NLP Extraction from Daily Drilling Reports
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Solution Overview
Problem
Current methods for extracting information from daily drilling reports (DDRs) are labor-intensive, time-consuming, and often limited to a subset of data, resulting in underutilization of valuable operational insights.
Innovation Solution
The method involves preprocessing raw data from DDRs to remove ambiguity and formatting errors, followed by the use of natural language processing (NLP) algorithms to extract topics and measurement data, which are then aggregated to form discrete data points for presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual screening of daily drilling reports is performed, then information extraction accuracy is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical screening processes with an automated NLP-based system. The system uses named entity recognition, text classification, and information extraction algorithms to automatically identify and extract relevant information from daily drilling reports, eliminating the need for manual review while maintaining extraction accuracy through sophisticated computational models.
Solution Approach 2:
The system enables self-service information extraction where the NLP model autonomously processes drilling reports without human intervention. The automated system performs topic identification, entity extraction, and data aggregation independently, allowing the system to serve itself rather than requiring manual operational support for routine information extraction tasks.
2Loss of information
If comprehensive review of all daily drilling reports is performed, then information completeness is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent extracts only the most relevant information from comprehensive drilling reports using NLP techniques. The system identifies and extracts specific entities, topics, and measurements of interest while filtering out unnecessary content, thereby maintaining information completeness for critical elements while reducing overall processing volume and improving efficiency.
Solution Approach 2:
The system segments the information extraction process into distinct functional modules: topic classification, entity recognition, measurement extraction, and data aggregation. This segmentation allows parallel processing of different information types and enables the system to handle comprehensive reports efficiently by processing them through specialized sub-routines rather than monolithic processing.
3Productivity
If automated information extraction is implemented, then processing speed is improved, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional NLP system that performs multiple extraction tasks through unified models. The same system handles topic identification, entity recognition, measurement extraction, and data aggregation, reducing the need for separate specialized systems and thereby managing overall complexity while maintaining high processing speed through consolidated computational resources.
Data Source
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AI summary
A system and method are provided for extracting information regarding a drill site including forming one or more documents having one or more raw comments regarding a well site. Raw data may be extracted from the one or more documents to produce extracted raw data. The extracted raw date may be pre-processed by removing ambiguity, artifacts, and/or formatting errors from the one or more raw comments to produce pre-processed data. Topics data may be extracted from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data. Measurement data may also be extracted from the pre-processed data using the NLP algorithm to produce extracted measurement data. The extracted topics data and the extracted measurement data may be aggregated to form a set of discrete data points, such as calibration points, per comment to produce aggregated data and one more calibration points may be identified from the aggregated data. The results of the one or more calibration points may then be presented.