CAJ | 학술논문

实践中，抽油井动液面都是使用回声仪测试的，无法实时在线检测。而基于示功图分析的动液面实时在线检测方法存在计算精度不高的缺陷。考虑到数据驱动软测量建模方法存在随时间推移出现的模型老化现象，采用一种增量学习动态高斯过程回归(IDGPR)软测量建模方法，实现对抽油井动液面深度的实时在线检测。首先建立基本动态高斯过程回归软测量模型，在模型投入现场运行后，通过一种增量学习算法对模型进行在线更新，使其不断适应油井工况变化，自适应获得更加准确的软测量模型。现场应用表明，该软测量模型具有较高的预测精度和较好的泛化能力，可以满足工程应用要求。
실천중，추유정동액면도시사용회성의측시적，무법실시재선검측。이기우시공도분석적동액면실시재선검측방법존재계산정도불고적결함。고필도수거구동연측량건모방법존재수시간추이출현적모형노화현상，채용일충증량학습동태고사과정회귀(IDGPR)연측량건모방법，실현대추유정동액면심도적실시재선검측。수선건립기본동태고사과정회귀연측량모형，재모형투입현장운행후，통과일충증량학습산법대모형진행재선경신，사기불단괄응유정공황변화，자괄응획득경가준학적연측량모형。현장응용표명，해연측량모형구유교고적예측정도화교호적범화능력，가이만족공정응용요구。
In practice, dynamic fluid level is traditionally measured onsite by using the acoustic method. This method, however, has its limitation in determining real-time dynamic liquid level. Determining real-time dynamic liquid level by analyzing the measured dynamometer card has poor precision. Model aging happens as time goes by with the data driven soft sensing modeling method. An incremental dynamic Gaussian process regression (IDGPR) was presented for the soft sensing modeling in order to realize real-time determination of dynamic liquid level. At the beginning a basic soft sensing model based on dynamic Gaussian process regression was established. After the model was put into application, it could be updated on-line through an incremental learning method. The model could be constantly adaptable to the change of operating condition and precisely predict dynamic liquid level. The application result in the oil field showed that the proposed soft sensing model achieved high prediction precision and good generalization ability, meeting engineering requirement.