Active reservoir management for improved hydrocarbon recovery

积极的油藏管理可提高碳氢化合物采收率

• 批准号: NE/I029846/1
• 负责人: Ian Main
• 金额: $ 1.64万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FI029846%2F1
• 关键词: Active; reservoir; management; improved; hydrocarbon

Hydrocarbon extraction for energy supply, or injection of CO2 to mitigate climate change, both require a detailed knowledge of the structure of underground reservoirs, and how they evolve in response to engineering decisions (when and where to inject or extract, how fast etc.). In this project we will carry out market research and determine user requirements prior to a separate application to develop a commercial version of a new statistical reservoir model discovered during a NERC grant as an aid to understanding and engineering reservoirs of oil and gas and underground storage sites for CO2. The new model is a way of extracting information directly from flow rate data already recorded at injector or producer wells without significantly adding to the cost of acquiring the data. It has proven successful in a number of field trials as a scientific concept, but it has been harder to prove commercial value without a version that could be run in trials by a practitioner. This project will provide a first step in developing such a platform. Here we will engage with potential end-users to make it as fit-for-purpose in its design, and extend the range of applications to a wider range of operational and commercial problems. The technology is based on establishing a multivariate regression model to forecast oil and gas production rates from past injection and production data. The technique provides a low-cost, optimised targeted search for the relevant well pairs that respond to each other, and quantifies the strength of the correlation. The scientific concept has been proven in a number of research articles and test cases, and in an independently-refereed, 'blind test' forecast of data not available to our team. In this project we will carry out market research with the questions and objectives to be addressed listed in the Objectives section above. These work elements will enable us to submit a mature follow-on fund application by informing us of the size of the market, the specific technical problems of current interest, and to design and scope the template for a new software tool to be provided to a professional software engineer in a full follow-on fund application.End users will benefit primarily from being able to condition the functionality of the tool, particularly early adopters to be identified by the market research. Ultimately the tool will add an independent constraint to current reservoir models used to optimise the extraction of oil and gas, at little extra cost compared to acquiring the data or carrying out a conventional reservoir model. The aim is to provide a user-friendly platform to enable end-users to identify geo-mechanical effects, and to use the results to constrain parameters in conventional 'black oil' simulators, thereby improving reservoir description and conventional forecasting.

为能源供应而开采碳氢化合物，或注入二氧化碳以缓解气候变化，都需要详细了解地下储层的结构，以及它们如何响应工程决策（何时何地注入或提取，多快等）。在这个项目中，我们将进行市场研究，并确定用户需求之前，一个单独的应用程序，以开发一个新的统计储层模型的商业版本，发现在NERC赠款作为一个援助，以了解和工程的石油和天然气的储层和地下储存地点的二氧化碳。新模型是一种直接从注入井或生产井威尔斯已记录的流量数据中提取信息的方法，而不会显著增加获取数据的成本。作为一个科学概念，它已经在许多实地试验中证明是成功的，但是如果没有一个可以由实践者在试验中运行的版本，就很难证明商业价值。该项目将为开发这样一个平台迈出第一步。在这里，我们将与潜在的最终用户进行合作，使其在设计中符合目的，并将应用范围扩展到更广泛的运营和商业问题。该技术是基于建立一个多元回归模型，预测石油和天然气生产率从过去的注入和生产数据。该技术为相互响应的相关井对提供了低成本、优化的目标搜索，并量化了相关性的强度。这一科学概念已经在许多研究文章和测试案例中得到了证明，并且在我们团队无法获得的独立参考数据的“盲测”预测中得到了证明。在这个项目中，我们将进行市场调查的问题和目标，以解决上述目标部分。这些工作要素将使我们能够通过告知我们市场规模、当前感兴趣的具体技术问题来提交成熟的后续基金申请，并设计和确定新软件工具的模板范围，以便在完整的后续基金申请中提供给专业软件工程师。最终用户将主要受益于能够调整工具的功能，特别是市场调查中发现的早期采用者。最终，该工具将为用于优化石油和天然气开采的当前储层模型添加独立约束，与获取数据或执行传统储层模型相比，几乎没有额外成本。其目的是提供一个用户友好的平台，使最终用户能够识别地质力学效应，并使用结果来约束常规“黑油”模拟器中的参数，从而改善油藏描述和常规预测。