基于梯度提升回归树的气井油管积液高度预测OA

Prediction of liquid accumulation height in gas well tubing is a crucial aspect of gas reservoir development and an indispensable part of drainage gas recovery.In the late stages of gas well exploitation,liquid accumulation occurs at the bottom of the well,and excessive liquid accumulation can lead to well shutdown.To mitigate this issue,it is essential to predict the liquid accumulation height in the gas well tubing.However,traditional petroleum engineering models for predicting gas well tubing liquid accumulation height face challenges,including the need for a substantial amount of empirical parameters in specific calculations.This paper proposes a method based on the Gradient Boosting Regression Tree(GBRT)model to predict the liquid accumulation height in gas well tubing.The approach utilizes seven production data features,including annular pressure,oil pressure,tubing depth,reservoir depth,daily gas production,daily water production,and wellhead temperature.Employing ensemble learning,the method combines predictions from multiple decision trees in an iterative stepwise manner to enhance the overall performance of the model,thereby accurately predicting the liquid accumulation height in gas well tubing.Through comparative analysis with measurements from 32 instrument-detected wells,regression decision trees,and random forests,the GBRT model exhibits good agreement between predicted and actual values,demonstrating superior predictive performance.The average relative error is only 3.87％,and the adjusted R2 is 0.85.Compared to existing models for predicting liquid accumulation in the tubing and annulus,the model proposed in this paper reduces the average relative error by 1.9％.