Research on data‐driven model for soft sensing of natural gas production system

Abstract

AbstractIn view of the problems of high cost and low reliability in obtaining operation information such as flow rate and pressure of offshore natural gas production system, research on soft sensing is carried out, and a dynamic data‐driven model bank is established, in purpose of estimating single‐well flow rate and wellhead pressure, providing convenience tool for online monitoring and system safety analysis. Combining dynamic and steady‐state samples, introducing black‐box identification techniques including orthogonal least square regression and deep learning along with parameter correction techniques such as bi‐objective least square algorithm and transfer learning, a series of nonlinear auto‐regressive models with exogenous inputs (NARX) are built, consisting of black‐box and gray‐box polynomial NARX (Poly‐NARX) models as well as deep neural network NARX (DNN‐NARX) models, approximately describing the dynamic performance of gas production well. Through realistic operation data, the simulation results of Poly‐NARX, DNN‐NARX, and multiple‐layer‐perception‐NARX models are compared. It is observed that gray‐box DNN‐NARX model shows the best performance with advantages of higher global applicability, better approximation ability, and stronger generalization ability. Proposed model bank is of high expansibility and engineering applicability for soft sensing problems in the petroleum industry, laying the ground work for building smart oil and gas field.