用于二氧化碳监测调查的金伯利纳合成多物理场数据集

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
David Alumbaugh, Erika Gasperikova, Dustin Crandall, Michael Commer, Shihang Feng, William Harbert, Yaoguo Li, Youzuo Lin, Savini Samarasinghe
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引用次数: 0

摘要

我们以加利福尼亚州南圣华金盆地一个潜在的二氧化碳封存地点为基础,介绍了由金伯利纳 1.2 二氧化碳储层模型构建的合成多尺度、多物理场数据集。在 300 个模型中,一个选定的储层模拟方案生成了二氧化碳注入开始和 20 年后的水文状态模型。随后,这些模型被转化为地球物理属性,包括 P 波和 S 波地震速度、饱和密度(其中饱和流体可以是盐水和超临界二氧化碳的组合)以及电阻率(使用已建立的经验岩石物理关系)。根据这些地球物理特性的三维分布,我们生成了合成延时地震、重力和电磁响应,其采集几何形状模仿了现实的监测勘测,并可在实际现场情况下实现。我们还在注水井和三口监测井中创建了一系列二氧化碳饱和度、声速、密度和感应电阻率的合成测井记录。这些都是通过将地球物理模型的低频趋势与在潜在封存地点采集的实际测井记录的高频变化相结合而构建的。此外,为了更好地校准我们的数据集,还对构成主要储层单元的维德砂岩岩心进行了渗透率和孔隙连通性测量。这些测量结果为合成数据集提供了尺度范围,使其尽可能接近实际情况。该数据集由储层模型、地球物理模型、模拟延时地球物理响应和测井记录组成,是设计和测试二氧化碳地球物理监测系统以及成像和表征算法的多尺度、多物理场试验台。这套数值模型和数据已在美国国家能源技术实验室(NETL)的能源数据交换(EDX)网站上公开提供下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

We present a synthetic multi-scale, multi-physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir-simulation scenario produces hydrologic-state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P- and S-wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time-lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real-world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time-lapse geophysical responses and well logs forms a multi-scale, multi-physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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