新疆石油地质

新疆石油地质 ›› 2026, Vol. 47 ›› Issue (4): 481-487.doi: 10.7657/XJPG20260412

• 油藏工程 • 上一篇    下一篇

基于机器学习的凝析油气井井底流压预测模型

刘天宇1(), 赵海勇2, 吴利利1, 马丽2   

  1. 1 中国石油 长庆油田分公司 a.油气工艺研究院； b.低渗透油气田勘探开发国家工程实验室， 西安 710018
    2 中国石油 长庆油田分公司 第十一采油厂， 甘肃 庆阳 745000
  • 收稿日期:2025-07-09 修回日期:2025-09-01 接受日期:2025-09-16 出版日期:2026-08-01 发布日期:2026-07-30
  • 作者简介:刘天宇（1989-），男，黑龙江哈尔滨人，工程师，油气田智能开采，（Tel）029-86590669（Email）18071293968@163.com
  • 基金资助:
    中国石油科研项目(2024DJ26)

Machine Learning-Based Prediction Model of Bottomhole Flowing Pressure in Condensate Oil and Gas Wells

LIU Tianyu1(), ZHAO Haiyong2, WU Lili1, MA Li2   

  1. 1 PetroChina Changqing Oilfield Company, a. Oil & Gas Technology Research Institute; b. National Engineering Laboratory for Exploration and Development of Low Permeability Oil & Gas Fields, Xi’an, Shaanxi 710018, China
    2 No.11 Oil Production Plant, Changqing Oilfield Company, PetroChina, Qingyang, Gansu 745000, China
  • Received:2025-07-09 Revised:2025-09-01 Accepted:2025-09-16 Published:2026-08-01 Online:2026-07-30

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摘要：

针对凝析气采出时在井筒内温度压力变化过程中存在气液相变，导致传统方法如经验公式、修正H-B算法计算井底流压精度低的问题，提出了基于BP神经网络和LightGBM算法的机器学习井底流压预测模型。通过采集24口井1 536组生产数据，经过标准化、噪声剔除与相关性分析后，分别采用修正H-B算法、BP神经网络与LightGBM算法进行建模。结果表明，LightGBM算法的直方图分裂与特征绑定技术能够显著降低计算复杂度并提高拟合精度，相关系数可达0.989 3，平均绝对误差较修正H-B算法降低86.5%，并对井底流压预测主控因素进行量化分析，发现产气量对井底流压影响最大。在准确拟合井底流压的基础上，对无测压条件的凝析气井井底流压进行推广拟合，模型精准识别2口处于反凝析高危压力点的生产井。通过调整生产制度，A6井在井口油压增大2.73 MPa的基础上日产气量提高超2 000 m3，采气指数提高31.6%，有效识别高风险井并指导生产制度优化。机器学习可高效量化井筒多相流动态，为凝析气藏调整生产制度与反凝析污染防控提供可靠技术支撑。

关键词: 机器学习, 凝析气, 井底流压, BP神经网络, LightGBM算法, 预测模型

Abstract:

During production of condensate gas, there is a gas-to-liquid phase transition along with temperature and pressure changes in wellbores. This phenomenon results in low accuracy of conventional bottomhole flowing pressure (BHFP) calculation methods such as empirical formula and the modified H-B algorithm. To address this problem, a BHFP prediction model based on machine learning (ML) algorithms including BP neural network and LightGBM was proposed. Using 1,536 measured datasets from 24 wells, followed by normalization, noise removal, and correlation analysis, the modified H-B algorithm, BP neural network, and LightGBM algorithm were employed for modeling. The results show that the histogram-based splitting and feature binding techniques of LightGBM can significantly reduce the computational complexity and improve the fitting accuracy, with the correlation coefficient up to 0.9893, and the mean absolute error (MAE) of 86.5% lower than the modified H-B algorithm. A quantitative analysis was conducted on the main factors controlling BHFP prediction, suggesting gas production as the most prominent factor. Based on the accurate fitting of BHFP, the proposed model was extended to condensate gas wells without pressure-measurement conditions, and it accurately identified two production wells at high-risk pressure points of retrograde condensation. Specifically, for Well A6, the production regime was adjusted by increasing the wellhead tubing pressure by 2.73 MPa, which ultimately led to an increase in the daily gas production by more than 2,000 m3/d, and in the gas productivity index by 31.6%. This performance validated the effectiveness of the proposed model in identifying high-risk wells and guiding optimization of production regime. It is concluded that the ML-based BHFP prediction model can efficiently quantify the multiphase flow behaviors in wellbores, providing reliable technical support for adjusting the production regime of condensate gas reservoirs and controlling retrograde condensation pollution.

Key words: machine learning, condensate gas, bottomhole flowing pressure, BP neural network, LightGBM algorithm, prediction model

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