LLM Text Analysis for Oil and Gas Operations
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Solution Overview
Problem
Existing technologies face challenges in efficiently analyzing and extracting usable information from large volumes of unstructured textual data associated with oil and gas operations, requiring manual review and specialized programming skills.
Innovation Solution
The use of Large Language Models (LLMs) in conjunction with curated domain prompts allows for automated analysis of oil and gas textual data, reducing the need for manual effort and specialized programming, and enabling quick prototyping of ideas and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to analyze textual data, then analysis accuracy can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated LLM-based system that uses natural language processing to analyze textual data. The system substitutes human cognitive processing with AI models that can rapidly process and interpret unstructured text, thereby maintaining analysis accuracy while dramatically reducing time consumption and labor effort.
Solution Approach 2:
The system enables self-service analysis by allowing users to interact with the LLM through natural language queries without requiring specialized programming skills or manual data processing expertise. The automated system performs analysis independently, reducing dependency on manual intervention while maintaining analytical quality.
2Extent of automation
If rule-based solutions like fuzzy-string matching or regex matching are used, then automated text analysis can be achieved, but the rules require specialized programming skills and conditioning on specific datasets
Solution Approach 1:
The patent replaces complex rule-based systems (fuzzy-string matching, regex) with an LLM-based natural language processing system. This substitution eliminates the need for specialized programming skills and dataset conditioning, as the LLM inherently understands and processes natural language patterns without requiring manual rule configuration or programming expertise.
Solution Approach 2:
Instead of requiring complex, maintainable programming rules that need to be conditioned on specific datasets, the system uses pre-trained LLM models that can be quickly adapted to different domains through prompt engineering. This approach replaces expensive, complex programming infrastructure with more flexible, easier-to-deploy AI models that don't require specialized programming skills.
3Productivity
If existing NLP solutions are used, then some text processing can be achieved, but prototyping requires changes to code and cannot be quickly adjusted
Solution Approach 1:
The patent implements dynamic adaptability by using prompt engineering to quickly adjust the LLM's behavior for different analysis tasks. Instead of requiring code changes to prototype new functionality, users can dynamically modify prompts to adapt the system to different datasets and analysis requirements, enabling rapid prototyping and iteration without technical complexity.
Solution Approach 2:
The system uses pre-trained LLM models that come with built-in language understanding and processing capabilities. This preliminary training eliminates the need for developers to build or modify the core AI infrastructure, allowing them to quickly prototype applications by simply crafting appropriate prompts rather than programming complex processing logic.
4Quantity of substance
If large volumes of unstructured textual data are analyzed, then comprehensive information can be extracted, but the variety, volume, velocity, and veracity of data make processing difficult
Solution Approach 1:
The patent replaces complex mechanical data processing systems with an LLM-based natural language processing approach. The LLM's inherent ability to understand and process unstructured text allows it to handle large volumes of diverse data without requiring complex preprocessing, parsing, or data cleaning operations, thereby reducing processing complexity while maintaining comprehensive analysis capability.
Data Source
AI summary
The disclosure provides automated analysis of oil and gas textual data that uses one or more LLMs and designed prompts or prompt chains. The prompts, referred to a curated domain prompts, use oil and gas domain knowledge to simulate human thinking and analysis. The curated domain prompts are pre-configured such that users do not need to create prompts for analyzing the textual data. In one example, a method of automatically analyzing oil and gas textual data, includes: (1) obtaining a curated domain prompt that identifies an oil and gas operation event and a parameter associated with the oil and gas operation event, (2) automatically extracting, using a large language model (LLM), event data from oil and gas textual data based on the oil and gas operation event and the parameter, and (3) automatically generating an event summary that correlates the oil and gas operation event and the event data.