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DIRECT MID-IR OPTICAL MEASUREMENT OF SYNTHETIC DRILLING FLUID FILTRATE CONTAMINATION DURING FORMATION-TESTER PUMPOUTS

机译:直接中红外光学测量合成钻井液滤液滤液期间 - 测试泵泵

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摘要

Laboratory analysis of formation-tester samples provides critical information for exploration and production activities. The physical properties, chemical properties, and composition of samples are used to confirm and resolve reservoir architecture questions, including compartmentalization and compositional grading, which is then used to design completion and production strategies. The laboratory data are used to calibrate equation-of-state models employed in reservoir simulations to project the lifetime recovery of an asset under different scenarios. This yields both an optimal production strategy and reduced capital expenditure costs for a project. Furthermore, the data are used to identify flow assurance problems and to estimate the operational costs of production. Lastly, the data provide crude value, as required, to book reserves. However, these benefits are predicated on the requirement that samples be representative of the actual formation fluid. In this aspect, contamination of samples with near-wellbore drilling fluid filtrate remains the most common reason that samples are not fit-for-purpose. Therefore, contamination estimation must be improved. The ubiquitous real-time downhole contamination estimation procedure for filtrate in petroleum uses pumpout trend fitting. The trend fitting attempts to regress a model to a change in the formation tester sensor data during a pumpout in which fluid grades exist from filtrate to formation fluid. Sensor responses of the pure filtrate or formation fluid endmembers are estimated and the contamination calculated. When contamination is deemed sufficiently low, the sample is captured and shipped to a surface laboratory. Because sampling is often the last activity before cementing a section of well, there is not a second sampling opportunity if the laboratory determines the sample is not fit-for-purpose. Too often, the contamination estimate does not match the laboratory due to the breakdown of three key trend-fitting assumptions: 1) that the model is sufficient to describe reservoir complexity; 2) that the model can be extrapolated to determine endmember responses; and 3) that the asymptote of the pumpout is not falsely representing steady-state contamination. Improvement in current methods of contamination estimation are nearly all driven by improving trend fitting to existing sensor data as opposed to the development of new sensors. This work, however, describes the development of a new direct contamination sensor, designed to detect synthetic drilling fluid using optical measurements. Nearly all synthetic drilling fluids contain additives such as olefins, esters, ketones, alcohols, and amines which are not naturally present in geologic formations. These compounds look like other petroleum hydrocarbons in the conventional visible and near-infrared optical ranges of existing wireline tools. However, in the mid-infrared optical range, the signature of olefins and other synthetic filtrate additives is distinct, which has allowed for the construction of a synthetic drilling fluid (SDF) filtrate-specific detector that can be used to directly determine the contamination level of drilling fluid filtrate without the limitations of trend-fitting assumptions. A comprehensive study of eight pumpout stations from five wells has validated the performance to greater than +/- 2.5 wt%, consistently delivering results superior to those obtained via conventional trend-fitting methods.
机译:形成测试者样本的实验室分析为勘探和生产活动提供了关键信息。样品的物理性质,化学性质和组成用于确认和解决储层架构问题,包括分区化和组成分级,然后用于设计完成和生产策略。实验室数据用于校准在水库模拟中使用的状态模型,以在不同场景下投影资产的寿命恢复。这产生了最佳的生产战略和项目的资本支出费用。此外,数据用于识别流量保证问题,并估计生产的运营成本。最后,数据根据需要提供原油价值,以预订储备。然而,这些益处是对样品代表实际形成液的要求。在这方面,具有近井眼钻井液滤液的样品的污染仍然是样品不适合目的的最常见原因。因此,必须改善污染估计。石油中滤液的无处不在的实时井下污染估计程序使用泵浦趋势拟合。趋势拟合试图将模型作为在泵浦期间将模型转换为形成测试仪传感器数据的变化,其中流体等级从滤液到形成流体。估计纯滤液或形成流体终点的传感器响应并计算污染。当污染被认为足够低时,将样品捕获并运输到表面实验室。因为采样往往是最后一个活动,在巩固一部分井之前,如果实验室确定样品不适合目的,则没有第二个采样机会。通常,由于三个关键趋势假设的崩溃,污染估计与实验室不符:1)模型足以描述储层复杂性; 2)可以推断模型以确定终止的反应; 3)泵浦的渐近不是错误地表示稳态污染。目前污染估计方法的改进几乎通过改善现有传感器数据而改善现有传感器数据而导致的污染估计。然而,这项工作描述了一种新型直接污染传感器的开发,旨在使用光学测量来检测合成钻井液。几乎所有合成钻井液都含有添加剂,例如烯烃,酯,酮,醇和胺,其在地质形成中不存在。这些化合物在现有有线工具的常规可见和近红外光学范围内看起来像其他石油烃。然而,在中红外光学范围内,烯烃和其他合成滤液添加剂的特征是不同的,其允许构建合成钻井液(SDF)滤液特异性探测器,其可用于直接确定污染水平钻井液滤液而不存在趋势拟合假设的局限性。从五个井的八个泵浦站综合研究已经验证了大于+/- 2.5wt%的性能,始终如一地将优于通过常规趋势拟合方法获得的结果。

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