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Real-time fluid contamination prediction using bilinear programming

机译:使用双线性编程进行实时流体污染预测

摘要

A wellbore fluid comprises unknown fractions of a formation fluid and a filtrate fluid, each of unknown composition. Method 200 comprises receiving a sequence of measurements of a wellbore fluid (step 210), obtaining constraints on a sequence of filtrate fractions (230), performing a constrained bilinear optimisation (240) that minimises total measurement error over the measurement sequence subject to obtained constraints, computing minimum and maximum optimal values for the filtrate fraction (250), and providing these to a well operator (260). Measurements may be spectroscopic measurements of the filtrate and/or formation fluid, density measurements or refractive index measurements. Spectra may lie in a linear polyhedral space. Constraints may be based on initial conditions, or be temporal, and may be linear or bilinear. Advantageously, predicting fluid formation fluid purity in real time allows the operator to guide the physical sampling process, eg by forecasting a time before filtrate fraction drops below a threshold.
机译:井筒流体包括未知组分的地层流体和滤液流体,每个组分未知。方法200包括接收井眼流体的一系列测量值(步骤210),获得对一系列滤液级分的约束(230),执行约束双线性优化(240),该约束双线性优化使在整个测量序列上总的测量误差在受到所获得约束的情况下最小化,计算滤液级分的最小和最大最佳值(250),并将其提供给井操作员(260)。测量可以是滤液和/或地层流体的光谱测量,密度测量或折射率测量。光谱可能位于线性多面体空间中。约束可以基于初始条件,也可以是时间约束,并且可以是线性或双线性的。有利地,实时地预测流体地层流体的纯度允许操作者例如通过预测滤液分数下降到阈值以下之前的时间来指导物理采样过程。

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