| 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’ research and development in 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 none-empirical,that is applicable to predict the onset of liquid loading in versatile gas wells conditions,e.g.,horizontal,vertical and inclined,is still unavailable yet.In this paper,a complete literature review,investigation of these existing liquid loading onset prediction models and mechanism of liquid in different types of gas wells were conducted.The detailed information of more than 600 gas wells,including well geometries,gas properties,operation conditions etc.,from different gas fields was obtained.Then,the validity of various liquid loading onset prediction models can be evaluated by a novel model ranking approach and a CNN model.To fully account for the effects of gas well properties(including but not limited to production,wellhead pressure,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.At the same time,based on one-dimensional convolutional neural network(1D-CNN),the other study built a liquid loading prediction model according to the expanded gas well data set.The final prediction average accuracy of the model on the test data can reach 92%,and has good generalization ability.An extensive comparison and verification of the ranking approach and CNN model indicates that the combined method provides a good reference for the rational production allocation and stable production of gas wells. |
