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Automatic Isotherm Derivation from Field Data for Oilfield Scale Inhibitor Squeeze Treatments

机译:油田规模抑制剂挤压治疗现场数据的自动等温衍生

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Oilfield scale formation represents a very significant flow assurance challenge to the oil and gas industry, with increasing water production worldwide and higher oil prices. Scale Inhibitor (SI) squeeze treatment is the most widespread method to combat downhole scaling. In order to predict SI squeeze treatments accurately for further optimisation, it is necessary to simulate the SI retention in the formation, which may be described by pseudo-adsorption isotherms. While these are often derived from core flood experiments, sometimes they are not appropriate for modelling well treatments because the core tests on which they are based cannot fully represent field scale processes. In practice, the parameters of an analytic form of the isotherm equation are modified by trial and error by an experienced practitioner until a match is obtained between the prediction and the return profile of the first treatment in the field. The main purpose of this paper is to present a Stochastic Hill Climbing Algorithm for automatic isotherm derivation. The performance of the algorithm was evaluated using data from three field cases. Two success criteria were defined: firstly, ability to match a single historical treatment and secondly, ability to predict subsequent successive treatments. To test for the second criterion, a candidate isotherm was derived from the first treatment in a well that was treated with the same chemical package on consecutive occasions, and then the predictions using the suggested solution were compared with the observed scale inhibitor concentration return profiles from the subsequent treatments. In all the calculations, performance of the isotherms suggested by the Hill Climbing algorithm and isotherms derived by trial and error were compared. The results demonstrate that the Hill Climber Algorithm is a very effective technique for deriving an isotherm to enable accurate modelling of scale inhibitor squeeze treatments.
机译:油田规模形成是对石油和天然气工业的重大保证挑战,越来越多的水产产量和更高的油价。抑制剂(Si)挤压处理是打击井下缩放的最广泛的方法。为了预测SI挤压处理,以进一步优化,有必要模拟形成中的Si保留,这可以通过伪吸附等温物描述。虽然这些通常来自核心洪水实验,但有时它们是不适合建模井处理,因为它们基于所属的核心测试不能完全代表现场比例过程。在实践中,通过经历的从业者的试验和误差来修改等温方程的分析形式的参数,直到在该领域的第一次处理的预测和返回轮廓之间获得匹配。本文的主要目的是提出一种用于自动等温衍生的随机山攀爬算法。使用来自三个现场情况的数据进行评估算法的性能。定义了两项成功标准:首先,能够匹配单一的历史治疗,其次是预测后续连续治疗的能力。为了测试第二标准,候选等温衍生自孔中的第一次处理,其在连续场合用相同的化学包装处理,然后将使用所提出的溶液的预测与观察到的规模抑制剂浓度回报曲线进行比较随后的治疗方法。在所有计算中,比较了由山坡攀爬算法和等温机构所建议的等温的性能和通过试验和错误所获得的等温。结果表明,山地登山者算法是一种非常有效的技术,用于导出等温线,以实现规模抑制剂挤压处理的准确建模。

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