Reservoir Characterization Overview

Principles

Definition

Reservoir characterization is an interdisciplinary methodological approach within geosciences and petroleum engineering focused on comprehensively describing the static and dynamic properties of subsurface hydrocarbon reservoirs. It involves integrating diverse geological, geophysical, petrophysical, and engineering data to construct models essential for evaluating resource potential, forecasting production performance, and optimizing recovery strategies.

Ontological type

Core Objectives

Data Sources

Modeling Techniques

Key developments

Key Figures

Fracture and Layer Diagnostics era

Muhammad Sahimi [1] was a leading figure in the Fracture and Layer Diagnostics era, with affiliations at the University of Minnesota [3] and the University of Southern California [4]. His 1993 paper Flow phenomena in rocks: from continuum models to fractals, percolation, cellular automata, and simulated annealing [5] advanced the conceptual framework for fracture connectivity by linking continuum descriptions to fractal and network-based flow, a key driver of era-wide diagnostic development. K.H. Coats [2] contributed to this era through the 1980 paper An Equation of State Compositional Model [6], which provided a foundational compositional framework for reservoir simulations. K.H. Coats [2]'s work on equation-of-state modeling helped underpin upscaling and cross-porosity diagnostics in fractured-layer systems during this period.

Stochastic Inversion and Ensembles era

Clayton V. Deutsch [1] is a leading figure at Stanford University [3] and the University of Alberta [4] during the Stochastic Inversion and Ensembles era. His Geostatistical Reservoir Modeling [6] and High Resolution Reservoir Models Integrating Multiple-Well Production Data [7] advanced geostatistical conditioning and integration of multiple-well production data, enabling better representation of heterogeneity and fracture networks in uncertainty-aware reservoir characterization. Martin J. Blunt [2] is affiliated with Stanford University [3] and Imperial College London [5] during this era, and his work on Predictive pore-scale modeling of two-phase flow in mixed wet media [8] advanced understanding of micro-scale flow that underpins ensemble-based upscaling. By linking pore-scale physics to reservoir-scale forecasts, this work supported the probabilistic, uncertainty-aware framework that defined stochastic inversion and ensemble data assimilation in the era.

Physics-Informed Data Integration era

Stefan Iglauer [1] is associated with California Institute of Technology [3] and Imperial College London [4] in this era. His key contribution centers on Pore-scale imaging and modelling [6], which bridged pore-scale physics to field-scale understanding and helped enable physics-informed data integration in reservoir characterization. Martin J. Blunt [2] is associated with Imperial College London [4] and Cardiff University [5] in this era. His work on Pore-scale imaging and modelling [6] underpins multiscale upscaling and uncertainty-aware predictions, illustrating why connecting microscopic structure to macroscopic behavior was crucial for reservoir characterization in this era.