Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
The initialization of a reservoir simulator calls for the populating of a three-dimensional dynamic grid-cell model using subsurface data and interrelational algorithms that have been synthesized to be fit for purpose. These prerequisites are rarely fully satisfied in practice. This paper sets out to strengthen initialization through four key thrusts, all of which seek to optimize the bridgehead between reservoir geoscience and reservoir engineering, and thereby maximize value from reservoir simulation. The first addresses representative data acquisition, which includes the key-well concept as a framework for the cost-effective incorporation of free-fluid porosity and permeability within an initialization database. The second concerns the preparation of these data and their products for populating the static and dynamic models. Important elements are dynamically conditioned net-reservoir cut-offs, recognition of primary flow units, and establishing interpretative algorithms at the simulator grid-cell scale for application over net-reservoir zones. The third thrust is directed at the internal consistency of capillary character, relative permeability properties and petrophysically-derived hydrocarbon saturations over net reservoir. This exercise is central to the simulation function and it is an integral component of hydraulic data partitioning. The fourth concerns the handling of formation heterogeneity and anisotropy, especially from the standpoint of directional parametric averaging and interpretative algorithms. These matters have been synthesized into a workflow for optimizing the initialization of reservoir simulators. In so doing, a further important consideration is the selection of the appropriate procedures that are available within and specific to different software packages. It is the authors’ experience that implementation of these thrusts has demonstrably enhanced the authentication of reservoir simulators through more readily attainable history matches with less required tuning. This outcome is attributed to a more systematic initialization process with a lower risk of artefacts. Of course, these benefits feed through to more assured estimates of ultimate recovery and, thence, hydrocarbon reserves.
The initialization of a reservoir simulator calls for the populating of a three-dimensional dynamic grid-cell model using subsurface data and interrelational algorithms that have been synthesized to be fit for purpose. These prerequisites are rarely fully satisfied in practice. This paper sets out to strengthen initialization through four key thrusts, all of which seek to optimize the bridgehead between reservoir geoscience and reservoir engineering, and thereby maximize value from reservoir simulation. The first addresses representative data acquisition, which includes the key-well concept as a framework for the cost-effective incorporation of free-fluid porosity and permeability within an initialization database. The second concerns the preparation of these data and their products for populating the static and dynamic models. Important elements are dynamically conditioned net-reservoir cut-offs, recognition of primary flow units, and establishing interpretative algorithms at the simulator grid-cell scale for application over net-reservoir zones. The third thrust is directed at the internal consistency of capillary character, relative permeability properties and petrophysically-derived hydrocarbon saturations over net reservoir. This exercise is central to the simulation function and it is an integral component of hydraulic data partitioning. The fourth concerns the handling of formation heterogeneity and anisotropy, especially from the standpoint of directional parametric averaging and interpretative algorithms. These matters have been synthesized into a workflow for optimizing the initialization of reservoir simulators. In so doing, a further important consideration is the selection of the appropriate procedures that are available within and specific to different software packages. It is the authors’ experience that implementation of these thrusts has demonstrably enhanced the authentication of reservoir simulators through more readily attainable history matches with less required tuning. This outcome is attributed to a more systematic initialization process with a lower risk of artefacts. Of course, these benefits feed through to more assured estimates of ultimate recovery and, thence, hydrocarbon reserves.
Summary An examination of the core and log analysis of carbonate reservoirs has confirmed that identified shortcomings are rooted in disparate pore character. Many of the interpretation methods were developed for clastic rocks, which typically show an intergranular porosity, sometimes augmented by fracture porosity. In carbonate reservoirs, the primary pore system comprises interparticle porosity that coexists with a highly variable secondary system of dissolution voids and/or fractures. As a consequence, carbonate reservoirs are markedly heterogeneous from pore to reservoir scales, and this variability poses significant challenges to data acquisition, petrophysical evaluation, and reservoir description. For example, the ranges of carbonate facies and their pore character often control the distributions of net pay, porosity, and hydrocarbon saturation. Putting these matters together, conventional petrophysical practices that exclusively use reservoir zonation based on lithology/mineralogy have limited application in carbonates. Instead, recourse is made to a zonal discrimination that draws upon the distribution of microporosity and its connectivity with macroporosity and fractures. The discrimination scheme uses downhole technologies such as high-resolution imaging and magnetic resonance logs, supported by advanced core analysis. On this basis, a value-adding workflow is proposed to increase confidence in those petrophysical deliverables that are used in static volumetric estimates of petroleum Resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.