A Model Ranking Approach for Liquid Loading Onset Predictions

Abstract

As a natural gas well ages, liquid loading is frequently encountered, leading to the decrease of gas production rate and many other side effects, which may in turn cease the gas production. Thus, to accurately predict liquid loading onset is of significant importance in gas wells for the sake of stable production. With years of research and development in the natural gas industry, the liquid loading onset prediction models prevail in the existing literature. Based on varying mechanisms (e.g., droplet falling back, liquid film reversal, etc.), the critical gas velocities or flow rates corresponding to flow pattern transitions in gas wells can then be calculated. However, a universally validated model, whether empirical or non-empirical, that is applicable to predict the onset of liquid loading in versatile gas wells conditions (e.g., horizontal, vertical, and inclined) is, as yet, still unavailable. In this paper, we conduct a complete literature review and investigation of these existing liquid loading onset prediction models. First, we obtained detailed information of more than 600 gas wells, including well geometries, gas properties, operation conditions, and so on, from different gas fields. Then, we evaluate the validity of various liquid loading onset prediction models by use of a novel model ranking approach. To fully account for the effects of gas well properties (including but not limited to production, wellhead pressure, and pipe diameter) to the model prediction accuracy, the proposed method in this paper employs data clustering and normalization techniques, as well as the statistical relative error analysis, to rank and select the best suitable model for each specific gas well. Extensive comparison and verification of the ranking approach indicate that the proposed method provides a good reference for the rational production allocation and stable production of gas wells.