Drilling Report Summarization Using AI Prompts and Sentiment Scoring
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Solution Overview
Problem
Existing drilling and operations reports in the hydrocarbon recovery industry are lengthy and time-consuming to review, often hiding critical information and requiring manual scouring, with no efficient system to provide intelligent summaries based on user queries.
Innovation Solution
An AI-based system using a Large Language Model (LLM) and Natural Language Processor (NLP) generates intelligent summaries of drilling and operations reports, allowing users to enter prompts for quick access to relevant information, trend analysis, and enabling efficient decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed drilling and operations reports are provided to ensure complete information, then information completeness is improved, but report reading time and operational efficiency deteriorate
Solution Approach 1:
The system segments detailed drilling and operations reports into structured data fields and generates multiple summary versions (executive summary, technical summary, safety summary) based on different user needs. This segmentation allows complete information to be organized in a way that enables quick access to relevant details without requiring users to read entire lengthy reports.
Solution Approach 2:
The AI system extracts key information, critical findings, and essential data points from detailed reports to create condensed summaries. This extraction process removes unnecessary details while preserving crucial information, allowing users to quickly grasp the most important aspects without reading the complete original reports.
2Loss of information
If comprehensive drilling reports are generated to capture all operational data, then data completeness is improved, but ease of operation and quick analysis deteriorate
Solution Approach 1:
The system dynamically generates different summary versions based on user roles, preferences, and specific query requirements. The same comprehensive data set can be summarized in multiple ways (executive, technical, safety-focused) depending on what the user needs, making the system adaptable to different operational contexts and user expertise levels.
Solution Approach 2:
The AI language model acts as an intermediary between the comprehensive raw data and the user. It processes the complete operational data and translates it into user-friendly summaries that maintain data completeness while improving ease of analysis and interpretation.
3Measurement precision
If manual review of lengthy reports is performed to ensure thorough analysis, then analysis thoroughness is improved, but productivity and decision-making speed deteriorate
Solution Approach 1:
The system provides structured summaries with key findings, recommendations, and critical data points that enable users to quickly understand the analysis results. This feedback mechanism maintains thoroughness by including all essential information while presenting it in a format that accelerates decision-making compared to manual review of raw reports.
Solution Approach 2:
The patent replaces the mechanical process of manual report review with an AI-based natural language processing system. This substitution maintains or improves analysis thoroughness through systematic data processing while dramatically increasing productivity by eliminating the time-consuming manual reading and interpretation process.
4Measurement precision
If detailed operational data is recorded to improve monitoring accuracy, then monitoring precision is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The system changes the parameter of data presentation by transforming detailed operational data into summarized formats with varying levels of detail. The underlying monitoring accuracy is maintained through complete data collection, but the processed output adapts the level of detail based on user needs, effectively managing data processing complexity while preserving monitoring precision.
Data Source
AI summary
Embodiments of a method, system and machine-readable medium for generating a summary of drilling and operation reports based on a user prompt are disclosed herein. In one embodiment, a method comprises receiving a user prompt that was parsed by a natural language processor; generating a first summary of a plurality of drilling reports for at least one wellbore based on a report repository created from the plurality of drilling reports from at least one wellbore; indicating a sentiment score of the first summary; and generating a final summary based on the first summary and the user prompt.