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ISSN:2157-7463

Journal of Petroleum & Environmental Biotechnology
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Research Article

Parametric Study of Enhanced Condensate Recovery of Gas Condensate Reservoirs using Design of Experiment

Nkemakolam Izuwa and Basil C Ogbunude*
Department of Petroleum Engineering, Federal University of Technology, Owerri, Peace Wokoma, University of Port Harcourt, Nigeria
Corresponding Author : Basil C Ogbunude
Department of Petroleum Engineering
Federal University of Technology, Owerri
Peace Wokoma, University of Port Harcourt, Nigeria
Tel: +2348068628037
E-mail: [email protected] yahoo.com
Received September 02, 2015; Accepted December 12, 2015; Published December 22, 2015
Citation: Izuwa N, Ogbunude BC (2015) Parametric Study of Enhanced Condensate Recovery of Gas Condensate Reservoirs using Design of Experiment. J Pet Environ Biotechnol 7:259. doi:10.4172/2157- 7463.1000259
Copyright: © 2015 Izuwa N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Gas condensate reservoirs usually exhibit reduced well productivity because of condensate dropout that occurs below the dew point pressure. Gas recycling has become one of the most favorable methods of improving recovery of condensed liquid. However, understanding the influence of different injection and reservoir parameters on productivity is of great importance when planning a gas recycling scheme. Traditional methods of sensitization during reservoir simulation for gas condensate fields creates the challenge of quick identification of the most critical properties for sensitization, and hence delay of overall simulation project delivery. This work aims at identifying the key variables that influence productivity of a gas condensate reservoir under a gas recycling scheme using the design of experiment approach (DOE). DOE represents a more effective method for computer-enhanced, systematic approach to experimentation, considering all the factors simultaneously. Identification of these parameters will help simulators achieve best optimization targets and also save time and resources during dynamic simulation projects. Furthermore, it will be shown that experimental design can be used to fit responses (condensate/gas production) to mathematical models that will be able to predict outputs for any given combination of variables.

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