摘要
以地下储气库安全、智能化地注入与天然气智能化采集为目标,设计基于云平台的智能化储气系统。系统的基础设施层在地下储气库注采井中设置天然气传感模块。通过天然气传感模块采集注采井内的天然气浓度、流量、液压等数据。所采集的天然气数据传送至井口安全控制模块。井口安全控制模块利用技术层的比例积分微分(PID)神经网络的智能控制算法,控制地下储气库注采井井口安全阀关断,保障地下储气库的安全生产。智能化储气全过程在系统的业务层完成。业务层结合引擎模块、开发工具模块、支撑模块、管理模块共同合作为云平台用户提供服务。系统通过虚拟机迁移控制方法降低云平台的资源消耗,以提升云平台运算效率。系统测试结果表明,所设计的系统可以智能化地控制地下储气库注采井井口安全阀,保障天然气的良好注入与采集。
The intelligent gas storage system based on cloud platform is designed with the goal of safely and intellently injection and collection of natural gas intelligence in underground gas storage. The infrastructure layer of the system installs the natural gas sensing module in the injection and extraction wells of the underground gas storage reservoir. The natural gas sensor module collects natural gas concentration, flow rate and hydraulic pressure data from the injection and extraction well. The collected natural gas data is transmitted to the wellhead safety control module. The wellhead safety control module uses the intelligent control algorithm of proportional integral differential(PID) neural network of the technology layer to control the wellhead of the injection and extraction nells safety valve shut-off of the injection and extraction well in the underground gas storage reservoir to ensure the safe production of the underground gas storage reservoir. The whole process of intelligent gas storage is completed in the business layer of the system. The business layer combines engine module, development tool module, support module, and management module to work together to provide services for cloud platform users. The system reduces the resource consumption of the cloud platform and improves the computing efficiency of the cloud platform through the virtual machine migration control method. The system test results show that the designed system can intelligently control the wellhead safety valves of injection and extraction wells in underground gas storage to ensure good natural gas injection and collection.
作者
张磊
ZHANG Lei(CNPC(Xinjiang)Petroleum Engineering Co.,Ltd.,Kelamayi 834000,China)
出处
《自动化仪表》
CAS
2023年第2期53-58,共6页
Process Automation Instrumentation
关键词
云平台
智能化
储气系统
引擎模块
注采井
安全阀
虚拟机
比例积分微分神经网络
Cloud platform
Intelligence
Gas storage system
Engine module
Injection and extraction wells
Safety valve
Virtual machine
Proportional integral differential(PID)neural network