Development of Universal Decline Curve Analysis Technique for Forecasting the Performance of Oil Wells

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-31 Number-5
Year of Publication : 2016
Authors : Adeloye, Olalekan Michael, Mabamije Stephen, Abu, Robin Nyemenim
DOI :  10.14445/22315381/IJETT-V31P245

Citation 

Adeloye, Olalekan Michael, Mabamije Stephen, Abu, Robin Nyemenim"Development of Universal Decline Curve Analysis Technique for Forecasting the Performance of Oil Wells", International Journal of Engineering Trends and Technology (IJETT), V31(5),251-259 January 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Linear and Nonlinear regression analysis were carried out on Arps decline curve by using analytical Natural Cubic Spline Interpolation and Levenberg-Marquardt algorithm respectively. The linearised model was used to initialize the nonlinear model and this shows improvement in the result of nonlinear model by Levenberg-Marquardt algorithm.The results of these analyses were used for history-matching existing oil well production data from Niger Delta Field, Nigeria. The nonlinear regression by Levenberg-Maquardt algorithm shows high degree of accuracy in history matching the oil field production data and also for future production forecast.

 References

[1]. Makinde, F.A., Orodu, O.D., Ladipo, A. O. and Anawe, P.A.L. (2012): Cumulative Production Forecast of an oil well using Simplified Hyperbolic-Exponential Decline models, Global Journal of Researches in Engineering, 2(12)
[2]. Ahmed, T. (2006): Reservoir Engineering Handbook. 2nd edition. Gulf Publishing Company, Texas. 850 – 853.
[3]. Khulud, M.R, Mohammed, H., Hissein, N. and Giuma,S. (2013): Prediction of Reservoir Performance Applying Decline Curve Analysis, International Journal of Chemical Engineering and Application, 4(2),74.
[4]. Manolis, I.A.L. (2005) A Brief Description of the Levenberg- Marquardt Algorithm implemented by Lemver, Technical Report, Institute of Computer Science, Foundation for Research and Technology, Hellas.
[5]. Henri, P.G. (2015): The Levenberg-Marquardt Method for Nonlinear Least Squares Curve Fitting Problems, Department of Civil and Environmental Engineering, Duke University.
[6]. Madsen, K., Nielsen, H.B. and Tingleff, O. (2004): Methods for Nonlinear Square Problems, Technical Report, Informatics and Mathematical Modeling, Technical University of Denmark, Lecture Note, Available at http://www.imm.dtu.dk/courses/02611/n11sq.pdf
[7]. Kewen, L and Roland, N.H. (2003): A Decline Curve Analysis Model based on Fluid Flow Mechanism, 83470-MS, SPE. A Paper Presented at the SPE Western Regional/AAPG Pacific Section Joint Meeting held at Long Beach, California, USA.
[8]. Marks, K.T and James, P.S. (2012) Improvements to the Levenberg-Marquardt Algorithm for Nonlinear Least Squares Minimization, Preprint Submitted to Journal of Computational Physics.
[9]. Chithra, C. N. C., Ki-Young, S., Deoki, N.S. and Madan, M.G. (2013): Production Forecasting of Petroleum ReservoirAapplying Higher-Order Neural Networks with limited Reservoir Data, International Journal of Computer Application,0975-8887,72(2).

Keywords
Linear and Non-linear Regression, Levenberg-Marquardt algorithm, History Matching.