New Perspectives on Vogel Type <i>IPR</i> Models for Gas Condensate and Solution-Gas Drive Systems
Новые перспективы моделей IPR типа Vogel для газоконденсатных и систем с приводом растворённого газа
2003-03-23
SCID: 54.1/6gn2chuu
Discuss with AI
Vogel-type IPRalternating conditional expectationgas condensateinflow performance relationssolution-gas drive
Figures from the paper
Abstract (AI)
Abstract In this work we propose two new Vogel-type Inflow performance Relations (or IPR ) correlations for gas condensate reservoir systems. One correlation predicts gas production the other predicts condensate production. These correlations link reservoir rock and fluid properties (dewpoint, temperature, and endpoint relative permeabilities) to the flowrate-pressure per-formance for the system. The proposed IPR relationships for compositional reservoir systems are based on data from over 3000 compositional re-servoir simulation runs with various fluid properties and rela-tive permeability curves. The resulting IPR curves for gas condensate systems are quadratic in nature like the Vogel IPR trends (the Vogel profile generally presumed for the case of a solution gas-drive reservoir system). However in the gas con-densate case the coefficients in the quadratic relationship vary significantly depending on the richness of the condensate and the relative permeability. A model to predict these coeffi-cients was developed using an alternating conditional expecta-tion approach (optimal non-parametric regression). This work also includes a discussion of the Vogel IPR for solution-gas drive systems. The original work proposed by Vogel is based on an empirical correlation of numerical simulations for a solution-gas-drive system. Our work provides a critical validation and extension of the Vogel work by esta-blishing a rigorous, yet simple formulation for flowrate-pres-sure performance in terms of effective permeabilities and pres-sure-dependent fluid properties. The direct application of this work is to predict the IPR for a given system directly from rock-fluid properties and fluid pro-perties. This formulation provides a new mechanism that can be used to couple flowrate and pressure behavior for solution-gas-drive systems and it may be possible to extend the concept to gas condensate reservoir systems.
Key Findings
1
An alternating conditional expectation (optimal non-parametric regression) model was developed to predict the quadratic coefficients from rock–fluid parameters.
2
Resulting IPR curves for gas condensate systems are quadratic like Vogel trends, but quadratic coefficients vary substantially with condensate richness and relative permeability.
3
The correlations link reservoir rock and fluid properties (dewpoint, temperature, endpoint relative permeabilities) to flowrate–pressure performance.
4
The formulation enables direct prediction of IPR from rock–fluid properties and offers a mechanism to couple flowrate and pressure behavior, potentially extendable to gas condensate systems.
5
The proposed IPRs were derived from over 3000 compositional reservoir simulation runs covering varied fluid properties and relative permeability curves.
6
The work validates and extends Vogel’s solution-gas-drive IPR by providing a rigorous, simple formulation using effective permeabilities and pressure-dependent fluid properties.
7
Two new Vogel-type IPR correlations were developed specifically for gas condensate reservoirs: one predicting gas production and one predicting condensate production.
Research Object
Vogel-type inflow performance relationships (IPR) for gas condensate and solution-gas-drive reservoir systems
Research Subject
Predicting flowrate–pressure performance and condensate/gas production by linking reservoir rock and fluid properties (dewpoint, temperature, endpoint/effective relative permeabilities) to coefficients of quadratic Vogel-type IPRs, including coefficient prediction via alternating conditional expectation (non-parametric regression)
Publication Details
Publication Date
2003-03-23
Journal
Publisher
ISSN
Access Type
Author Information
Download PDF
Subscribe to digest