基于Fetkovich型曲线法的溶气驱气藏累积产量预测方法

Omaga Sumaila
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引用次数: 0

摘要

对于溶气驱油藏,常规的DCA方法是Arp方法。另一种改进的方法是Fetkovich型曲线方法,该方法将有限系统的速率方程和物质平衡方程结合起来,用背压指数(n)代替Arp下降指数(b),得到溶气驱油藏的速率-时间方程。然而,这种改进的方法有一些局限性。首先,很难判断哪一种类型的曲线生产数据匹配。它的分辨率较低。此外,开发的物质平衡方程的累积速率模型:pr2与Np呈线性关系,往往会给出不准确的结果。本文首先用数学方法提出了一个累积速率模型。然后,两种物质平衡方程形式的Fetkovich速率-时间关系:pr2与Np呈线性关系,PR与Np呈线性关系,通过寻找无因次速率(qDd)对无因次时间(tDd)的自然对数的导数,将其转换为线性关系(在对数-对数图中)。因此,生成了类型线,并在大约五十(50)次试验后,提出了最佳可加工性所需的条件。利用尼日尔三角洲某油藏的现场数据验证了所开发的累积速率模型。预测累积产量与实际生产数据的相关性为0.99988。因此,表明高度正相关。此外,线性化模型与堪萨斯州Arbuckle Lime的生产数据进行了验证。相关性也高达0.99988。然后,使用Excel VBA创建了一个用户友好的Microsoft Excel电子表格应用程序,用于计算给定的累积产量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decline Line Analysis DLA: A Method for Forecasting Cumulative Production for Solution-Gas Drive Reservoirs Based on Fetkovich Type Curve Approach
A conventional method for DCA for a solution-gas drive reservoir is the Arp's Approach. Another approach, an improved approach, is the Fetkovich Type Curve Approach which involves the combination of rate equation and material balance equation for finite systems to obtain rate-time equations for solution-gas drive reservoir using backpressure exponent(n) in place of Arp's decline exponent(b). This improved approach, however, has a number of limitations. First, it is difficult to judge which type curve production data match. It has a lower resolution. Also, the developed cumulative-rate model for the material balance equation form: PR2is linear with Np, tends to give inaccurate result. This paper, first, presents a cumulative-rate model using a mathematical approach. Then, the Fetkovich rate-time relationships for both the material balance equation forms: PR2is linear with Np, PR is linear with Np, are transferred into linear relationships(in a log-log plot) by finding the derivative of the natural logarithm of the dimensionless rate(qDd) with respect to the dimensionless time(tDd). Consequently, the type lines are generated and upon about fifty (50) trials, conditions required for optimum workability are presented. The developed cumulative-rate model was validated with field data from a reservoir in the Niger Delta. The correlation between forecasted cumulative production and actual production data is 0.99988. Thus, indicating high positive correlation. Also, the linearized models were validated with production data from Arbuckle Lime, Kansas. The correlation too, is as high as 0.99988. Thereafter, a user-friendly Microsoft Excel spreadsheet application for computing cumulative production given rate is created using Excel VBA.
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