Generative ML for Petrophysics Interpretation
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Solution Overview
Problem
Petrophysics interpretation is challenging due to the complexity and heterogeneity of subsurface geological formations, requiring significant expertise and being time-consuming, prone to errors, and subject to variability in traditional manual processes.
Innovation Solution
Generative machine learning models, such as GANs, VAEs, and FBGMs, are trained on large datasets of petrophysical data to automate the interpretation of well logs, seismic data, and core samples, learning patterns and relationships to predict reservoir properties like porosity, permeability, and fluid saturation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual petrophysics interpretation methods are used, then expertise and experience can be applied to analyze complex subsurface data, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with automated machine learning systems. Neural networks and algorithms process well log data, seismic data, and core sample information automatically, eliminating the need for manual analysis while maintaining or improving accuracy through consistent application of learned patterns from training data.
Solution Approach 2:
The machine learning models are trained on comprehensive datasets to independently perform interpretation tasks without continuous human intervention. The system self-learns from training data and can autonomously generate petrophysical interpretations, reducing dependency on manual expertise while maintaining reliability through the learned knowledge base.
2Measurement precision
If traditional manual interpretation processes are used, then detailed analysis can be performed on heterogeneous geological formations, but the process is prone to variability and errors
Solution Approach 1:
Manual interpretation operations are replaced with automated computational systems that apply consistent algorithms to all data. The machine learning models process heterogeneous geological data uniformly, eliminating variability introduced by different interpreters while maintaining precision through sophisticated pattern recognition capabilities.
Solution Approach 2:
The system transforms qualitative expert judgment into quantitative computational parameters. By converting manual interpretation criteria into algorithmic parameters and processing thresholds, the system achieves consistent application across all interpretations while maintaining the ability to handle complex heterogeneous formations through adjusted parameter settings.
3Reliability
If comprehensive petrophysical analysis is performed on complex subsurface formations, then accurate reservoir characterization can be achieved, but the cost and time requirements increase significantly
Solution Approach 1:
The machine learning models are pre-trained on extensive datasets of well logs, seismic data, and core samples before deployment. This preliminary training phase enables the models to rapidly process new data without requiring time-consuming manual analysis, achieving both high accuracy in reservoir characterization and improved productivity through efficient automated processing.
Solution Approach 2:
The patent replaces slow manual analysis processes with high-speed computational systems. Machine learning algorithms process large volumes of petrophysical data rapidly, maintaining comprehensive analysis capabilities while dramatically improving productivity through automated computation and pattern recognition.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for analyzing and interpreting geophysical and petrophysical data and in particular, for using generative machine learning models to characterize and predict reservoir properties. A process of the disclosed technology can include steps for providing a set of formation measurement data to a generative machine learning model and generating, via the generative machine learning model, a set of latent space data corresponding to the set of formation measurement data. The process can further include steps for clustering the set of latent space data to generate a set of clusters and determining a petrophysical interpretation based on the clusters. Systems and machine-readable media are also provided.