Applying TimeGrad and Informer models to forecasting production from unconventional reservoirs presents several limitations and challenges:
Data Scarcity and Quality: Unconventional reservoirs often have limited historical production data due to their relatively recent development. The quality of available data may also be inconsistent, leading to challenges in training robust models. The generative AI models require substantial amounts of high-quality data to learn effectively, and data gaps can hinder their performance.
Complex Flow Dynamics: The physics governing fluid flow in unconventional reservoirs, such as shale gas or tight oil formations, is often more complex than in conventional reservoirs. Factors like microfractures, varying permeability, and complex multiphase flow behavior can introduce significant nonlinearity and variability in the data, making it difficult for the models to capture these dynamics accurately.
Parameter Sensitivity: The performance of TimeGrad and Informer models can be sensitive to hyperparameters and model architecture choices. In unconventional reservoirs, where the underlying physics may not be well understood, selecting appropriate parameters can be challenging and may require extensive experimentation.
Multiscale Interactions: Unconventional reservoirs often exhibit multiscale interactions between geological features and fluid flow. The generative AI models may struggle to capture these interactions effectively, particularly if the training data does not encompass the full range of spatial and temporal scales involved.
Uncertainty Quantification: While the generative AI models provide probabilistic forecasts, quantifying uncertainty in unconventional reservoirs can be particularly challenging due to the complex interplay of geological, operational, and environmental factors. Accurately representing this uncertainty is crucial for informed decision-making but may require additional modeling efforts.
Integration with Existing Models: Integrating generative AI models with existing physics-based reservoir simulation models can be complex. Ensuring that the AI models complement rather than contradict established physical principles is essential for maintaining credibility and reliability in forecasts.