Field Equipment Data Quality Scoring With Machine Learning
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and analyzing field equipment data for reservoir operations, particularly in managing uncertainties and optimizing operations in complex geologic environments.
Innovation Solution
A system and method utilizing a trained machine learning model to automatically process field equipment data and generate quality scores, integrated with a graphical user interface and various computational frameworks, enabling real-time data analysis and operational decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used for field equipment data, then processing simplicity is maintained, but data processing efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with machine learning models that automatically analyze field equipment data. The ML models substitute manual or rule-based processing systems, enabling more efficient and accurate data analysis without requiring complex human intervention or manual processing workflows.
Solution Approach 2:
The system enables self-service data processing through automated machine learning models that independently evaluate field equipment data quality and generate insights without requiring extensive human oversight. The ML models autonomously process data, identify patterns, and provide quality assessments, reducing the need for manual processing steps.
2Measurement precision
If manual data processing methods are used, then system complexity is reduced, but measurement precision and analysis accuracy deteriorate
Solution Approach 1:
The patent replaces manual data quality assessment methods with machine learning models that automatically evaluate data precision and accuracy. The ML models substitute human analysts or simple rule-based systems, providing more accurate measurement precision through automated pattern recognition and quality scoring mechanisms.
3Productivity
If automated machine learning processing is implemented, then data processing efficiency is improved, but processing time for model training and deployment increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with historical field equipment data before actual deployment. This allows the models to be ready for immediate use when new data needs processing, reducing the time loss during operational phases. The models are prepared in advance through training sessions using available data sets.
4Reliability
If comprehensive data analysis is performed to reduce uncertainties, then decision-making accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most critical features and patterns from field equipment data using machine learning models, rather than analyzing every detail comprehensively. This selective extraction approach maintains high decision-making reliability by focusing on the most important data elements while reducing overall processing time and computational resource requirements.
Data Source
AI summary
A method can include receiving a request for field equipment data; responsive to the request, automatically processing the field equipment data using a trained machine learning model to generate a quality score for the field equipment data; and outputting the quality score.