中国P站

Abstract

Well test interpretation is fundamental for reservoir characterization, with contemporary methods leveraging advanced analytics and numerical simulations. Machine learning is increasingly adopted to enhance accuracy and efficiency, enabling automated and predictive workflows. Real-time analysis supports dynamic reservoir management. Specialized techniques are employed for complex formations and multi-phase flow. Transient pressure analysis remains a core methodology, augmented by advanced deconvolution for complex well data. Interference and extended tests aid in understanding connectivity and boundaries. Inverse modeling and uncertainty quantification are advancing the reliability of formation evaluation from well test data.