Affiliation:
1. Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Jingzhou 434100, China
Abstract
The prediction of the liquid build-up height in gas wells is a crucial aspect of reservoir development and is essential for the efficient execution of drainage and gas extraction operations. Excessive liquid accumulation can lead to well flooding and operational shutdowns, resulting in significant economic losses. To prevent such occurrences, accurate estimation of the liquid height in gas well tubing is necessary. However, existing petroleum engineering models face numerous challenges in predicting liquid height, including complex theoretical solution steps and reliance on fundamental well parameters and extensive empirical data. The paper proposes an innovative blend of the Crayfish Optimization Algorithm (COA) with the eXtreme Gradient Boosting (XGBoost) methodology to forecast the liquid loading heights in gas wells. The COA is employed to optimize eight hyperparameters of the XGBoost, including the number of trees, maximum depth, minimum child weight, learning rate, minimum loss reduction, subsample, L1 regularization, and L2 regularization. After fine-tuning the hyperparameters, the XGBoost undergoes a retraining process, followed by an evaluation. Through comparative analysis with actual measurements from 32 wells in a gas field as well as support vector regression (SVR), XGBoost, random forest (RF), and PLATA (which predict liquid volume in the tubing and annulus), the proposed COA–XGBoost demonstrates a high degree of alignment with the measured values. It provides the most accurate predictions, with a mean relative error of only 2.25%. Compared with the traditional XGBoost, the COA–XGBoost reduced the mean relative error in predicting gas well tubing liquid loading height by 32.63%. Compared with the previous PLATA, the proposed model achieved a 3.52% decrease in mean relative error, enabling more accurate assessment of the severity of liquid loading in gas wells.
Reference34 articles.
1. Improved prediction of liquid loading in gas wells;Shekhar;SPE Prod. Oper.,2017
2. A simplified model to predict transient liquid loading in gas wells;Pagan;J. Nat. Gas Sci. Eng.,2016
3. Methodology for estimating recovery factors in large tight sandstone gas fields;Guo;Acta Pet. Sin.,2018
4. An improved model to predict liquid holdup in vertical gas wells;Luo;J. Pet. Sci. Eng.,2020
5. A Deep Regression Method for Gas Well Liquid Loading Prediction;Chen;SPE J.,2024