Well Log Repeatability Verification System
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Solution Overview
Problem
Manual verification of well log repeatability in oil and gas fields is time-consuming and inefficient, requiring human experts to validate and visualize large datasets, leading to lengthy data quality verification processes.
Innovation Solution
A data gathering and analysis system that generates and analyzes well log data files using a computer processor to determine repeatability measures, presenting them via a graphical user interface, facilitating field operations based on user input, and automating the verification process through a system comprising a buffer, verification configuration engine, repeatability check engine, and verification display engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of well log repeatability is performed by human experts, then data quality validation can be thoroughly conducted, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical verification processes with automated computational systems. A computer processor automatically performs repeatability verification by comparing main logs with repeat logs, calculating correlation coefficients and statistical measures. This substitution of human expert analysis with algorithmic processing dramatically reduces verification time while maintaining or improving measurement precision through consistent, error-free computational analysis.
Solution Approach 2:
The system enables self-service verification where the well log data automatically validates itself through programmed algorithms. The computer processor independently compares datasets, computes repeatability metrics, and generates verification results without requiring continuous human intervention. This self-service approach allows the system to handle multiple verification tasks simultaneously, reducing overall process time while ensuring thorough data quality validation.
2Reliability
If human experts manually analyze and visualize large well log datasets, then comprehensive validation can be achieved, but the complexity and time requirement increase significantly
Solution Approach 1:
The verification system is segmented into distinct functional modules: data loading, comparison algorithms, statistical calculation, and result generation. Each module handles a specific aspect of the verification process, making the overall complex task manageable and systematic. The computer processor executes these segmented functions in sequence, ensuring comprehensive validation while organizing complexity into structured, reusable components that can be independently optimized.
Solution Approach 2:
The system creates digital copies of well log data in standardized formats for computational processing. By converting physical or semi-structured log data into digital representations that can be algorithmically manipulated, the system enables automated analysis without requiring physical handling or manual visualization. These digital copies facilitate rapid comparison and statistical analysis, improving reliability while managing complexity through software-based operations.
3Manufacturing precision
If repeated well logging is performed to verify repeatability, then data quality can be ensured, but the field operation time and resource consumption increase
Solution Approach 1:
The system performs preliminary automated processing of repeat logs immediately after acquisition, comparing them with main logs before further field operations. By pre-calculating repeatability metrics and identifying potential issues early, the system ensures log data consistency is verified upfront, preventing wasted time on subsequent operations with questionable data. This preliminary action maintains manufacturing precision while protecting field operation efficiency by catching problems before they propagate.
Solution Approach 2:
The verification system transforms raw well log data into standardized parameters and statistical measures that facilitate automated comparison. By converting diverse log formats into uniform parameters (correlation coefficients, statistical deviations, quality metrics), the system enables efficient algorithmic analysis of repeatability. This parameter transformation maintains data consistency verification while improving productivity by enabling rapid computational processing compared to manual analysis methods.
Data Source
AI summary
A method to perform a field operation with well log repeatability verification is disclosed. The method includes generating, by repeatedly performing well logging of a wellbore penetrating a subterranean formation in a field, a set of well log data files each comprising a plurality of data channels, each data channel comprising a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, a main log and a repeat log of the set of well log data files to determine a repeatability measure of the set of well log data files, presenting, using a graphical user interface, the repeatability measure to a user, and facilitating, based on a user input in response to presenting the repeatability measure, the field operation.