ML Well Test Validation Automates Data Analysis
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Solution Overview
Problem
The manual validation of well test data in the oil and gas sector is cumbersome and prone to human errors due to the complexity and volume of data, leading to inaccuracies and inefficiencies.
Innovation Solution
A method using machine-learning (ML) models trained on historical well test data to automate the validation of new well test data, incorporating natural language processing (NLP) to analyze textual comments and operational reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual validation process is used for well test data, then human expertise and careful analysis can be applied, but the process becomes cumbersome and susceptible to human errors leading to inaccuracies
Solution Approach 1:
The patent replaces the manual mechanical validation process with an automated machine learning-based system. The ML model automatically processes well test data, identifies patterns, detects outliers, and validates data quality without human intervention, thereby eliminating human errors while maintaining validation accuracy and significantly improving processing efficiency.
Solution Approach 2:
The validation system performs self-service by automatically validating well test data against predefined criteria and historical patterns. The ML model independently makes validation decisions, flagging acceptable or rejected data points without requiring continuous human oversight, thus improving both accuracy and efficiency simultaneously.
2Productivity
If automated validation tools and algorithms are used, then manual intervention is reduced and validation efficiency is improved, but computational demands become substantial due to intricate nature of problems and variables
Solution Approach 1:
The patent optimizes computational resource consumption by dynamically adjusting model parameters and validation thresholds based on the complexity of the well test data. The system adapts its computational intensity to match the actual requirements of each validation task, reducing unnecessary processing power consumption while maintaining high validation efficiency for routine cases.
Solution Approach 2:
The validation system applies partial automation by selectively applying complex computational algorithms only to critical validation steps and data points that require advanced analysis. Routine validation tasks use simpler, less computationally intensive methods, thereby reducing overall computational resource consumption while maintaining validation efficiency where needed.
3Difficulty of detecting and measuring
If complex computational models are used for validation, then data analysis capability is enhanced, but the models struggle to account for uncertainties and errors associated with production activities
Solution Approach 1:
The patent implements feedback mechanisms where the ML model continuously learns from validation results and adjusts its predictions. Historical validation data and expert feedback are fed back into the model to refine its understanding of uncertainties and errors in production activities, thereby improving both data analysis capability and reliability over time.
Solution Approach 2:
The validation system prepares for uncertainties by incorporating predefined tolerance ranges and error margins into the validation criteria. The ML model is trained to anticipate and accommodate common production-related uncertainties, cushioning against their impact on validation results before they occur, thereby maintaining reliability despite data complexities.
Data Source
AI summary
A method for validating a well test includes receiving historical well test data. The historical well test data includes one or more accepted flags and one or more rejected flags. The method also includes training a machine-learning (ML) model based upon the historical well test data to produce a trained ML model. The method also includes receiving new well test data. The new well test data does not include the one or more accepted flags and the one or more rejected flags. The method also includes determining whether the new well test data meets or exceeds a predetermined validation threshold using the trained ML model.