基于大规模计算科学的非常规井完井设计优化

D. Cotrell, T. Hoeink, Elijah Odusina, Sachin Ghorpade, S. Stolyarov
{"title":"基于大规模计算科学的非常规井完井设计优化","authors":"D. Cotrell, T. Hoeink, Elijah Odusina, Sachin Ghorpade, S. Stolyarov","doi":"10.2118/196980-ms","DOIUrl":null,"url":null,"abstract":"In the current state of the oil and gas industry, unconventional resources are a significant source of the total production output. Unconventional wells remain profitable at various price points, because initial stimulation treatments can be tailored to changing market conditions, reflecting completion costs and (estimated) hydrocarbon prices. The same holds true for re-stimulation of already producing wells. Stimulation treatment \"opens\" up the subsurface to ultimately allow for better drainage of the reservoir hydrocarbons. The primary stimulation treatment currently in use is hydraulic fracturing, in which the wellbore is broken up into multiple stages, and highly pressurized fluid (oftentimes water) is pumped into each stage of the wellbore. This causes fractures to propagate away from the wellbore, which in turn enhances the local reservoir permeability and allows for economical production. Historically, the number of stages, and clusters per stage, for hydraulic stimulation has been based on wellbore horizontal length (e.g., 200 ft or 400 ft), or much valued previous experience in the same or similar area, as well as other investment considerations. Over time, a strong tendency has developed to place stages and clusters closer together to improve production. However, it is reasonable to assume that there will be a point beyond which adding another stage becomes more expensive than what is gained by increased production revenue from the greater stage count (i.e., less profitable depending on the time of investment). This scenario frames a classic optimization problem which is solved using Monte Carlo methods. Results show that optimal stimulation treatment configurations are robust for many objective functions related to the fracturing process (e.g., propped length and propped height). However, we find that objective functions related to production, production revenue, and profit often provide different optimum treatment configurations, and that those optima shift with respect to the considered timeframe. Because business decisions will ultimately be based on profit decisions over a given time span, we propose utilizing the appropriate objective function together with an integrated modeling approach such as presented here.","PeriodicalId":10977,"journal":{"name":"Day 2 Wed, October 23, 2019","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Completion Design Optimization for Unconventional Wells Using Large Scale Computational Science\",\"authors\":\"D. Cotrell, T. Hoeink, Elijah Odusina, Sachin Ghorpade, S. Stolyarov\",\"doi\":\"10.2118/196980-ms\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the current state of the oil and gas industry, unconventional resources are a significant source of the total production output. Unconventional wells remain profitable at various price points, because initial stimulation treatments can be tailored to changing market conditions, reflecting completion costs and (estimated) hydrocarbon prices. The same holds true for re-stimulation of already producing wells. Stimulation treatment \\\"opens\\\" up the subsurface to ultimately allow for better drainage of the reservoir hydrocarbons. The primary stimulation treatment currently in use is hydraulic fracturing, in which the wellbore is broken up into multiple stages, and highly pressurized fluid (oftentimes water) is pumped into each stage of the wellbore. This causes fractures to propagate away from the wellbore, which in turn enhances the local reservoir permeability and allows for economical production. Historically, the number of stages, and clusters per stage, for hydraulic stimulation has been based on wellbore horizontal length (e.g., 200 ft or 400 ft), or much valued previous experience in the same or similar area, as well as other investment considerations. Over time, a strong tendency has developed to place stages and clusters closer together to improve production. However, it is reasonable to assume that there will be a point beyond which adding another stage becomes more expensive than what is gained by increased production revenue from the greater stage count (i.e., less profitable depending on the time of investment). This scenario frames a classic optimization problem which is solved using Monte Carlo methods. Results show that optimal stimulation treatment configurations are robust for many objective functions related to the fracturing process (e.g., propped length and propped height). However, we find that objective functions related to production, production revenue, and profit often provide different optimum treatment configurations, and that those optima shift with respect to the considered timeframe. Because business decisions will ultimately be based on profit decisions over a given time span, we propose utilizing the appropriate objective function together with an integrated modeling approach such as presented here.\",\"PeriodicalId\":10977,\"journal\":{\"name\":\"Day 2 Wed, October 23, 2019\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 2 Wed, October 23, 2019\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2118/196980-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 Wed, October 23, 2019","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2118/196980-ms","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在目前的石油和天然气行业中,非常规资源是总产量的重要来源。非常规井在不同的价格点上仍然有利可图,因为初始增产措施可以根据不断变化的市场条件进行调整,反映完井成本和(估计的)碳氢化合物价格。对于已经生产的井进行再增产也是如此。增产措施“打开”了地下,最终可以更好地排出储层中的油气。目前使用的主要增产措施是水力压裂,将井筒分成多个压裂段,然后将高压流体(通常是水)泵入井筒的每个压裂段。这使得裂缝向井筒外扩展,从而提高了局部储层的渗透率,从而实现了经济生产。从历史上看,水力增产的级数和每级压裂簇的数量是基于井筒水平长度(例如200英尺或400英尺),或在相同或类似区域的宝贵经验,以及其他投资考虑因素。随着时间的推移,为了提高产量,井段和井簇之间的距离越来越近。然而,我们可以合理地假设,在某个点上,增加另一个压裂段的成本会高于增加压裂段数量所带来的产量收益(即,根据投资时间的不同,利润会减少)。这个场景构造了一个经典的优化问题,用蒙特卡罗方法求解。结果表明,对于与压裂过程相关的许多目标函数(例如支撑长度和支撑高度),最佳增产处理配置具有鲁棒性。然而,我们发现与产量、生产收入和利润相关的目标函数通常会提供不同的最佳处理配置,并且这些最佳配置会随着所考虑的时间框架而变化。由于业务决策最终将基于给定时间范围内的利润决策,因此我们建议将适当的目标函数与集成的建模方法(如本文所示)结合使用。
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
Completion Design Optimization for Unconventional Wells Using Large Scale Computational Science
In the current state of the oil and gas industry, unconventional resources are a significant source of the total production output. Unconventional wells remain profitable at various price points, because initial stimulation treatments can be tailored to changing market conditions, reflecting completion costs and (estimated) hydrocarbon prices. The same holds true for re-stimulation of already producing wells. Stimulation treatment "opens" up the subsurface to ultimately allow for better drainage of the reservoir hydrocarbons. The primary stimulation treatment currently in use is hydraulic fracturing, in which the wellbore is broken up into multiple stages, and highly pressurized fluid (oftentimes water) is pumped into each stage of the wellbore. This causes fractures to propagate away from the wellbore, which in turn enhances the local reservoir permeability and allows for economical production. Historically, the number of stages, and clusters per stage, for hydraulic stimulation has been based on wellbore horizontal length (e.g., 200 ft or 400 ft), or much valued previous experience in the same or similar area, as well as other investment considerations. Over time, a strong tendency has developed to place stages and clusters closer together to improve production. However, it is reasonable to assume that there will be a point beyond which adding another stage becomes more expensive than what is gained by increased production revenue from the greater stage count (i.e., less profitable depending on the time of investment). This scenario frames a classic optimization problem which is solved using Monte Carlo methods. Results show that optimal stimulation treatment configurations are robust for many objective functions related to the fracturing process (e.g., propped length and propped height). However, we find that objective functions related to production, production revenue, and profit often provide different optimum treatment configurations, and that those optima shift with respect to the considered timeframe. Because business decisions will ultimately be based on profit decisions over a given time span, we propose utilizing the appropriate objective function together with an integrated modeling approach such as presented here.
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
Practical Application of the Fluorescent Microspheres Method Technology in Horizontal Wells of the Upper Salym Oil Field: Efficiency of the Method, Technology and Approach Evolution of Horizontal Wells Production Logging Using Markers Successful Implementation of Managed Pressure Drilling Technology Under the Conditions of Catastrophic Mud Losses in the Kuyumbinskoe Field Completion Design Optimization for Unconventional Wells Using Large Scale Computational Science Assessing Efficiency of Multiwell Retrospective Testing MRT in Analysis of Cross-Well Interference and Prediction of Formation and Bottom- Hole Pressure Dynamics
×
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