Accelerating Wellbore Domain Interpretations Using Generative AI and Knowledge Graphs

Abstract The petroleum industry relies heavily on accurate wellbore domain interpretation for effective resource management and extraction. Traditionally, this process involves numerous complex and time-consuming steps, such as data normalization, unit harmonization, and depth matching, often requiring extensive expertise. This paper presents an AI-powered solution designed to automate the creation of wellbore interpretation workflows, significantly enhancing efficiency and accuracy. Our solution integrates generative AI, large language models (LLM), and graph-based methods to streamline the workflow creation process. The methodology involves three key steps: knowledge gathering from extensive documentation, automatic workflow generation through natural language interactions with a chatbot, and execution of quality-checked workflows. By leveraging a comprehensive knowledge base built from training manuals, API references, and historical interpretation data, our system can intelligently suggest optimal workflows in response to user queries. Tests conducted using data from the Groningen field demonstrate the effectiveness of this approach. The time required to create a lithology computation workflow was reduced from the typical 15-30 minutes to just 10 seconds using our AI-powered workflow advisor. This represents a time reduction factor of up to 180 for junior petrophysicists. The knowledge graph employed in this process showed a 93% accuracy in building workflows, ensuring reliability and precision. This innovative solution not only saves time but also minimizes errors, enabling non-experts to perform accurate data interpretation. The AI-powered workflow advisor represents a significant advancement in the automation of wellbore interpretation tasks, demonstrating the potential of machine learning to revolutionize the petroleum industry. By automating tedious and complex processes, our solution contributes to faster, more reliable wellbore analysis, ultimately improving decision-making and operational efficiency.

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Publication Details

Published
2024-11-04
DOI
https://doi.org/10.2118/222164-ms
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
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article

Accelerating Wellbore Domain Interpretations Using Generative AI and Knowledge Graphs

Karim Rekik, Zengpeng Zhou, Olfa Zened, Sohaib Ouzineb et al.
Reservoir Engineering and Simulation Methods
article

Accelerating Wellbore Domain Interpretations Using Generative AI and Knowledge Graphs

Karim Rekik, Zengpeng Zhou, Olfa Zened, Sohaib Ouzineb, Myriam Amour
article en

Abstract

Abstract The petroleum industry relies heavily on accurate wellbore domain interpretation for effective resource management and extraction. Traditionally, this process involves numerous complex and time-consuming steps, such as data normalization, unit harmonization, and depth matching, often requiring extensive expertise. This paper presents an AI-powered solution designed to automate the creation of wellbore interpretation workflows, significantly enhancing efficiency and accuracy. Our solution integrates generative AI, large language models (LLM), and graph-based methods to streamline the workflow creation process. The methodology involves three key steps: knowledge gathering from extensive documentation, automatic workflow generation through natural language interactions with a chatbot, and execution of quality-checked workflows. By leveraging a comprehensive knowledge base built from training manuals, API references, and historical interpretation data, our system can intelligently suggest optimal workflows in response to user queries. Tests conducted using data from the Groningen field demonstrate the effectiveness of this approach. The time required to create a lithology computation workflow was reduced from the typical 15-30 minutes to just 10 seconds using our AI-powered workflow advisor. This represents a time reduction factor of up to 180 for junior petrophysicists. The knowledge graph employed in this process showed a 93% accuracy in building workflows, ensuring reliability and precision. This innovative solution not only saves time but also minimizes errors, enabling non-experts to perform accurate data interpretation. The AI-powered workflow advisor represents a significant advancement in the automation of wellbore interpretation tasks, demonstrating the potential of machine learning to revolutionize the petroleum industry. By automating tedious and complex processes, our solution contributes to faster, more reliable wellbore analysis, ultimately improving decision-making and operational efficiency.

Industry, innovation and infrastructure
Openalex Percentile: Top 74%
Reservoir Engineering and Simulation Methods
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Accelerating Wellbore Domain Interpretations Using Generative AI and Knowledge Graphs — Karim Rekik, Zengpeng Zhou, et al. · (2024) | TGRS Research Map | TGRS