Machine Learning History-Match Quality Assessment for Reservoirs
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Solution Overview
Problem
Traditional geological history matching methods lack objective characterization of reservoir performance, relying on subjective visual inspection and error metrics like RMSE, which are prone to errors and inconsistencies, especially in large data volumes.
Innovation Solution
Implement machine learning-driven history-match quality assessment using trained models to integrate multi-parameter comparisons, learning from labeled Good and Acceptable matches to provide objective reservoir characterization and optimize well planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional graphical comparison methods are used for history matching, then the process is simple and easy to operate, but the accuracy and objectivity of reservoir characterization deteriorates due to subjective visual inspection
Solution Approach 1:
The patent replaces the manual visual inspection process with an automated machine learning classification system. The ML model automatically compares simulated data against probe data and classifies matches as Good, Acceptable, or Poor, eliminating subjective human judgment while maintaining systematic evaluation through computational algorithms
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the simulated data and the assessment decision. This intermediary system processes the complex multi-parameter comparisons and outputs objective classifications, serving as a mediator that transforms raw data into standardized quality assessments without direct human intervention
2Reliability
If simple error metrics like RMSE are used, then the calculation is straightforward, but the reliability of assessment deteriorates due to errors and inconsistencies in large data volumes
Solution Approach 1:
The patent changes the assessment parameters from simple scalar metrics like RMSE to multi-parameter comparisons including data density distribution, match classification categories (Good, Acceptable, Poor), and parameter-specific statistical characteristics. This transformation provides more reliable assessment by capturing the complexity of reservoir behavior across multiple dimensions
Solution Approach 2:
The patent segments the assessment process into distinct components: data density distribution analysis, parameter-specific match evaluation, and classification decision-making. By dividing the complex assessment into manageable segments, the system improves reliability through systematic evaluation of multiple parameters rather than relying on a single error metric
3Measurement precision
If multi-parameter comparisons are integrated using machine learning, then the objectivity and accuracy of assessment improves, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service automation where the machine learning model autonomously performs the entire history match quality assessment process. The system automatically compares simulated and probe data, determines data density distributions, classifies matches, and generates well planning recommendations without requiring manual intervention at any stage of the assessment
4Productivity
If traditional visual inspection methods are used, then the operation is simple, but the productivity of well planning deteriorates due to time-consuming manual analysis
Solution Approach 1:
The patent replaces manual visual inspection with automated machine learning classification that rapidly processes large volumes of data and provides objective assessments. This substitution dramatically increases productivity by eliminating time-consuming manual analysis while maintaining systematic evaluation through computational algorithms
Solution Approach 2:
The patent enables continuous automated assessment where the machine learning model continuously compares simulated data against probe data and updates well planning recommendations. This continuous operation eliminates interruptions in the analysis process and maintains productive workflow without the periodic manual intervention required by traditional methods
Data Source
AI summary
Methods, systems, and computer-readable storage media for machine learning driven history-match quality assessment. Probe data collected by probes included in operating wells or observation wells within a field is received. A list of training dataset including examples of Good and Acceptable matches is received. Data density distribution a matching the probe data to simulated data for respective parameters is learned. Training parameters for subsequent history-match assessment from the labeled input for the respective parameters can be retrieved. A well history-match quality is determined within the field using the parameter match assessment to provide well planning within the field.