油气藏评价与开发 >
2025 , Vol. 15 >Issue 1: 152 - 160
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2025.01.019
基于模糊逻辑控制的页岩气井智能化调产预警
收稿日期: 2023-12-04
网络出版日期: 2025-01-26
基金资助
中国石化科技部项目“威荣深层页岩气立体开发优化技术研究”(P22041)
Intelligent production adjustment early warning for shale gas wells based on fuzzy logic control
Received date: 2023-12-04
Online published: 2025-01-26
在大数据技术不断发展的背景下,对油气田进行数字化、智能化生产管理已成为一种必然趋势。页岩气井存在积液、出砂和强应力敏感等问题,产量调整中需要考虑诸多因素。传统的方法存在人工工作量大、调产预警效率偏低等问题,无法综合考虑多重因素实施最优化调产。为解决这一问题,结合页岩气井典型生命周期特征,将其划分为排液输气、稳产降压和定压降产3个阶段,明确气井全生命周期差异化的调产规则,设定稳产期压降法、单位压降产气法、临界出砂流量法、临界携液流量法、经验图版法和间歇开关井法6项调产指标。根据调产规则、调产指标与现场经验建立了基于模糊逻辑控制的页岩气井智能化调产预警模型,并采用Python编程实现所有调产指标随气井生产动态变化的实时计算和模糊逻辑控制算法。该方法在威荣页岩气田百余口井调产预警时间仅需30 s,而传统方法至少需要5 d,气井的调产需求被及时预警,取得了较好的应用效果。未来可结合远程控制的油嘴或节流阀实现生产制度调节,为智能化油气田提供技术支撑。
何春艳 , 赵勇 , 李南颖 , 杨建 , 曹海涛 , 唐荣林 . 基于模糊逻辑控制的页岩气井智能化调产预警[J]. 油气藏评价与开发, 2025 , 15(1) : 152 -160 . DOI: 10.13809/j.cnki.cn32-1825/te.2025.01.019
Against the backdrop of continuously advancing big data technology, digitization and intelligent production management of oil and gas fields have become an inevitable trend. Shale gas wells face challenges such as liquid accumulation, sand production, and high stress sensitivity, necessitating the consideration of numerous factors during production adjustment. Traditional methods, which require extensive manual labor and exhibit low efficiency in production adjustment warnings, fail to consider multiple factors for optimal adjustment. To address this issue, considering the typical lifecycle characteristics of shale gas wells, the study divided the lifecycle into three stages: liquid unloading and gas transportation, stable production and pressure reduction, and fixed pressure and production reduction. Specific production adjustment rules tailored to different lifecycle stages were defined and six types of production adjustment indicators were proposed, including pressure drop method in stable production period, gas production per unit pressure drop method, critical sand-carrying flow rate method, critical liquid-carrying flow rate method, empirical chart method, and intermittent well-switching method. Based on these production adjustment rules and indicators, combined with field experience, an intelligent production adjustment early warning model for shale gas wells using fuzzy logic control was established. Implemented in Python, this model allows for real-time calculation of all production adjustment indicators and the fuzzy logic control algorithm as the well production dynamics change. This method has been applied in over a hundred wells in the Weirong shale gas field, reducing early warning times for production adjustments to just 30 seconds, compared to the traditional methods that require at least 5 days. The timely early warning of production adjustment needs has achieved good application results. In the future, combining remote control of oil nozzles or choke valves to adjust the production regime can provide technical support for intelligent oil and gas fields.
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