Liquid loading prediction and identification model for vertical and inclined gas wells

Abstract

Defined as the backflow of a liquid film into a wellbore, liquid loading is a severe issue for gas wells because it decreases the production rate of gas. If the rate of liquid accumulation in the wellbore is extremely high, the production rate will significantly decrease, and, under extreme cases of accumulation, the operating company will abandon the well, leading to substantial financial losses. Consequently, to avoid this, it is appropriate for the operating company to predict and identify the liquid loading status of the gas wells and use practical tools and pathways to prevent such a loading.This paper introduces a model based on a liquid film reversal to predict liquid loading. It adopts the momentum balance equation of each phase as a basis and transcends the limits of earlier models. The proposed model relies on the theory, which assumes that the loading phenomenon initiates when the transition from an annular flow (liquid film surrounding the gas core) into a slug or churn flow takes place. Furthermore, the developed model considers the influences of the deviation angle, the tubing diameter, and the void fraction.The efficiency of the proposed model is evaluated by comparing it with few renowned existing models using vertical, inclined, and near-horizontal published gas field datasets, newly acquired ones, as well as laboratory datasets from published papers. As a result, the proposed model provides both the highest prediction accuracy and the least average errors. Further results show that the tubing diameter and the inclination angle are the leading influential parameters of the critical gas flow velocity/rate. Consequently, as the proposed model outperforms the earlier published models, it is the most suitable model for identifying and predicting the liquid loading in vertical, inclined, and near-horizontal gas wells.