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Bias in Rate-Transient Analysis Methods: Shale Gas Wells

机译:速率分析方法中的偏差:页岩气井

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Superposition-time functions offer an effective way for handling variable-rate data. However, these functions can also be biased and misleading. The superposition approach may generally be more useful for well-test analysis (constant rate solutions) than rate-transient analysis. Calculated data points do not tend to be sequential with superposition time but do tend to fall on a straight line corresponding to the superposition function chosen. Examples of superposition are logarithm of time (infinite acting vertical wells) and material balance time (boundary dominated flow). Production data from shale gas wells are usually subjected to operating issues that yield noises and outliers. When the rate data are noisy or contain outliers, distinguishing their effects from common regime will be difficult if the superposition time functions are used as a plotting time function on log-log plots. The superposition function may then lead to a log-log plot that has erroneous straight-line segments. A simple technique is presented to rapidly check whether or not there is data bias on the superposition-time specialized plots. The technique is based on evaluating the superposition time function of each flow regime for the maximum production time. Whatever data are beyond the maximum production time (MPT) are considered as biased data and depend on the superposition function chosen. A workflow involving different diagnostic and filtering techniques is proposed. Different synthetic examples and field examples are used in this study. Once all the problematic issues were detected and filtered out, it was clear that superposition time data beyond the MPT is biased and should be ignored. Thus, the proposed MPT technique can be relied on to detect and filter out biased data points on superposition-time log-log plots. Both raw and filtered data were analyzed using type-curve matching of linear-flow typecurves developed by Wattenbarger et al. (1998) for calculating the original gas in place (OGIP). It has been found that biased data yield a noticeable reduction in OGIP. Such reduction is attributed to the early fictitious onset of boundary dominated flow.
机译:SuperThion-Time函数为处理可变速率数据提供有效的方法。但是,这些功能也可以偏见和误导。叠加方法通常对比速率瞬态分析更良好的测试分析(恒定率解决方案)更有用。计算的数据点不倾向于与叠加时间顺序顺序,但是倾向于落在与所选择的叠加功能对应的直线上。叠加的例子是时间对数(无限作用垂直孔)和材料平衡时间(边界主导流量)。来自页岩气井的生产数据通常受到产生噪声和异常值的运营问题。当速率数据嘈杂或包含异常值时,如果叠加时间函数用作日志绘图绘图的绘图时间函数,则困难地区分其从公共区域的效果。然后,叠加功能可以导致具有错误的直线段的日志日志图。提出了一种简单的技术,以便快速检查超级定位时间专用图是否存在数据偏差。该技术基于评估每个流动制度的叠加时间函数,以获得最大的生产时间。无论数据超出最大生产时间(MPT)都被视为偏置数据,取决于所选的叠加功能。提出了一种涉及不同诊断和过滤技术的工作流程。本研究使用不同的合成实例和现场实施例。一旦检测到并过滤出所有有问题的问题,就清楚的是,超越MPT的叠加时间数据被偏见并且应该被忽略。因此,可以依赖于所提出的MPT技术来检测和滤除叠加时间记录日志图上的偏置数据点。使用WattenBarger等人开发的线性流动类型的类型曲线匹配来分析原始和过滤的数据。 (1998)用于计算原始气体(OGIP)。已经发现,偏置数据产生显着的ogip减少。这种减少归因于边界主导流的早期虚拟发作。

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