Scholarly record
ARTIFICIAL INTELLIGENT FOR PREDICTION OF CONTINUOUS GAS LIFT PARAMETERS
Abstract
The purpose of this study has been assessment of capability of Artificial Neural Networks in Gas Lift Operations optimization, which is in use for improved oil recovery from oil wells. This in detail is in fact estimation of the two most important parameters of the process, i.e. optimal injection depth and optimal gas injection rate. For gas lift, gas is injected continuously or intermittently at selected location(s), resulting in a reduction in the natural flowing gradient of the reservoir fluid, and thus reducing the hydrostatic component of the pressure drop from the bottom to the top of the well. In this article two Neural Network models are presented for prediction and optimization of both the optimal injection depth and gas injection rate, using this methodology. For this purpose four-layer neural networks have been designed and trained using real data of 36 wells. After the training step, four real data were also used for the model test step and as a reliability check. The outputs of models for test data are compared with the wellflo v3.6d software analysis. It has been concluded that Artificial Neural Networks approach has an excellent competing capability for this purpose compared to the conventional methods and can be used interchangeably. This methodology and design can significantly help in the prompt optimal design of gas lift operations. This appears to be the first report of applying ANNs to continuous gas lift optimization problem.
Publication details
References8
Duns H. Jr. and J. C. J. Ros, "Vertical Flow of Gas and Liquid Mixtures in Wells", Proc. 6th World Petroleum Congress, Frankfurt, 1963, 451-65
Kaya A. S., Sarica C. and Brill J. P., "Comprehensive Mechanistic Modeling of Two-Phase Flow in Deviated Wells", SPE 56522, presented at the SPE Annual Technical Conference and Exhibition held in Huston, TX, October 3-6, 1 999
Eclipse Technical Description v2004A, p. 277, Schlumberger GeoQuest Package.
Brown, K.E. et. al. (1985), "System analysis of oil and gas wells", SPE 14714, Richardson, TX.
McCulloch, W.S., Pitts, W. (1943), "A logical calculus of ideas immanent in nervous activity", Bulletine of Mathematical Biophysics 5, 115.
Widrow, B. (1962), "Generalization and information storage in networks of Adeline neurons", Self-Organizing Systems, Yovitz MC, Jacobi, G.T. and Goldstein,G.D. (eds.), Spartan Books, Washington DC, pp. 435-461.
Malallah, A., Nashawi, S. (2005), "Estimating the fracture gradient coefficient using neural networks for a field in the Middle East" Journal of Pet. Science and Eng. 49, pp. 193-211.
Arbib, M.A. (2003), "Handbook of brain theory and neural networks", 2nd edition, MIT.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.