Table of Contents
Journal of Petroleum Engineering
Volume 2015, Article ID 714541, 16 pages
Research Article

Examination of Experimental Designs and Response Surface Methods for Uncertainty Analysis of Production Forecast: A Niger Delta Case Study

1Department of Petroleum Engineering, African University of Science and Technology (AUST), Km 10 Airport Road, Galadimawa, Abuja, Nigeria
2No. 1, Odi Street, Old GRA, Port Harcourt, Rivers State, Nigeria

Received 18 November 2014; Accepted 22 February 2015

Academic Editor: Mikhail Panfilov

Copyright © 2015 Akeem O. Arinkoola and David O. Ogbe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this paper is to examine various DoE methods for uncertainty quantification of production forecast during reservoir management. Considering all uncertainties for analysis can be time consuming and expensive. Uncertainty screening using experimental design methods helps reducing number of parameters to manageable sizes. However, adoption of various methods is more often based on experimenter discretions or company practices. This is mostly done with no or little attention been paid to the risks associated with decisions that emanated from that exercise. The consequence is the underperformance of the project when compared with the actual value of the project. This study presents the analysis of the three families of designs used for screening and four DoE methods used for response surface modeling during uncertainty analysis. The screening methods (sensitivity by one factor at-a-time, fractional experiment, and Plackett-Burman design) were critically examined and analyzed using numerical flow simulation. The modeling methods (Box-Behnken, central composite, D-optima, and full factorial) were programmed and analyzed for capabilities to reproduce actual forecast figures. The best method was selected for the case study and recommendations were made as to the best practice in selecting various DoE methods for similar applications.