🚀 Accelerating History Matching with AI & ES-MDA 🎯🔬
4D Seismic monitoring provides crucial insights for reservoir management, but its high dimensionality and processing complexity significantly impact the efficiency of inversion problems like history matching with Ensemble Smoother with Multiple Data Assimilation (ES-MDA).
🔍 The Challenge:
4D seismic data requires intensive processing, which can degrade assimilation performance and slow down model updates.
💡 Our Solution:
By leveraging AI-driven feature extraction and NVIDIA CUDA, we accelerate seismic data assimilation, making ES-MDA 90% faster while maintaining high accuracy in reservoir model updates.
🔹 Key Benefits:
✅ Faster History Matching – AI reduces the dimensionality of seismic data while preserving key geological features.
✅ Enhanced Model Performance – More efficient updates lead to improved reservoir forecasting.
✅ Seamless Integration – The solution is available for Petrel software, ensuring practical adoption in industry workflows.
This project was developed through our partnership with Petrobras CENPES, combining expertise in data assimilation, deep learning, and geosciences.
📖 Read more in our recent publications:
📌 Feature Extraction in Time-Lapse Seismic Using Deep Learning for Data Assimilation
🔗 https://lnkd.in/dwPJyCTY
📌 Deep Feature Extraction for Data Assimilation with Ensemble Smoother
🔗 https://lnkd.in/duxCr8Qg
#AI #SeismicData #HistoryMatching #DataAssimilation #ReservoirEngineering #MachineLearning #EnergyInnovation #OilAndGas #DeepLearning