Artificial intelligence enhances well test interpretation by automating noise reduction, stabilizing pressure-derivative calculations, and recognizing flow regimes more reliably than manual analysis. By combining machine learning with physics-informed models, AI accelerates parameter inversion and improves the estimation of key reservoir properties such as permeability, skin, storativity, and boundary effects. Unlike purely traditional methods, AI can handle complex, noisy, or incomplete datasets while providing uncertainty quantification. This results in faster, more accurate, and more robust reservoir characterization, ultimately supporting better decision-making in reservoir management.

Mohsen ali Al Mabrouk , Artificial Intelligence (AI) can automatically identify flow regulations and improve the accuracy of well testing interpretation, distinguishing between wellbore storage effects, radial flows, and boundary effects with high precision compared to traditional curve matching. Trained machine learning models on historical well testing data can detect micro-patterns in pressure and flow reactions that can be overlooked by manual analysis. An AI-operated adaptation algorithm can reduce reservoir parameters such as permeability, skin factor and reservoir boundaries in assessing the reservoirs. By integrating seismic, geological and production data, the AI ​​provides a more overall interpretation of the reservoir behavior, leading to more reliable property estimates. Deep learning techniques can handle noise or incomplete well testing data, improving strength in challenging field conditions. Overall, AI reduces human bias, accelerates analysis, and increases confidence in the reservoir characterization for better decision-making in the development of the oil field.

Dear Mohsen ali Al Mabrouk ,

Over the past decade, the topic that has attracted considerable attention is modeling fluid dynamics in gas condensate systems and/or near critical reservoirs. A highly relevant and widely used feature in the modeling stage of such intricate systems is relative permeability. The ratio between the fluid's effective permeability at a given saturation level and its absolute permeability when fully saturated is referred to as relative permeability.

Article Artificial Intelligence for Reservoir Modeling and Property ...

Regards,

Shafagat