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A Practical Method of Predicting Chemical Scale Formation in Well Completions

机译:一种预测整井化学规模形成的实用方法

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Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent well completion in which the interval control valve (ICV) positions must be changed in order to control flow rate; the completion will become ineffective, if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed. This paper describes this method, which allows prediction of tool performance under scale-forming conditions for downhole applications. This semi-empirical method uses chemical data and flow fields generated by computational fluid dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons using brines matching the chemistry of connate fluids. Tests in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150°C) to simulate downhole well conditions have been conducted. Two test sets each with fluid at rest and where an impeller generates low velocity in the reaction vessel were conducted, ranging from 4 hours to 4 days with scaling rates determined from coupon weight gain. Concentrations in the range of 50% to 125% of the typical connate fluid concentration were used. The laboratory test data are used with velocity field data to develop an artificial-intelligence-based mathematical model to determine scale formation rates. The model can be applied to any tool geometry as long as the operating conditions are within allowable limits of the model. The model also provides some insight into the mechanism of scale formation. To verify accuracy, scale formation in a 4.5-inch interval control valve was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions reasonably well, enabling Petrobras to design a better completion and fluid-handling system for a pre-salt well.
机译:井下管状流动段的鳞片形成可能导致部分地完成堵塞,这将对产生或注射率产生不利影响。在智能井完成中,必须改变间隔控制阀(ICV)位置以控制流速;如果间隙堵塞防止阀致动,则完成将变得无效。为了减轻这些问题,已经开发了一种预测现实条件下规模形成潜在速度的方法。本文介绍了这种方法,它允许在井下应用的尺度形成条件下预测工具性能。该半经验方法使用由计算流体动力学(CFD)模型产生的化学数据和流场用于井下工具。使用野生匹配生成流体化学的盐水来获得化学数据。在高压(100至10,000psi)和高温(75至150℃)的高压,耐腐蚀容器中的测试已经进行了模拟井下井条件。两种测试在休息时具有流体,并且在叶轮产生反应容器中产生低速的情况下,从4小时至4天,通过优惠率重量增益确定的缩放速率。使用50%至125%的典型生成流体浓度范围内的浓度。实验室测试数据与速度场数据一起使用,以开发基于人工智能的数学模型,以确定规模形成速率。只要操作条件在模型的允许极限范围内,该模型可以应用于任何刀具几何体。该模型还提供了一些对规模形成机制的洞察。为了验证精度,在高压,高温条件下预测4.5英寸间隔控制阀中的刻度形成。阀门上的实验室测试与模型预测合理相结合,使Petrobras能够为预盐井设计更好的完成和流体处理系统。

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