Selecting and Modifying Multiphase Correlations for Gas-Lift Wells Using Machine Learning Algorithms

Roman Gorbachev, A. Gubaev, A. Lubnin, A. Chorny, V. Kurbanov
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Abstract

In the conditions of the development of the oil fields of the Cuu Long Basin in the continental shelf of the Republic of Vietnam, in the absence of downhole gauge systems, the urgent task is improving the accuracy of the calculation of bottomhole pressure in the producing wells based on the operation modes and construction. The aim of the paper is to create tools for selecting and modifying the correlation of multiphase flow most suitable for the development of a particular group of fields, as well as to develop a tool to implement effective management of the modes of operation of gas-lift wells by choosing the optimal gaslift injection rate. Based on data from 814 instrumental measurements in wells with different construction, liquid flow rate, watercut, GOR and gaslift injection, the calculation of bottom hole pressures was made. The calculated and actual bottomhole pressures were compared with five correlations of multiphase flow, the most suitable correlations were determined and modified, including using machine learning methods, which helped to significantly improve the convergence of calculated and actual bottomhole pressures. On the basis of the newly modified correlation, a calculation of bottom hole pressure (BHP) in each production well was made, the calculation of the change in bottomhole pressure when changing the operating modes of wells has been implemented. For the field group of the Cuu Long Basin, it was revealed that with the increase in watercut in the producing wells significantly reduces the efficiency of the gas-lift method of operation. This effect is not reflected in the widespread correlations of multiphase flow, which does not allow to use the results of calculations without making additional edits. A way to adapt the calculation values to instrumental measurements has been implemented, one of the known correlations has been modified and used in the forecast of changes in bottomhole pressure after changes in operating modes of wells throughout the well stock.
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利用机器学习算法选择和修改气举井的多相关联
在越南共和国大陆架Cuu Long盆地油田开发的条件下,在没有井下测量系统的情况下,根据作业方式和施工情况,提高生产井井底压力计算的准确性是当务之急。本文的目的是创建一种工具,用于选择和修改最适合特定油田开发的多相流相关性,以及开发一种工具,通过选择最佳气举注入速度来实现气举井操作模式的有效管理。根据814口不同构造、不同液流量、不同含水、不同GOR、不同气举注水井的仪器测量数据,计算了井底压力。将计算出的井底压力与实际井底压力与5种多相流相关关系进行对比,确定并修正最合适的相关关系,包括使用机器学习方法,这有助于显著提高计算和实际井底压力的收敛性。在此基础上,计算了每口生产井的井底压力(BHP),并计算了不同作业方式下井底压力的变化。对于错龙盆地油田组,随着生产井含水率的增加,气举作业效率显著降低。这种影响没有反映在多相流的广泛相关性中,这使得不进行额外编辑就不能使用计算结果。一种使计算值适应仪器测量的方法已经实现,其中一个已知的相关性已经被修改,并用于预测井底压力在整个井群的操作模式变化后的变化。
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