International Journal of Econometrics and Financial Management
ISSN (Print): 2374-2011 ISSN (Online): 2374-2038 Website: http://www.sciepub.com/journal/ijefm Editor-in-chief: Tarek Sadraoui
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International Journal of Econometrics and Financial Management. 2014, 2(6), 236-242
DOI: 10.12691/ijefm-2-6-3
Open AccessArticle

Predicting Oil Production Level for Specified Cost, Price and Revenue

C. I. Nwoye1, , I. D. Adiele2, S. E. Ede3, I. H. Obiagwu4 and C. C. Nwoye5

1Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

2Department of Engineering Research Development and Production, Project Development Institute, Enugu, Nigeria

3Department of Metallurgical and Materials Engineering, Enugu State University of Science & Technology, Enugu, Nigeria

4United Bank for Africa, Lagos, Nigeria

5Robert Gordon University, Aberdeen

Pub. Date: October 29, 2014

Cite this paper:
C. I. Nwoye, I. D. Adiele, S. E. Ede, I. H. Obiagwu and C. C. Nwoye. Predicting Oil Production Level for Specified Cost, Price and Revenue. International Journal of Econometrics and Financial Management. 2014; 2(6):236-242. doi: 10.12691/ijefm-2-6-3

Abstract

This paper presents an evaluation of the trend of oil level production dependence on its operating cost, current oil price and expected revenue over a production period of 10 years (2003-2012). Results from both experiment and model prediction show that the oil production level is very significantly affected by its operating cost and expected revenue at a particular current oil price. A three-factorial model was derived, validated and used for the response analysis. Results from evaluations indicated that the standard error incurred in predicting oil production level for each value of the expected revenue & operating cost considered, as obtained from derived model and regression model were 0.0401 and 3.5 x 10-4 & 3.7348 and 3.7544% respectively. Further evaluation indicates that oil production per unit operating cost as obtained from experiment; derived model and regression model were 0.0556, 0.0552 and 0.0552 MTSB /M$ respectively. Comparative analysis of the correlations between oil production and operating cost, current oil price and expected revenue as obtained from experiment; derived model and regression model indicated that they were all > 0.99. The maximum deviation of the model-predicted oil production level (from experimental results) was less than 7%. This translated into over 93% operational confidence for the derived model as well as over 0.93 reliability response coefficients of oil production dependence to the operating cost, current oil price and expected revenue.

Keywords:
prediction oil production level cost price revenue

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