INSPIRE: A Data-Driven Approach toward Exploring Natural and Anthropogenic Methane Emissions in Regions of Shale Gas Development

INSPIRE:探索页岩气开发地区自然和人为甲烷排放的数据驱动方法

基本信息

  • 批准号:
    1639150
  • 负责人:
  • 金额:
    $ 100万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-08-01 至 2022-07-31
  • 项目状态:
    已结题

项目摘要

This INSPIRE project addresses the issue of high volume hydraulic fracturing, also called fracking, and its effects on ground water resources. Fracking allows drillers to extract natural gas from shale deep within the earth. Methane gas sometimes escapes from shale gas wells and can contaminate water resources or leak into the atmosphere where it contributes to greenhouse gas emissions. Monitoring for these potential leaks is difficult because methane is also released into aquifers naturally, and because monitoring is time- and resource-intensive. Such subsurface leakage may also be relatively rare. This project seeks to improve overall understanding of the impacts of natural gas drilling using both advances in computer science and geoscience, and to teach the public about such impacts. The project will elucidate both the effects of human activities such as shale gas development as well as natural processes which release methane into natural waters. Results of the proposed research will lead to a better understanding of water quality in areas of shale-gas development and will highlight problems and potentially problematic management practices. The research will advance both the fields of geoscience and computer science, will train interdisciplinary graduate students, and involve citizen scientists in collecting data and understanding environmental data analysis. The project combines new hydro-geochemical strategies and data mining approaches to study the release of methane into streams and ground waters. For example, researchers will explore how to analyze the heterogeneous spatial data that describe distributions of methane concentrations in natural waters. The objectives of this project are to i) transform the ability to measure methane in streams; ii) train citizen scientists to work with project scientists to sample streams in an area of shale-gas development and publish large-volume datasets of methane in natural waters and aquifers; iii) innovate data mining and machine learning methods for environmental data to identify anomalous spots with potential leakage; iv) run field campaigns to measure methane concentrations and isotopic signatures of water samples in these spots; v) foster dialogue among nonscientists, consultants, university scientists, members of the gas industry, government agencies, and nonprofit organizations in and beyond the target region. Toward this end, the team will host workshops aimed to build dialogue among stakeholders and will release data analytic software for environmental measurements to benefit a broader research community.
该INSPIRE项目解决了大流量水力压裂的问题,也称为水力压裂,及其对地下水资源的影响。水力压裂技术使钻探者能够从地下深处的页岩中提取天然气。甲烷气体有时会从页岩气井中泄漏出来,可能会污染水资源或泄漏到大气中,从而导致温室气体排放。监测这些潜在的泄漏是困难的,因为甲烷也是自然释放到含水层中的,而且监测是时间和资源密集型的。这种地下泄漏可能也相对较少。该项目寻求利用计算机科学和地球科学的进步来提高对天然气钻探影响的全面了解,并向公众传授这种影响。该项目将阐明人类活动的影响,如页岩气开发,以及向自然水域释放甲烷的自然过程。拟议研究的结果将有助于更好地了解页岩气开发地区的水质,并将突出问题和潜在的有问题的管理做法。这项研究将推动地球科学和计算机科学领域的发展,将培养跨学科的研究生,并让公民科学家参与收集数据和了解环境数据分析。该项目将新的水文地球化学战略和数据挖掘方法结合起来,研究甲烷向溪流和地下水中的释放。例如,研究人员将探索如何分析描述自然水域甲烷浓度分布的异质空间数据。这个项目的目标是:i)转变测量河流中甲烷的能力;ii)培训公民科学家与项目科学家合作,对页岩气开发地区的河流进行采样,并出版关于天然水域和含水层甲烷的大量数据集;iii)创新环境数据的数据挖掘和机器学习方法,以确定可能发生泄漏的异常地点;iv)开展实地活动,测量这些地点的甲烷浓度和水样的同位素特征;v)促进目标区域内外的非科学家、顾问、大学科学家、天然气行业成员、政府机构和非营利组织之间的对话。为此,该小组将举办研讨会,旨在建立利益攸关方之间的对话,并将发布环境测量数据分析软件,以造福于更广泛的研究社区。

项目成果

期刊论文数量(20)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
One Step toward Developing Knowledge from Numbers in Regional Analysis of Water Quality
区域水质分析中从数字中发展知识的一步
  • DOI:
    10.1021/acs.est.8b01035
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    11.4
  • 作者:
    Niu, Xianzeng;Wen, Tao;Li, Zhenhui;Brantley, Susan L.
  • 通讯作者:
    Brantley, Susan L.
Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool
  • DOI:
    10.1021/acs.est.9b06761
  • 发表时间:
    2020-07-21
  • 期刊:
  • 影响因子:
    11.4
  • 作者:
    Agarwal, Amal;Wen, Tao;Brantley, Susan L.
  • 通讯作者:
    Brantley, Susan L.
Gas well integrity and methane migration: evaluation of published evidence during shale-gas development in the USA
  • DOI:
    10.1007/s10040-020-02116-y
  • 发表时间:
    2020-02-26
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Hammond, Patrick A.;Wen, Tao;Engelder, Terry
  • 通讯作者:
    Engelder, Terry
Using a neural network – Physics-based hybrid model to predict soil reaction fronts
使用神经网络 – 基于物理的混合模型来预测土壤反应前沿
  • DOI:
    10.1016/j.cageo.2022.105200
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Wen, Tao;Chen, Chacha;Zheng, Guanjie;Bandstra, Joel;Brantley, Susan L.
  • 通讯作者:
    Brantley, Susan L.
Searching for anomalous methane in shallow groundwater near shale gas wells
  • DOI:
    10.1016/j.jconhyd.2016.10.005
  • 发表时间:
    2016-12-01
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Li, Zhenhui;You, Cheng;Brantley, Susan L.
  • 通讯作者:
    Brantley, Susan L.
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Susan Brantley其他文献

Susan Brantley的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Susan Brantley', 18)}}的其他基金

Workshop Proposal: Mapping a Future for Management of Low-Temperature Geochemical Data: Atlanta, GA or Charlotte, NC - February 2020
研讨会提案:绘制低温地球化学数据管理的未来:佐治亚州亚特兰大或北卡罗来纳州夏洛特 - 2020 年 2 月
  • 批准号:
    1939257
  • 财政年份:
    2019
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
EAGER SitS: Emergent Properties during Soil Formation
EAGER SitS:土壤形成过程中的新兴特性
  • 批准号:
    1841568
  • 财政年份:
    2018
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Collaborative research: Quantifying weathering rind formation rates using U-series isotopes along steep gradients of precipitation, bedrock ages, and topography in Guadeloupe
合作研究:利用U系列同位素沿着瓜德罗普岛陡峭的降水梯度、基岩年龄和地形量化风化皮的形成速率
  • 批准号:
    1251875
  • 财政年份:
    2013
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
Using the Susquehanna - Shale Hills CZO to Project from the Geological Past to the Anthropocene Future
利用萨斯奎哈纳 - 页岩山 CZO 来预测从地质过去到人类世的未来
  • 批准号:
    1331726
  • 财政年份:
    2013
  • 资助金额:
    $ 100万
  • 项目类别:
    Cooperative Agreement
An Accomplishment-Based Request for Renewal of the Susquehanna-Shale Hills Critical Zone Observatory (SSHO)
基于成就的萨斯奎哈纳-页岩山关键区域天文台 (SSHO) 更新请求
  • 批准号:
    1239285
  • 财政年份:
    2012
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Soils and vegetation as a record of anthropogenic pollutants: Mn in the Shale Hills CZO
土壤和植被作为人为污染物的记录:页岩山 CZO 中的锰
  • 批准号:
    1052614
  • 财政年份:
    2011
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
RCN-SEES: The Marcellus Shale Research Network
RCN-SEES:马塞勒斯页岩研究网络
  • 批准号:
    1140159
  • 财政年份:
    2011
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Collaborative Research: Acquisition of nitrogenase metal cofactors in soils: role of metallophores and limitation of N2-fixation
合作研究:土壤中固氮酶金属辅助因子的获取:金属团的作用和固氮的限制
  • 批准号:
    1024559
  • 财政年份:
    2010
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Critical Zone Science: A Workshop on the Biological Aspects of Weathering; October 3-5, 2009; Washington, D.C.
关键区域科学:风化生物学方面的研讨会;
  • 批准号:
    0946877
  • 财政年份:
    2009
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
Using a Critical Zone Exploration Network to Quantify Controls on Earth's Regolith
使用关键区域勘探网络量化对地球风化层的控制
  • 批准号:
    0819857
  • 财政年份:
    2008
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant

相似国自然基金

Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    合作创新研究团队
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    外国青年学者研究基金项目
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
  • 批准年份:
    2020
  • 资助金额:
    40 万元
  • 项目类别:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 批准年份:
    2013
  • 资助金额:
    77.0 万元
  • 项目类别:
    面上项目
Molecular Interaction Reconstruction of Rheumatoid Arthritis Therapies Using Clinical Data
  • 批准号:
    31070748
  • 批准年份:
    2010
  • 资助金额:
    34.0 万元
  • 项目类别:
    面上项目
高维数据的函数型数据(functional data)分析方法
  • 批准号:
    11001084
  • 批准年份:
    2010
  • 资助金额:
    16.0 万元
  • 项目类别:
    青年科学基金项目
染色体复制负调控因子datA在细胞周期中的作用
  • 批准号:
    31060015
  • 批准年份:
    2010
  • 资助金额:
    25.0 万元
  • 项目类别:
    地区科学基金项目
Computational Methods for Analyzing Toponome Data
  • 批准号:
    60601030
  • 批准年份:
    2006
  • 资助金额:
    17.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Facilitating circular construction practices in the UK: A data driven online marketplace for waste building materials
促进英国的循环建筑实践:数据驱动的废弃建筑材料在线市场
  • 批准号:
    10113920
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    SME Support
N2Vision+: A robot-enabled, data-driven machine vision tool for nitrogen diagnosis of arable soils
N2Vision:一种由机器人驱动、数据驱动的机器视觉工具,用于耕地土壤的氮诊断
  • 批准号:
    10091423
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Collaborative R&D
Data Driven Discovery of New Catalysts for Asymmetric Synthesis
数据驱动的不对称合成新催化剂的发现
  • 批准号:
    DP240100102
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Discovery Projects
PIDD-MSK: Physics-Informed Data-Driven Musculoskeletal Modelling
PIDD-MSK:物理信息数据驱动的肌肉骨骼建模
  • 批准号:
    EP/Y027930/1
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Fellowship
CC* Networking Infrastructure: YinzerNet: A Multi-Site Data and AI Driven Research Network
CC* 网络基础设施:YinzerNet:多站点数据和人工智能驱动的研究网络
  • 批准号:
    2346707
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Collaborative Research: Data-Driven Elastic Shape Analysis with Topological Inconsistencies and Partial Matching Constraints
协作研究:具有拓扑不一致和部分匹配约束的数据驱动的弹性形状分析
  • 批准号:
    2402555
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
CAREER: Data-Driven Hardware and Software Techniques to Enable Sustainable Data Center Services
职业:数据驱动的硬件和软件技术,以实现可持续的数据中心服务
  • 批准号:
    2340042
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Continuing Grant
CAREER: A Universal Framework for Safety-Aware Data-Driven Control and Estimation
职业:安全意识数据驱动控制和估计的通用框架
  • 批准号:
    2340089
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
ERI: Data-Driven Analysis and Dynamic Modeling of Residential Power Demand Behavior: Using Long-Term Real-World Data from Rural Electric Systems
ERI:住宅电力需求行为的数据驱动分析和动态建模:使用农村电力系统的长期真实数据
  • 批准号:
    2301411
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
Collaborative Research: Data-driven engineering of the yeast Kluyveromyces marxianus for enhanced protein secretion
合作研究:马克斯克鲁维酵母的数据驱动工程,以增强蛋白质分泌
  • 批准号:
    2323984
  • 财政年份:
    2024
  • 资助金额:
    $ 100万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了