integrationAI: Bridging Seismic Data, Inversions, Well Logs, and Reservoir Production
integrationAI, a core package within subsurfaceAI, is designed to seamlessly integrate diverse geoscience data sources—including multiple seismic attributes, inversion outputs, well logs, and reservoir production metrics. By leveraging advanced machine learning (ML) algorithms such as neural networks, XGBoost, and Random Forest, integrationAI generates high-resolution property maps and 3D property grids for target reservoir intervals. This data-driven approach enhances subsurface characterization, supporting informed decision-making in reservoir development.
Enhancing ML Model Trust with Explainable Tools
Applying machine learning to subsurface data presents a significant challenge: ensuring that model results are both accurate and geologically interpretable. integrationAI tackles this challenge by offering a comprehensive suite of explainable ML tools that enhance quality control and performance analysis. These tools enable geoscientists to not only assess model performance but also understand the key factors influencing predictions.
Key Explainable ML Tools in integrationAI
- Training and Validation Accuracy Curves
- Visualize the model’s learning progress over training epochs to assess convergence and detect overfitting.
- Aid in selecting the optimal training duration and verifying generalization to unseen data.
- Partial Dependence (PD) Curves
- Illustrate the marginal effect of one or more seismic attributes on the predicted property while averaging out the influence of other variables.
- Identify the seismic features that most significantly impact model predictions.
- Individual Conditional Expectation (ICE) Curves
- Provide a more granular view of prediction variations for individual data instances.
- Show how changes in a specific attribute affect predictions across different wells or reservoir regions.
- Feature Importance Plots
- Rank seismic attributes and input features based on their influence on model output.
- Help prioritize data acquisition and further analysis on the most impactful attributes.
- Shapley Plots
- Use cooperative game theory (Shapley values) to quantify the contribution of each attribute to individual predictions. The Shapley values are used to calculate Feature Importance Curves,
- Provide a statistically sound interpretation of feature importance at both global and local levels. Dependence Plot, and the “Beeswarm Summary Plot”.
- Cross Plots Between Validation, Testing, and Prediction Results
- Retain a percentage of samples as “ground truth” for validating ML predictions.
- Compare predicted vs. actual values and calculated R-Squared, a key metric indicating the proportion of variance explained by the model.
Quality Control, Performance Analysis, and Uncertainty Quantification
By integrating these explainable ML tools, integrationAI offers a transparent framework for evaluating the reliability and geological consistency of ML predictions. Users can:
- Evaluate Feature Relevance
- Determine which seismic attributes are most critical for predicting reservoir properties.
- Ensure that model outputs align with established geological understanding.
- Monitor Model Robustness
- Compare training and validation performance to detect overfitting and assess generalization to new data.
- Assess Prediction Uncertainty
- Use multiple seed numbers in training-validation splits to generate a range of prediction outcomes.
- Employ an ensemble approach to derive uncertainty measurements (P10, P50, P90), offering probabilistic insight into prediction reliability.
Conclusion
integrationAI is more than just a data integration tool—it provides a robust framework for ML-driven reservoir characterization. With its powerful combination of performance curves, sensitivity analyses (PD and ICE), and feature importance assessments (Importance and Shapley Plots), geoscientists can confidently interpret ML results in the context of local geology. Furthermore, its uncertainty quantification capabilities enhance decision-making in reservoir management and development strategies.
This technical solution enhances transparency and trust in ML applications, making integrationAI an invaluable asset in subsurface exploration and production workflows.
