Dykstra-Parsons注水计算方法的计算机实现

P. Lekia
{"title":"Dykstra-Parsons注水计算方法的计算机实现","authors":"P. Lekia","doi":"10.2118/207151-ms","DOIUrl":null,"url":null,"abstract":"\n One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance.\n The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement.\n This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry.\n The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Computer Implementation of the Dykstra-Parsons Method of Waterflood Calculation\",\"authors\":\"P. Lekia\",\"doi\":\"10.2118/207151-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance.\\n The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement.\\n This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry.\\n The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.\",\"PeriodicalId\":10899,\"journal\":{\"name\":\"Day 2 Tue, August 03, 2021\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Tue, August 03, 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/207151-ms\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, August 03, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/207151-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

石油工业面临的挑战之一是实现油藏的最大采收率。储层的自然能量,在大多数情况下,一次采收率不超过20%。为了提高采收率,采用了二次采收率技术。采用水驱等二次采收率技术,可实现15%至25%的增量采收率。目前已经发展了几种预测注水动态的理论和方法。Dykstra-Parson技术是这些方法中应用最广泛的。作者开发了一个离散的解析解,根据该解可以确定垂向覆盖面积、水油比、累计产油量、累计产油量和注入水量以及注入所需时间。Reznik等人扩展了Dykstra和Parson的工作,包括精确的、解析的、连续的解,具有时间、恒定注射压力、恒定总注射速率条件、属性时间、实际或过程时间的显式解,并假设活塞式位移。这项工作提出了一个计算机实现来比较Dykstra和Parson方法的结果,以及Reznik等人的扩展。使用Python 3为这两种方法开发了一个用户友好的图形用户界面可执行应用程序。该应用程序使用python matplotlib模块和Pandastable为图形和表格提供交互式GUI输出。GUI是用Tkinter构建的,并使用Pyinstaller和Nullsoft Scriptable Install System转换为可执行的桌面应用程序,作为石油工程师和行业的动手工具。两种方法的程序计算结果与Flow(开放多孔介质)的模拟结果非常接近。结果对这些方法的基本原理和应用提供了更多的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Computer Implementation of the Dykstra-Parsons Method of Waterflood Calculation
One of the challenges of the petroleum industry is achieving maximum recovery from oil reservoirs. The natural energy of the reservoir, primary recoveries in most cases do not exceed 20%. To improve recovery, secondary recovery techniques are employed. With secondary recovery techniques such as waterflooding, an incremental recovery ranging from 15 to 25% can be achieved. Several theories and methods have been developed for predicting waterflood performance. The Dykstra-Parson technique stands as the most widely used of these methods. The authors developed a discrete, analytical solution from which the vertical coverage, water-oil ratio, cumulative oil produced, cumulative water produced and injected, and the time required for injection was determined. Reznik et al extended the work of Dykstra and Parson to include exact, analytical, continuous solutions, with explicit solutions for time, constant injection pressure, and constant overall injection rate conditions, property time, real or process time, with the assumption of piston-like displacement. This work presents a computer implementation to compare the results of the Dykstra and Parson method, and the Reznik et al extension. A user-friendly graphical user interface executable application has been developed for both methods using Python 3. The application provides an interactive GUI output for graphs and tables with the python matplotlib module, and Pandastable. The GUI was built with Tkinter and converted to an executable desktop application using Pyinstaller and the Nullsoft Scriptable Install System, to serve as a hands-on tool for petroleum engineers and the industry. The results of the program for both methods gave a close match with that obtained from the simulation performed with Flow (Open Porous Media). The results provided more insight into the underlying principles and applications of the methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Production and Performance Evaluation of Biodetergents as an Alternative to Conventional Drilling Detergent Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction The Limitation of Reservoir Saturation Logging Tool in a Case of a Deeper Reservoir Flow into a Shallower Reservoir Within the Same Wellbore Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance Understanding the Impacts of Backpressure & Risk Analysis of Different Gas Hydrate Blockage Scenarios on the Integrity of Subsea Flowlines
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1