基于机器学习的盐阳市油田储层管理系统及ESP作业控制过程优化

A. Musorina, Grigory Sergeyevich Ishimbayev
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摘要

在目前的油气生产条件下,以成熟的生产油田为特征,重点转向生产过程的数字化和机器学习(ML)模型的使用,提高井控数据的准确性和一致性的问题变得越来越重要。因此,SPD已经成功实施了将环空压力传感器与机器学习模型相结合的项目,以控制井的环空压力,作为油田开发计划合规的一部分。在现场开发项目中,通常使用回声测深仪和遥测系统读数来控制环空压力和动态液位。然而,回声测深仪并不是作为测量仪器设计的,其读数精度较低,无法可靠地评估井的动态流动水平和环空压力,也无法实现井的最大潜力,而且用于测量泵入口压力的遥测系统可能会出错。本文描述了基于机器学习模型数据的生产井环空压力评估方法。机器学习(ML)模型是目标变量(井底压力)的函数,目标变量是根据实际数据进行预测的:静态参数(井图、泵设计)和动态参数(环空和管线压力、流量)。输入参数解释根据历史数据得到目标变量的最可能值。
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Optimization of the Reservoir Management System and the ESP Operation Control Process by Means of Machine Learning on the Oilfields of Salym Petroleum Development N.V.
Under the present conditions of oil and gas production, which are characterized by mature production fields and the focus shifted towards digitalization of production processes and use of machine learning (ML) models, the issues related to the improvement of accuracy and consistency of the well operation control data are becoming increasingly important. As a result, SPD has successfully implemented the project of using annular pressure sensors in combination with machine learning models to control the well annular pressure as part of the field development program compliance. Under the field development program, echosounder and telemetry system readings are typically used to control the annular pressure and the dynamic flowing level. Echosounders, however, are not designed as measuring instruments, the accuracy of their readings being low and making it impossible to reliably evaluate the well's dynamic flowing level and annular pressure, as well as to achieve the well's maximum potential, and the telemetry systems used to measure the pump intake pressure may go wrong. This manuscript describes the approach to the producer well annular pressure assessment based on the machine learning model data. The machine learning (ML) model is a function of the target variable (bottom-hole pressure), which is predicted on the basis of the actual data: static parameters (well schematic, pump design) and dynamic parameters (annular and line pressures, flowrate). The input parameter interpretation results in the most probable value of the target variable based on the historic data.
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