基于大数据的油气集输系统生产能耗时序预测模型

针对集输系统组成关系多、系统行为复杂、子系统之间以及系统与环境之间的关联程度高、耦合性强、易产生故障和能耗高等特点,基于油气集输生产过程中积累的温度、压力、流量、设备工作制度、能耗等海量数据,建立了集输数据粒度模型,实现了基于热能利用率、单位液量能耗等多目标、多变量时序的集输系统生产能耗预测。针对不同时间粒度(如日、月、年等)、不同空间粒度(如井组、区块、油田等)、不同集输方式粒度(如单相输、油-气-水混输),建立了多变量时序混沌能耗预测模型;构造了粒关联规则模式挖掘算法。以大港油田A集输系统为例,研究了集输生产系统的能耗因素粒之间的关联关系;预测了集输生产参数调整对系统未来能耗变化,获得集输系统效率和能耗的预警。

Energy consumption prediction and application in oil and gas gathering and transferring system production based on large data

The gathering system, which is prone to failure and high energy consumptions, consists of many different parts, perform complex system behaviors and has a high degree of correlation with the environment and strong coupling with each other.In the view of these characteristics, in this paper, based on the big data including temperature, pressure, flow rate, working system of the equipment and energy consumptions data accumulated during oil and gas gathering and transferring process, a granularity model of gathering and transferring data is set up to predict energy consumption with multiple targets like heat energy utilization efficiency, energy consumption per unit amount of liquid and multi-variable time sequence. A chaotic energy consumption forecasting model with multi-variable time sequence is established according to different time granularity(day, month and year, etc.), different space granularity(well group, block and field, etc.) and different ways of gathering and transferring(single-phase flow transferring and oil-gas-water three-phase flow transferring, etc.).Take a certain gathering system in Dagang oilfield as an example, the grain of association rule algorithm is constructed to study the relationship between the energy consumption factors of gathering system. Early warnings of gathering and transferring system efficiency and energy consumption are gained by predicting the production parameters.