Artificial-Intelligence-Assisted Pressure Transient Analysis in Unconventional Hydrocarbon Reservoirs: Progress, Challenges, and Perspectives

Abstract

Unconventional hydrocarbon reservoirs have become increasingly important for sustaining the energy supply, but their efficient development remains constrained by complex fracture networks, multiscale flow behavior, strong heterogeneity, and pronounced nonuniqueness in pressure transient analysis (PTA). Conventional workflows rely heavily on predefined physical models, type curve matching, and interpreter experience, which limits their robustness and field applicability under complex reservoir conditions. Recent advances in artificial intelligence (AI), data analytics, and high-performance computing provide new opportunities for automated model identification, parameter inversion, uncertainty evaluation, and engineering decision support. This review summarizes the progress of AI-assisted pressure transient analysis in unconventional hydrocarbon reservoirs. Analytical, semianalytical, and numerical pressure-transient models are first revisited as the physical foundation for AI-assisted PTA and physics-constrained learning. Representative methods, including expert systems, nonlinear regression, artificial neural networks, deep-learning-based methods, and physics-informed approaches, are then critically compared in terms of technical characteristics, physical consistency, interpretability, generalization capability, and engineering applicability. Key challenges are identified, including limited high-quality field data sets, insufficient data standardization and sharing, nonunique model identification, parameter nonidentifiability, limited physical consistency and geological plausibility of purely data-driven inversion, insufficient uncertainty quantification and statistical validation, limited synthetic-to-field transferability, and the lack of closed-loop workflows linking data, models, algorithms, uncertainty evaluation, and development decisions. Future research should move beyond isolated algorithmic improvements toward physics-guided, interpretable, uncertainty-aware, and workflow-oriented frameworks that integrate pressure transient model libraries, multisource field data, field validation, and reservoir engineering decision support. This review provides a structured basis for advancing artificial-intelligence-assisted pressure transient analysis from methodological exploration to practical deployment in unconventional hydrocarbon reservoir development.