The generative AI-based forecasting framework, particularly through models like TimeGrad and Informer, can be extended to incorporate the coupling between fluid flow and geomechanical processes by integrating additional data sources and modeling techniques that capture the interactions between these domains. This can be achieved through the following approaches:
Multiphysics Modeling: By integrating multiphysics models that account for both fluid dynamics and geomechanical behavior, the framework can simulate how changes in fluid pressure and flow rates influence stress distributions in the reservoir. This can involve coupling the generative AI models with finite element or finite difference methods that solve the governing equations of fluid flow and geomechanics.
Data Fusion: Incorporating diverse datasets, such as seismic data, pressure measurements, and fluid production rates, can enhance the model's ability to capture the complex interactions between fluid flow and fault activation. Machine learning techniques can be employed to analyze these datasets and identify patterns that indicate potential seismic events related to fluid extraction or injection.
Probabilistic Framework: The probabilistic nature of generative AI models allows for the estimation of uncertainty in predictions. By modeling the probability density functions (PDFs) of both fluid production and geomechanical responses, the framework can provide insights into the likelihood of fault activation and induced seismicity under various operational scenarios.
Real-time Monitoring and Feedback: Implementing real-time monitoring systems that feed data back into the generative AI models can help refine predictions and adapt operations dynamically. This feedback loop can be crucial for managing risks associated with induced seismicity, allowing operators to adjust fluid injection or extraction rates based on the model's forecasts.
Scenario Analysis: The framework can be used to conduct scenario analyses that explore the impacts of different operational strategies on both fluid production and geomechanical stability. By simulating various conditions, operators can identify optimal strategies that minimize the risk of induced seismicity while maximizing production efficiency.