Inflow Performance Relationship Correlation for Solution Gas-Drive Reservoirs Using Non-Parametric Regression Technique
Ahmed M. Daoud1, *, Mahmoud Abdel Salam2, Abdel Alim Hashem1, M.H. Sayyouh1
Identifiers and Pagination:Year: 2017
First Page: 152
Last Page: 176
Publisher Id: TOPEJ-10-152
Article History:Received Date: 28/03/2017
Revision Received Date: 08/05/2017
Acceptance Date: 13/06/2017
Electronic publication date: 12/08/2017
Collection year: 2017
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The Inflow Performance Relationship (IPR) describes the behavior of flow rate with flowing pressure, which is an important tool in understanding the well productivity. Different correlations to model this behavior can be classified into empirically-derived and analytically-derived correlations. The empirically-derived are those derived from field or simulation data. The analytically-derived are those derived from basic principle of mass balance that describes multiphase flow within the reservoir. The empirical correlations suffer from the limited ranges of data used in its generation and they are not function of reservoir rock and fluid data that vary per each reservoir. The analytical correlations suffer from the difficulty of obtaining their input data for its application.
In this work, the effects of wide range of rock and fluid properties on IPR for solution gas-drive reservoirs were studied using 3D radial single well simulation models to generate a general IPR correlation that functions of the highly sensitive rock and fluid data.
More than 500 combinations of rock and fluid properties were used to generate different IPRs. Non-linear regression was used to get one distinct parameter representing each IPR. Then a non-parametric regression was used to generate the general IPR correlation. The generated IPR correlation was tested on nine synthetic and three field cases.
Results & Conclusion:
The results showed the high application range of the proposed correlation compared to others that failed to predict the IPR. Moreover, the proposed correlation has an advantage that it is explicitly function of rock and fluid properties that vary per each reservoir.