New Production Rate Model of Wellhead Choke for Niger Delta Oil Wells

Document Type: Research Paper

Authors

Department of Petroleum Engineering, Federal University of Petroleum Resources, Effurun, Nigeria

Abstract

An accurate prediction of production rate for wellhead choke is highly vital in petroleum production engineering applications. It is deployed in the control of surface production, prevention of water and gas coning, and optimization of the entire production systems. Although there are several choke correlations in literature to estimate production rate; however, most of the published correlations were derived with datasets outside Niger Delta fields. Thus, this study presents a new empirical-based model, which is a derivative from Choubineh et al. model, to predict the liquid production rate of chokes for Niger Delta oil wells. The new model was developed and optimized using multivariate regression and the Generalized Reduced Gradient (GRG) optimization algorithm. Furthermore, a total of 283 production data points from 21 oil wells in 7 fields in the Niger Delta region, with a randomly generated ratio of 70: 30 of the datasets, was used to develop and validate the developed model. The developed Model 2 predicted the choke production rate with a fitting accuracy of average absolute percentage error (AAPE) of 23.73% and coefficient of determination (R2) of 0.973; in addition, the model predicted validating accuracy of AAPE of 9.33% while the coefficient of determination (R2) stands at 0.982. Consequently, this model can be relied on as a quick and robust tool for estimating the choke production rate of producing oil wells. Moreover, the sensitivity analysis results show that the choke size has the most significant impact on the predicted liquid rate. In contrast, gas gravity has the least impact.
 

Keywords


  1. Guo B, Lyons WC,  Ghalambor AG (2007) Petroleum production engineering: A computer-assisted approach, 1st ed., Gulf Professional Publishing, 1-287.##
  2. Ghareeb M, Shedid AS (2007) A new correlation for calculating wellhead production considering influences of temperature, GOR and water-cut for artificially lifted wells, International Petroleum Technology Conference, 4-6. ##
  3. Tangren RF, Dodge CH, Seifert HS (1949) Compressibility effects in two-phase flow, Journal of Applied Physics, 20, 7: 637–645. ##
  4. Gilbert WE (1954) Flowing and gas-lift well performance, in Drilling and production practice, New York: American Petroleum Institute. ##
  5. Baxendell PB (1958) Producing Wells on Casing Flow – An Analysis of flowing pressure gradients, AIME, 213, 01: 202-206. ##
  6. Ros N (1961) An analysis of critical simultaneous gas/liquid flow through a restriction and its application to flow metering, Journal of Applied Science Research, 9, 1: 374–389. ##
  7. Achong IB (1961) Revised bean and performance formula for Lake Maracaibo wells, Houston, TX. ##
  8. Pilehvari AA (1981) Experimental study of critical two-phase flow through wellhead chokes, University of Tulsa. ##
  9. Beiranvand MS, Mohammadmoradi P, Aminshahidy B, Fazelabdolabadi B (2012) New multiphase choke correlations for a high flow rate Iranian oil field, Journal of Mechanical Science, 3, 1: 43–47. ##
  10. Owolabi OO, Kune KK, and Ajienka JA (1991) Producing the multiphase flow performance through wellhead choke for the Niger Delta oil wells, International Conference of the Society of Petroleum Engineers Nigeria Section Annual Proceeding, Nigeria. ##
  11. Okon AN, Udoh FD, Appah D (2015) Empirical wellhead pressure – production rate correlations for niger delta oil wells, Society of Petroleum Engineers Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, August 4-6. ##
  12. Khorzoughi MB, Beiranvand MS, Rasaei MR (2013) Investigation of a new multiphase flow choke correlation by linear and non-linear optimization methods and Monte Carlo sampling, Journal of Petroleum Exploration, Production and Technology, 3: 279-285. ##
  13. Choubineh A, Ghorbani H, Wood DA, Moosavi SR, Khalafi E, Sadatshojaei E (2017) Improved predictions of wellhead choke liquid critical-flow rates: modelling based on hybrid neural network training learning-based optimization, Fuel, 207: 547–565. ##
  14. Ghorbani H, Moghadasi J, Wood DA (2017) Prediction of gas flowrates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm, Journal of Natural Gas Science Engineering, 43: 256-271. ##