Horizontal Gas Well Liquid Accumulation State Determination based on Ensemble Learning Algorithm

Yu Fu; Qinghui He

doi:10.54097/fcis.v4i1.9419

Authors

Yu Fu
Qinghui He

DOI:

https://doi.org/10.54097/fcis.v4i1.9419

Keywords:

Gas Well Liquid Accumulation, Critical Liquid-carrying Flow Rate, Machine Learning, Drainage Gas Recovery

Abstract

With the depletion of reservoir energy, liquid accumulation in horizontal wells of the Sulige gas field has become an increasingly severe issue. This study proposes a GBDT algorithm-based predictive model using pure on-site data mining, which overcomes the limitations of traditional models in considering complex wellbore structures and the coupling effects of multiphase flow. Through an analysis of the liquid accumulation state in 25 gas wells in the block, the model achieves an accuracy of 84% in determining the wellbore liquid status, demonstrating higher accuracy compared to traditional models. It provides a more accurate prediction method for addressing gas well liquid accumulation issues.

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