基于深度学习的海上气田生产系统数据驱动软测量模型

Data-driven soft sensing model for production system of offshore gas fields based on deep learning

针对海上气田生产系统关键状态变量的运行数据获取难度大、成本高、可靠性低的问题,开展软测量研究,建立了数据驱动的流量、压力动态估计模型,为中控人员提供在线监测工具,便于进行系统安全分析。综合动态和稳态样本,采用以深度学习为基础的黑箱辨识技术、以迁移学习为基础的参数校正技术,建立了包含黑箱和灰箱的非线性有源自回归(Nonlinear Auto-Regressive with Exogenous Inputs,NARX)深度前馈网络(Dense Neural Network,DNN)模型(简称DNN-NARX模型)库,近似描述天然气井的动态流动特性,用以估计单井流量和井口压力。通过实例验算,分别对比了DNN-NARX黑箱与灰箱动态模型、DNN-NARX模型与传统的多层感知器(Multi-Layer Perception,MLP)-NARX模型(简称MLP-NARX模型)的模拟结果。结果表明,DNN-NARX模型与MLP-NARX模型的准确度和计算时间均满足在线估计需求,其中,DNN-NARX灰箱动态模型的优势较突出,抗干扰能力和泛化能力更强。所建模型具有较强的工程适用性,对海上油气生产领域的软测量问题具有良好的借鉴意义。

In view of the problems of great difficulty,high cost and low reliability in obtaining the operation data on the key state variables of production system in offshore natural gas fields,research on soft sensing was carried out,and a dynamic data-driven estimation model of flow and pressure was established,providing the central controllers an online monitoring tool for safety analysis of the system.Combining the dynamic and static samples,a Dense Neural Network model library of Nonlinear Auto-Regressive with Exogenous Inputs(DNN-NARX model),comprising the black box and grey box,was established with the black-box identification technique based on deep learning,as well as the parameter correction technique based on transfer learning,approximately describing the dynamic flowing characteristics of gas production well,so as to estimate the single well flow and wellhead pressure.The simulation results of the dynamic DNN-NARX black-box and grey-box model,the DNN-NARX model and the traditional Multiple-Layer Perception-NARX model(MLP-NARX model)were compared by examples of calculation.The results indicate that both of the accuracy and calculation time of DNNNARX model and MLP-NARX model satisfy the requirements of online estimation.Therein,the dynamic DNN-NARX grey-box model shows the remarkable advantages of higher immunity from interference and generalization capability.Thus,the proposed model has high engineering applicability,providing good referential significance to the soft sensing problems in the field of offshore oil and gas production.