminate systemic causes of costly lost time.

Now implemented on over 91% of the KCA Deutag-operated rig fleet, the machine learning model uses a sophisticated algorithm to assess textual comments submitted to the company’s existing enhanced drilling reporting system (EDRS). Usually, the unstructured nature of such written comments would make them difficult to analyze. However, the algorithm automatically classifies each entry as either a rig problem, a well problem, normal operation, HSE event or waiting time. This categorization allows the company’s performance teams to more easily analyze large volumes of drilling reports to help identify recurring lost-time trends. The tool is especially helpful in pinpointing invisible lost time, or inefficient practices that can hamper well progress even without complete downtime.

KCA Deutag initiated the project in February 2020 in collaboration with AI specialist Endila, which provided the data science expertise, and the UK-based Oil and Gas Technology Centre, which contributed funding. The goal was to help customers, some of whom were experiencing 20-40% overall NPT per well, said technology engineer Anna Moffat, who managed the project.

What it had were 4 million rows of unanalyzed free-text data from its internal EDRS database. Drillers on every KCA Deutag-operated rig enter information about drilling activity into this system. While most data can be entered via drop-down menus, individuals can also enter text, describing rig activities in their own words (e.g., “Drill from 34 to 64 m”). After initial submission and review, these comments are typically never viewed again, unless required as part of an operational investigation.

The KCA Deutag performance teams believed that an analysis of these unstructured comments could reveal important insights into the root causes of customer NPT and ILT. However, unlike the structured data from the drop-down menus, which could be bulk analyzed, there was no effective way to dissect and analyze these free-text entries on a macro scale.

Not only was the volume of text huge – and growing with new entries every day – but the comments also contained a lot of “noise,” such as inconsistent abbreviations, spelling errors, punctuation and special characters. An AI approach was needed that could remove this noise and put the comments in a format that would support complex data analytics...

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