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Closed-loop feedback control of smart wells for production optimisation using self-potential measurements

机译:智能井的闭环反馈控制,用于使用自电位测量进行生产优化

摘要

Closed-loop ‘reactive’ feedback control techniques used for smart well optimisation, triggered by changes in flow (such as unwanted water production) measured at the well can increase the net present value (NPV) and mitigate reservoir uncertainty, as opposed to model-based control strategies, which use models that are rarely predictive at their spatial and temporal scales required to identify optimum control actions. However, the drawback faced with closed-loop ‘reactive’ feedback control is that control actions are only taken after adverse changes of flow occur at the well. We present a modified close-loop ‘proactive’ feedback inflow control approach based on near-well, downhole measurements of self-potential (SP) and quantify the potential benefit of this approach in different well and reservoir settings during waterflooding or aquifer support production. udThe measurement of SP signals downhole in production wells is an encouraging technique that can be used to image waterfronts and has the potential of detecting water encroachment tens to hundreds of meters away from the well. SP signals arise in order to preserve electrical neutrality when charge separation arises due to gradients in pressure, temperature and chemical concentration of the reservoir brine phase. These gradient effects are commonly encountered during waterflooding processes and can be assessed numerically to predict the SP generated downhole in oil production wells. The numerical modelling of SP can be used as a cheap alternative to carrying out actual field experiments and serve as a proxy for predicting the SP measurements taken during waterflood production. Hence, a closed-loop ‘proactive’ feedback control strategy triggered by downhole SP measurements is developed.udWe use the NPV of the production wells to measure and compare the performance of the closed-loop feedback control in two different synthetic production cases; the first production case is a simple thin oil-column reservoir with production enabled by a single long horizontal well, and the second more realistic SPE Brugge field model, with production enabled by 20 production wells. The results observed are promising, and suggest that closed-loop control on the basis performance of downhole SP feedback can yield increased gains in NPV, by delaying the production of unwanted fluids compared with water-cut monitoring. These gains are also observed even if the reservoir lies outside the range predicted by reservoir models. Finally, we investigate the potential utility of SP monitoring in analogue real field applications. Overall the results are promising and suggest that SP measurements can be useful in making critical decisions in real field exploration and production applications, and other non-oil related fields such as saline intrusion monitoring in coastal aquifers.
机译:与模型模型相反,用于智能井优化的闭环“反应”反馈控制技术由井中测量的流量变化(例如多余的水产生)触发,可以增加净现值(NPV)并减轻储层不确定性。基于控制的策略,该策略使用的模型在识别最佳控制动作所需的时空尺度上很少具有预测性。但是,闭环“反应性”反馈控制面临的缺点是,只有在井眼流量发生不利变化后才采取控制措施。我们提出了一种基于自井(SP)近井,井下测量的改进的闭环“主动”反馈流入控制方法,并量化了该方法在注水或含水层支持生产过程中在不同井和储层中的潜在效益。 ud在生产井的井下测量SP信号是一种令人鼓舞的技术,可用于对滨水区进行成像,并有潜力检测距井数十至数百米的水侵。当由于储层盐水相的压力,温度和化学浓度的梯度而发生电荷分离时,出现SP信号是为了保持电中性。这些梯度效应通常在注水过程中会遇到,可以通过数字评估以预测石油生产井中井下SP的产生。 SP的数值模型可以用作进行实际田间试验的廉价替代方案,并且可以用作预测注水生产过程中进行的SP测量的替代方法。因此,开发了一种由井下SP测量触发的闭环“主动”反馈控制策略。 ud我们使用生产井的NPV来测量和比较两种不同的合成生产案例中闭环反馈控制的性能;第一个生产案例是一个简单的薄油塔油层,其中一个单水平长井就可以生产,第二个更现实的SPE Brugge油田模型可以生产20个生产井。观察到的结果是有希望的,并且表明与井下监控相比,基于井下SP反馈的闭环控制可以通过延迟多余流体的产生来增加NPV的收益。即使油藏在油藏模型预测的范围之外,也可以观察到这些收益。最后,我们研究了SP监视在​​模拟实际应用中的潜在效用。总体而言,结果令人鼓舞,并表明SP测量可用于在实地勘探和生产应用以及其他与石油无关的领域(如沿海含水层中的盐水入侵监测)中做出关键决策。

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    Ijioma Amadi;

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  • 年度 2016
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