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

P. Olapade, Bin Dai, C. M. Jones, Mehdi Alipour Kallehbasti
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Abstract

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.
利用光学传感器数据和状态方程的主成分渐近线实时估计洁净油藏流体气泡点和其他性质
由于井内泥浆柱压力过平衡,泥浆滤液侵入井附近。油基泥浆(OBM)与水基泥浆(WBM)不同,油基泥浆与储层流体可混溶,油基泥浆污染会改变原始地层流体的性质。污染流体的气泡点通常低于清洁流体。当流体被泵出地层时,地层变得更清洁,气泡点增加;增加的上限是清洁地层流体。虽然提高泵送速率可以缩短清理时间,但低于泡点的泵送会改变流体的相行为,导致地层流体中的沥青质含量析出,传感器数据变得不稳定且有噪声。因此,重要的是不要在气泡点以下泵送,实时了解清洁液的气泡点可以为现场工程师提供指导。流体取样的目的是收集具有代表性的地层流体样品,具有可接受的低污染。清洁液的气泡点提供了泵送压力的下限,这有助于确保泵送不低于气泡点,并且样品处于单相。本文描述了如何从主成分分析(PCA)重建分数的渐近线确定清洁流体成分,然后将其作为状态方程(EOS)程序的输入,以计算气泡点和气油比(GOR)等流体性质。首先对光学流体分析仪的光谱数据进行消噪,然后利用鲁棒普通最小二乘回归(ROLSR)和鲁棒主成分分析(RPCA)去除消噪数据中的异常值。在去除异常值后,使用渐近PCA评分和PCA加载重建清洁流体光谱数据。利用神经网络模型,从重建的流体光谱数据中确定清洁流体成分,并将流体成分作为EOS程序的输入,以确定流体性质。结果证实,泵出几十升液后,洁净液的气泡点和GOR没有明显变化。对第一主成分(PC1)的分析证实,大多数变化发生在泵出的前几十升,表明预测的清洁流体成分和性质在一定程度上是稳定的。这种方法可以帮助确定清洁流体的性质,即使在取样前进行泵送,也有助于确保单相流体样品。当泵出累积体积达到40 ~ 50l时,在泵出污染流体的15 ~ 20min内,可以估计出清洁流体的成分和性质,并用于确定储层的连续性。此外,了解干净的储层GOR和API重力可以帮助实时确定储层流体的类型。
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