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Estimating Clean Reservoir Fluid Bubblepoint and Other Properties in Real Time Using PCA Asymptote of Optical Sensor Data and Equation of State

机译:使用PCA渐近光学传感器数据及状态方程实时估算清洁储层流体泡点和其他性质

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Mud filtrate invasion occurs in the immediate vicinity of the well as a result of the overbalance pressure of the mud column in the well.Oil-based muds(OBM),unlike water-based muds(WBM),are miscible with reservoir fluid,and OBM contamination alters the properties of the original formation fluid.The bubblepoint of contaminated fluid is usually lower than clean fluid.While fluid is pumped out of the formation,it becomes cleaner and the bubblepoint increases; the upper limit of the increase is the clean formation fluid.While increasing the pumping rate can shorten cleanup time,pumping below the bubblepoint can modify the fluid phase behavior and cause asphaltene content in the formation fluid to precipitate out and sensor data to become erratic and noisy.Therefore,it is important not to pump below the bubblepoint,knowing the clean fluid bubblepoint in real time provides a guideline for the field engineer.The purpose of fluid sampling is to collect a representative formation fluid-samples with an acceptably low contamination.The clean fluid bubblepoint provides a lower limit on pumping pressure,which helps ensure pumping does not go below the bubblepoint and the sample is in single phase.This paper describes how clean fluid compositions are determined from the asymptote of the principal component analysis(PCA)reconstructed scores and then used as input for the equation of state(EOS)program to compute fluid properties such as bubblepoint and gas/oil ratio(GOR).The optical spectral data from the optical fluid analyzer is first despiked,and outliers from the despiked data are removed using the robust ordinary least squares regression(ROLSR)method and robust PCA(RPCA).After removing outliers,clean fluid spectra data are reconstructed using asymptotic PCA scores and PCA loadings.Using a neural network model,clean fluid compositions are determined from reconstructed fluid spectral data,and fluid compositions are used as input for the EOS program to determine fluid properties.Results confirm that the clean fluid bubblepoint and GOR do not change significantly after a few tens of liters of fluid pumpout.Analysis of the first principal component(PC1)confirms that most of the variations occur during the first few tens of liters of pumpout,indicating the predicted clean fluid compositions and properties are somewhat stable.This approach can help determine the clean fluid properties,even while pumping before taking the sample,helping ensure a monophasic fluid sample.When pumpout accumulated volume reaches 40 to 50 L-within 15 to 20 min of pumping out contaminated fluid-clean fluid compositions and properties can be estimated and used to determine reservoir continuity.Additionally,knowing the clean reservoir GOR and API gravity can help determine the type of reservoir fluid in real time.
机译:泥滤液入侵在井中的泥浆柱的过分稳定压力的直接附近发生在井里的泥浆(OBM)中,与水基泥浆(WBM)不同,与储层流体混溶, OBM污染改变了原始地层流体的性质。受污染的流体的泡泡点通常低于清洁的流体。液体被泵出了地层,它变得更加清洁,泡泡点增加;增加的上限是清洁地层流体。增加泵送速率可以缩短清理时间,在泡沫点下方泵送可以改变流体相行为并导致地层流体中的沥青质含量沉淀出来,传感器数据变得不稳定和传感器数据因此,重要的是不要在泡泡点下方泵出来,知道清洁流体泡泡实时,为现场工程师提供了指导。流体采样的目的是收集具有可接受的低污染的代表性地层液相。清洁流体泡点为泵送压力提供较低限位,这有助于确保泵送不能低于泡泡点,并且样品处于单相中。本文描述了如何从主成分分析(PCA)的渐变中确定清洁的流体组合物重建分数,然后用作状态(EOS)程序方程的输入,以计算流体性质,例如泡泡点和气体/油比(GOR)。从光学流体分析仪中的光谱数据首先被敏锐地消失,并且使用较强的普通最小二乘回归(ROLSR)方法和鲁棒PCA(RPCA)来拆除来自消除数据的异常值。移除异常值,使用渐近PCA分数和PCA载荷重建清洁流体光谱数据。用于神经网络模型,清洁流体组合物由重建的流体光谱数据确定,并且流体组合物用作EOS程序的输入,以确定流体特性。结果确认在几十升流体泵浦之后,清洁流体泡泡点和GOR不会显着变化。第一主成分(PC1)的分析证实,大多数变化发生在前几十几十升的泵浦中,表明预测清洁的流体组成和性质有些稳定。此方法可以有助于确定清洁的流体性质,即使在采用样品之前泵送,有助于帮助确保泵出泵出累积的体积达到40至50升40至20分钟的泵送污染的流体清洁流体组合物和特性以内,可以估算并用于确定储层连续性。加工,了解清洁水库GOR和API重力可以帮助实时确定储层流体的类型。

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