{"title":"基于智能模型的天然气生产数据分析:应用天然气生产","authors":"M. Ahmadi, Zhangxin Chen","doi":"10.3997/2214-4609.201803024","DOIUrl":null,"url":null,"abstract":"Predicting the future oil and gas production rate and evaluating oil/gas reserves are very challenging issues. Many engineers have found decline curve analysis a useful approach (Ahmed, 2010; Arps, 1945; Ebrahimi, 2010; Fetkovich, 1980; Gentry, 1972; Li and Horne, 2005; Ling and He, 2012; Oghena, 2012; Shirman, 1999; Zheng and Fei, 2008). The production rate \nor cumulative production at a constant bottom-hole pressure declines with time (Ahmed, 2010). Since mechanisms affecting \nthe production are constant throughout the lifetime of a reservoir, extrapolating decline curves is used to forecast the future production rate. To do so, initial production rate, the decline curvature, and its rate should be considered (Ahmed, 2010). Arps’s equations are fundamental for the most heuristic and conventional decline curve analysis models (Arps, 1945). Arps demonstrated that the hyperbolic family of equations can express mathematically the curvature behaviour of the production rate versus time curve. The Arps (Arps, 1945) equations are divided into three categories, including exponential, hyperbolic, \nand harmonic decline curve models. Fetkovich (Fetkovich, 1980) proposed type curves for analysing decline curves. The procedure of type curve matching is summarized by the visual matching with log-log paper that includes pre-plotted curves of \nproduction data. Each of the curves has characteristics which can be shown when plotting them on Cartesian, semi-log and log-log scales as shown in Figure 1.","PeriodicalId":231338,"journal":{"name":"First EAGE/PESGB Workshop Machine Learning","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis Of Gas Production Data Via An Intelligent Model: Application Natural Gas Production\",\"authors\":\"M. Ahmadi, Zhangxin Chen\",\"doi\":\"10.3997/2214-4609.201803024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting the future oil and gas production rate and evaluating oil/gas reserves are very challenging issues. Many engineers have found decline curve analysis a useful approach (Ahmed, 2010; Arps, 1945; Ebrahimi, 2010; Fetkovich, 1980; Gentry, 1972; Li and Horne, 2005; Ling and He, 2012; Oghena, 2012; Shirman, 1999; Zheng and Fei, 2008). The production rate \\nor cumulative production at a constant bottom-hole pressure declines with time (Ahmed, 2010). Since mechanisms affecting \\nthe production are constant throughout the lifetime of a reservoir, extrapolating decline curves is used to forecast the future production rate. To do so, initial production rate, the decline curvature, and its rate should be considered (Ahmed, 2010). Arps’s equations are fundamental for the most heuristic and conventional decline curve analysis models (Arps, 1945). Arps demonstrated that the hyperbolic family of equations can express mathematically the curvature behaviour of the production rate versus time curve. The Arps (Arps, 1945) equations are divided into three categories, including exponential, hyperbolic, \\nand harmonic decline curve models. Fetkovich (Fetkovich, 1980) proposed type curves for analysing decline curves. The procedure of type curve matching is summarized by the visual matching with log-log paper that includes pre-plotted curves of \\nproduction data. Each of the curves has characteristics which can be shown when plotting them on Cartesian, semi-log and log-log scales as shown in Figure 1.\",\"PeriodicalId\":231338,\"journal\":{\"name\":\"First EAGE/PESGB Workshop Machine Learning\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First EAGE/PESGB Workshop Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3997/2214-4609.201803024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First EAGE/PESGB Workshop Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3997/2214-4609.201803024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
预测未来油气产量和评估油气储量是非常具有挑战性的问题。许多工程师发现下降曲线分析是一种有用的方法(Ahmed, 2010;Arps, 1945;Ebrahimi, 2010;Fetkovich, 1980;贵族,1972;Li and Horne, 2005;凌和何,2012;Oghena, 2012;Shirman, 1999;Zheng and Fei, 2008)。在井底压力恒定的情况下,产量或累积产量随着时间的推移而下降(Ahmed, 2010)。由于影响产量的机制在油藏的整个生命周期中是恒定的,因此可以使用外推递减曲线来预测未来的产量。为此,应考虑初始产量、下降曲率及其速率(Ahmed, 2010)。Arps的方程是大多数启发式和传统的下降曲线分析模型的基础(Arps, 1945)。Arps证明了双曲型方程族可以用数学方法表达产量随时间曲线的曲率行为。Arps (Arps, 1945)方程分为三类,包括指数型、双曲型和谐波衰退曲线模型。Fetkovich (Fetkovich, 1980)提出了用于分析衰退曲线的类型曲线。通过与生产数据预绘曲线的对数-对数纸的可视化匹配,总结了类型曲线匹配的过程。每条曲线都有特征,可以在笛卡尔、半对数和对数尺度上绘制它们,如图1所示。
Analysis Of Gas Production Data Via An Intelligent Model: Application Natural Gas Production
Predicting the future oil and gas production rate and evaluating oil/gas reserves are very challenging issues. Many engineers have found decline curve analysis a useful approach (Ahmed, 2010; Arps, 1945; Ebrahimi, 2010; Fetkovich, 1980; Gentry, 1972; Li and Horne, 2005; Ling and He, 2012; Oghena, 2012; Shirman, 1999; Zheng and Fei, 2008). The production rate
or cumulative production at a constant bottom-hole pressure declines with time (Ahmed, 2010). Since mechanisms affecting
the production are constant throughout the lifetime of a reservoir, extrapolating decline curves is used to forecast the future production rate. To do so, initial production rate, the decline curvature, and its rate should be considered (Ahmed, 2010). Arps’s equations are fundamental for the most heuristic and conventional decline curve analysis models (Arps, 1945). Arps demonstrated that the hyperbolic family of equations can express mathematically the curvature behaviour of the production rate versus time curve. The Arps (Arps, 1945) equations are divided into three categories, including exponential, hyperbolic,
and harmonic decline curve models. Fetkovich (Fetkovich, 1980) proposed type curves for analysing decline curves. The procedure of type curve matching is summarized by the visual matching with log-log paper that includes pre-plotted curves of
production data. Each of the curves has characteristics which can be shown when plotting them on Cartesian, semi-log and log-log scales as shown in Figure 1.