Automated Well Log Data Quality Control System
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Solution Overview
Problem
The manual validation of well log data quality is tedious and time-consuming, especially when dealing with large files in diverse formats like DLIS and LAS, which requires multiple iterations between data providers and receivers, and is critical for compliance with standard operating procedures in the oil and gas industry.
Innovation Solution
An automated system using a computer processor for well log data quality control, which includes data manipulation, statistical analysis, and classification to determine a quality score, granting access to validated data and generating reports for unsatisfactory data, employing a checkpoint module, data management module, and machine learning for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual validation of well log data is performed by human experts, then data quality can be assessed with high precision, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
An automated validation system acts as an intermediary between human experts and well log data. The system performs preliminary validation of hundreds or thousands of data channels, generating reports that human experts can then review. This intermediary system handles the time-consuming routine work while maintaining validation precision through systematic automated checks.
Solution Approach 2:
The validation system enables data to be self-validated through automated processes. The system independently checks data quality, generates validation reports, and identifies issues without requiring manual inspection of each data channel. This self-service approach dramatically reduces the time human experts need to spend on validation while maintaining thoroughness.
2Productivity
If automated validation systems are implemented to reduce manual effort, then processing speed and productivity increase, but the complexity of the system increases
Solution Approach 1:
The validation system is divided into modular components that handle different aspects of data validation independently. Each module can validate specific data channels or parameters, making the overall complex validation process manageable through segmentation. This modular approach allows the system to handle high throughput while keeping individual components relatively simple.
Solution Approach 2:
The automated validation system is designed to handle multiple data formats (DLIS, LAS, PDF, CGM, Excel) and various data types through a single unified platform. This multi-functional capability increases productivity by eliminating the need for separate validation processes for different data types while the standardized approach actually reduces overall system complexity compared to having multiple specialized tools.
3Reliability
If multiple iterations of communication between data providers and receivers are performed to achieve valid data, then data quality standards are met, but the process becomes tedious and time-consuming
Solution Approach 1:
The automated validation system performs preliminary validation checks before data is formally submitted or reviewed by human experts. By catching and flagging issues in advance through systematic automated scanning of all data channels, the system prevents the need for multiple communication iterations. Data providers can address identified issues in a single pass rather than engaging in repeated back-and-forth communications.
Solution Approach 2:
The system provides immediate, automated feedback on data quality issues through detailed validation reports. Instead of waiting for manual review and then receiving feedback through multiple communication cycles, the automated system instantly identifies and reports all validation failures, allowing data providers to correct issues efficiently in a single iteration rather than through tedious repeated communications.
Data Source
AI summary
A method and a system for well log data quality control is disclosed. The method includes obtaining a well log data regarding a geological region of interest, verifying an integrity and a quality of the well log data, determining the quality of the well log data based on a quality score of the well log data and making a determination regarding the access to the databases based on the quality of data. Additionally, the method includes performing the statistical analysis and the classification of well log data, a predictive and a prescriptive analysis of trends and predictions of the well log data, and generating an action plan for datasets with unsatisfactory quality scores.