Article Sidebar
Article Details
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form may be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
Main Article Content
An Improved Model for the Prediction of Liquid Loading in gas Wells using Firefly and Particle Swarm Optimization Algorithms
Abstract
Liquid loading is an undesired phenomenon in gas wells that occurs when producing wells attain a flow rate below which liquid will not be able to flow to the surface. The inability of the energy from the gas to transport the liquid to the surface causes back flow and eventual accumulation of liquid at the wellbore. This is characterised by intermittent flow, which, if left unchecked, can eventually kill the well. An effective and reliable predictive method must therefore, be employed. In this study, improved models based on data set from condensate/water in a gas well were developed by applying firefly (FA) and particle swarm optimisation (PSO) algorithms. The results showed that the model developed out perform many of the existing models. The models predicted liquid loading in gas well at 86% level of accuracy compared to the 81% highest possible from published models. Although, the FA and PSO models predicted liquid loading at higher accuracy compared with Turner and Coleman models for higher wellhead pressure systems, the Coleman model appeared to perform better in the prediction of critical gas rate for low-pressure systems. However, the developed model can significantly improve the prediction of liquid loading in gas wells at a higher reliability and accuracy levels. Thus, the proposed models can be a veritable tool for accurately predicting liquid loading in gas wells.
