Automated Framework Labels CT Job Type and Technology for Large Operational Data Sets
Authors: Carpenter Chris 
Published: January 2025

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218865, “Labeling Job Type and Technology for Large Operational Data Sets,” by Santiago Hassig Fonseca, SPE, Aymeric L. Jan, and Kristina Prokopetc, SLB, et al. The paper has not been peer reviewed.

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Each year, coiled tubing (CT) well-intervention fleets produce terabytes of multimodal data. These are recorded from surface and downhole sensors on each job. Among these data are job types and technologies used on each job. Traditionally, a field crew manually supplies this information. Given the diversity of data, many acquisition labels often are missing or inaccurate. In the complete paper, a multimodal framework is presented that identifies the job type and technologies used during an acquisition automatically.

Case Studies on Labeling Job Type and Technology

Four methods were used to label job type and technology for the different data types available. For unstructured data [e.g., bottomhole assembly (BHA) toolstring data in the design file, folder structure in the job archive], natural language processing (NLP) was used to label data based on libraries of existing keywords related to job types and technologies. For acquisition data, pattern-recognition methods were used to identify job type based on the behavior of the operational parameters and technology based on signals that typically are used to activate specific BHA modules (e.g., electronic firing heads). The channels contained in the acquisition data also were used to identify specific technologies that are run on the BHA. Each method is presented separately in the complete paper, with a discussion of its challenges, implementation, and results. The four methods include the following:

- Method 1: NLP for unstructured data

- Method 2: Pattern recognition of electronic firing heads using time-series data

- Method 3: Pattern recognition of cementing jobs using time-series data

- Method 4: Technology recognition using presence of channels

The first two of these methods are described in this synopsis.

Method 1: NLP for Unstructured Data.

Classification Based on Keywords.

An attribute-extraction and -classification model was built for labeling job type and technology by leveraging historical textual data. The labeling exercise used 21 job type and 20 technology labels.

For each job, the acquisition file contained time-series data for each channel that was recorded and header data that described administrative information such as the country, city, well name, field name, and description of the job. These header data were mostly free-text and unstructured. During the initial setup of the acquisition, the field engineer or supervisor fills out the header data as well as information about the BHA, which can be parsed to identify job type and technologies on that job. Each entry is dependent on the engineer’s description and susceptible to different naming conventions, abbreviations, and even typographical errors.

The free text description was extracted from the acquisition file, preprocessed, and classified.