Wellbore Schematics to Structured Data Using Artificial Intelligence Tools

Abstract

Wellbore schematics are essential to well planning and operations because they detail well design, completions, and sometimes the production mechanism. There are multiple formats and types of wellbore schematics; however, they generally consist of a well diagram accompanied by tables of annotations listing components and equipment details such as depths and diameters. Paper-based wellbore schematic reports are often distributed as the primary account of technical information concerning old wells after being acquired by oil and gas operators. Any intervention or further operation on those wells would require a thorough and manual interpretation of those reports, which can be lengthy and prone to errors. Therefore, to automatically convert the diagram and annotations into a readable database, a practical technique or tool has to be developed. Artificial intelligence (AI)-powered image analysis addresses similar problems for other engineering disciplines and industries, and with the latest advances for software and computer hardware capabilities, it is possible to design specialized solutions for the oil and gas industry. Therefore, a methodology was defined and implemented to import the available machine learning technology for automating the interpretation and analysis of wellbore schematics. With this novel tool, scanning the paper-based wellbore schematic results in digital and easily shareable structured data that can be used to regenerate a digital wellbore schematic. This method analyzes the diagram and the annotations on the wellbore schematic file and then combines the analysis results by matching the diagram with the surrounding annotations and engineering constraints. The methodology was tested on a set of wellbore schematic files, and digital schematics were regenerated. Fundamental components and equipment were detected that matched the original schematics in terms of depths and diameters. The designed tool saves considerable time and effort while providing accuracy and repeatability. These results highlight some of the benefits of applying multidisciplinary ideas for data management to the industry. The object detection technique in image analytics is new to the oil and gas industry for identifying components in well schematics. Further, this project is comprehensive because it identifies the diagram and related annotations. Challenges and breakthroughs experienced in this research will be addressed.