Short-term model-based production optimization of a surface production network with electric submersible pumps using piecewise-linear functions
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Short-term model-based production optimization of a surface production network with electric submersible pumps using piecewise-linear functions

机译:基于短期模型的生产网络的生产网络,采用电动潜水泵使用分段 - 线性函数

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AbstractThis paper describes the development details and results of a model-based production optimization scheme to advice how to set frequencies of electric submersible pumps to maximize total oil production in a surface network. Furthermore, the effect of model fidelity and modifications to enforce high ESP efficiency are studied. The particular system targeted is surface networks with ESP-lifted wells, high water cut, low API gravity and gas oil ratio where wells require regular updates to their frequencies and there are multiple operational constraints.The model employed for the optimization is a steady-state synthetic surface network with 15 wells. The optimization is formulated as a Mixed-Integer Linear Problem by approximating the network model using piecewise linear functions (tables). Well opening and ESP frequency are the two controllable variables. Monte Carlo simulations were performed varying randomly the predicted pressure drop in each pipe section within 20%. The operational envelope of the ESP was reduced to enforce high pump efficiency.For the cases tested the optimization methodology has low runtime (13?s avg.), reproduces with an acceptable accuracy (average 0.6%, maximum 5%) the original network model, it handles successfully multiple operational constraints and guarantees global optimality. Additionally, it can be easily updated to reflect depletion changes by generating new tables. Monte Carlo simulations show that model fidelity has a minimal effect in the variation of the optimal conditions found. The modifications to enforce high ESP efficiency reduce significantly the maximum oil production predicted (37%).Highlights?Model-based production optimization is applied to a surface network with ESP-boosted wells.?The optimization method consists of using piecewise linear functions generated using the black box model.?For the case tested the method has low runtime, acceptable deviation from the original black-box model.?For the case tested the model fidelity seems to have little effect on the optimal conditions.?Modifications to ensure high pump efficiency reduce significantly the maximum oil produced.]]>
机译:<![CDATA [ 抽象 本文介绍了基于模型的生产优化方案的开发细节和结果,以建议设置频率的建议电动潜水泵最大化地面网络中的总油生产。此外,研究了模型保真度和修改来强制实施高鉴率效率的影响。针对具有ESP升起的井的表面网络,高水位,低API重力和煤气油比,在井中需要定期更新,并且有多种操作约束。 优化所用模型是一个带有15个井的稳态合成表面网络。通过使用分段线性函数(表)近似网络模型,优化将优化作为混合整数线性问题。打开和ESP频率良好是两个可控变量。蒙特卡罗模拟随机地进行改变,在20%内的每个管道部分中的预测压降。 ESP的操作包络被降低以强制实施高泵效率。 对于测试的案例,优化方法具有低运行时( 13?S AVG。),以可接受的准确度再现(平均0.6%,最大5%)原始网络模型,它处理成功多个操作约束并保证全球最优性。此外,可以通过生成新表来轻松更新以反映耗尽变化。蒙特卡罗模拟表明,模型保真度在发现的最佳条件的变化中具有最小的效果。强制实施高ESP效率的修改显着降低了预测的最大油生产(37%)。 突出显示 < CE:PARA ID =“P0010”View =“全部”>基于模型的生产优化应用于具有ESP升高的井的表面网络。 优化方法包括使用使用黑匣子模型产生的分段线性函数。 对于CAS E测试该方法具有低运行时,可接受的偏离原始黑匣子型号。 对于所测试的模型保真度似乎对最佳条件影响不大。 修改,以确保高泵效率降低产生的最大油。 ]]>

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