Optimization Models for Investment, Operation and Water Management in Shale Gas Supply Chains

页岩气供应链投资、运营和水管理优化模型

• 批准号: 1437668
• 负责人: Ignacio Grossmann
• 金额: $ 21.35万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1437668&HistoricalAwards=false
• 关键词: Optimization; Models; Investment; Operation; Water

1437668 - GrossmannThe production of shale gas is one of the most important developments that has taken place in the US in the last decade. It has radically changed the availability of energy sources, as well as greatly improved the international competitiveness of the U.S. petrochemical industry from low-cost feedstocks. The Energy Information Administration predicts U.S. shale gas production to grow from 23% to almost 50% of the total gas production for the next 25 years. In order to support this projection, the objective of this proposal is to contribute to the optimal and sustainable development of the shale gas industry through new computational tools for optimizing the investment and operation of production fields and processing plants, as well as optimizing water management and reuse. The proposed computational tools will be based on novel optimization models and advanced mixed-integer programming methods that optimize the design and operation of the supply chain infrastructure, as well as optimizing the use and cost of water management for hydraulic fracturing operations, while accounting for its environmental impact. The motivation for this research is that currently there are virtually no computer tools available to support decision-makers in the development of shale gas facilities. The project will be complemented by educational materials that can be used in undergraduate design courses in chemical engineering. The goal is to make students aware of the engineering challenges and opportunities involved in shale gas production.Intellectual Merit :The primary goal of the proposed work is to develop a new mixed-integer optimization model for the design of shale gas supply chain infrastructures. The proposed model is aimed at optimizing the selection of the number of wells to drill on new/existing pads, size and location of new gas processing plants, location and length of new pipelines for gathering raw gas, delivering dry gas, and natural gas liquids, location and power of gas compressors to be installed, and planning of freshwater consumption from available reservoirs for well drilling and fracturing. The goal of this model is to maximize the net present value of the supply chain infrastructure over a long planning time horizon. A major challenge in the model involves solving a large-scale mixed-integer nonlinear program (MINLP) to global optimality. The proposed project will also involve the development of a detailed operational mixed-integer linear model to optimize water use life cycle for well pads with the objective of reducing freshwater consumption by reuse and recycle, while minimizing transportation and treatment costs. The supply chain and water management models will be extended to minimize environmental impact within a multi-objective optimization framework that incorporates Life Cycle Analysis.Broader Impacts :From a theoretical point of view this research will open new application areas for modeling and optimization that have not been addressed before in process systems engineering. These are likely to impact the academic community by promoting research in the development of new complex MINLP models for design and operation of shale gas processing. From a practical point of view, the proposed project will provide new advanced computer tools that have not been used by shale gas producers, whether large or small. The proposed work on supply chain infrastructure will help to optimally integrate investment and operational decisions, including scheduling the drilling of wells. Furthermore, it is hoped that this work on water management will promote the efficient and sustainable use of water, as well as reducing congestion of roads for its transportation. The proposed models will help to quantitatively assess the environmental impact of shale gas production. From an educational perspective, the PI will engage undergraduates in research in these areas, and develop educational materials in the form of case studies that can be used in the teaching of undergraduate design courses in chemical engineering to make students aware of the challenges and opportunities involved in producing shale gas. Finally, the PI will leverage the project with his industrial partners of the Center for Advanced Process Decision-making, gas producers in the greater Pittsburgh area, and international collaborations with researchers in Argentina and Norway where the production of shale gas is actively pursued. The practical relevance of the proposed models will be validated with case studies provided by industrial collaborators.

1437668 - Grossmann 页岩气的生产是过去十年美国最重要的发展之一。它从根本上改变了能源的可获得性，并通过低成本原料极大地提高了美国石化工业的国际竞争力。美国能源情报署预测，未来 25 年美国页岩气产量占天然气总产量的比例将从 23% 增长到近 50%。为了支持这一预测，该提案的目标是通过新的计算工具优化生产领域和加工厂的投资和运营，以及优化水管理和再利用，为页岩气行业的优化和可持续发展做出贡献。所提出的计算工具将基于新颖的优化模型和先进的混合整数编程方法，优化供应链基础设施的设计和运营，以及优化水力压裂作业的水管理的使用和成本，同时考虑其环境影响。这项研究的动机是，目前几乎没有可用的计算机工具来支持决策者开发页岩气设施。该项目将得到可用于化学工程本科设计课程的教育材料的补充。目标是让学生意识到页岩气生产中涉及的工程挑战和机遇。智力优势：拟议工作的主要目标是为页岩气供应链基础设施的设计开发一种新的混合整数优化模型。所提出的模型旨在优化新/现有平台上钻探井数量的选择、新天然气加工厂的规模和位置、用于收集原气、输送干气和天然气液体的新管道的位置和长度、要安装的气体压缩机的位置和功率，以及规划用于钻井和压裂的可用储层的淡水消耗。该模型的目标是在较长的规划时间内最大化供应链基础设施的净现值。该模型的一个主要挑战涉及求解大规模混合整数非线性规划（MINLP）以实现全局最优。拟议项目还将涉及开发详细的混合整数线性操作模型，以优化井场的用水生命周期，目的是通过再利用和循环利用来减少淡水消耗，同时最大限度地减少运输和处理成本。供应链和水管理模型将得到扩展，以在包含生命周期分析的多目标优化框架内最大限度地减少对环境的影响。更广泛的影响：从理论角度来看，这项研究将为建模和优化开辟新的应用领域，而这些领域以前在过程系统工程中尚未得到解决。这些可能会通过促进用于页岩气加工设计和操作的新型复杂 MINLP 模型的开发研究来影响学术界。从实用的角度来看，拟议的项目将提供页岩气生产商（无论大小）尚未使用的新的先进计算机工具。拟议的供应链基础设施工作将有助于优化整合投资和运营决策，包括安排钻井时间。此外，希望这项水管理工作能够促进水的高效和可持续利用，并减少交通道路的拥堵。所提出的模型将有助于定量评估页岩气生产对环境的影响。从教育角度来看，PI将让本科生参与这些领域的研究，并以案例研究的形式开发教育材料，可用于化学工程本科生设计课程的教学，让学生意识到页岩气生产所面临的挑战和机遇。最后，PI 将与高级工艺决策中心的工业合作伙伴、大匹兹堡地区的天然气生产商以及与积极从事页岩气生产的阿根廷和挪威的研究人员的国际合作来利用该项目。所提出模型的实际相关性将通过工业合作者提供的案例研究进行验证。