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Geothermal: the Gift that Could Keep on Giving 地热:一份可以继续馈赠的礼物
Q3 Earth and Planetary Sciences Pub Date : 2023-10-01 DOI: 10.3997/1365-2397.fb2023085
Sander de Jong, Kevin McCarthy, William Pettitt
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引用次数: 0
Using Seismic and CSEM Imaging to Improve Geological Understanding of Mineralisation Along Mohns Ridge 利用地震和断层扫描成像技术提高对莫恩斯山脊矿化的地质认识
Q3 Earth and Planetary Sciences Pub Date : 2023-10-01 DOI: 10.3997/1365-2397.fb2023087
Vetle Vinje, Marit Stokke Bauck, Hao Jiang, Carsten Scholl, Stephen Edwards, Sarah Hill, Federico Buriola, Richard Wombell
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引用次数: 0
Adapting Hydrocarbon Workflows to Enable Efficient and Rapid Screening for CO2 Storage Potential 调整碳氢化合物工作流程,实现高效、快速的CO2储存潜力筛选
Q3 Earth and Planetary Sciences Pub Date : 2023-10-01 DOI: 10.3997/1365-2397.fb2023081
Joss Smith, Ashley Uren, Joe Jennings, Thomas Butt, Craig Lang
Identifying and screening subsurface carbon storage options requires knowledge of numerous elements that can be costly and time consuming to assess at the regional scale. To remediate this, we use Neftex® Predictions which has automated the process of screening its global geological content so that carbon storage fairway extents and storage resources can be readily assessed and compared. This frees the geoscientist from the burden of time-consuming data cleaning and geoprocessing workflows. Attributes that indicate suitability of the storage option are based on a bespoke Carbon Storage Adequacy Index, developed to assess the reservoir, seal and operational factors. This automated workflow enables rapid portfolio generation and comparison, with high ranked fairways being prioritised for further investigation. Once prioritised, areas within fairways that have the greatest storage potential are useful to identify, here termed ‘sweet spots’. An example of how to do this by creating distribution maps of volumetric Prospective Storage Resource is presented. This shows how the Neftex Predictions datasets, alongside third-party data, can enhance screening map generation, especially when inferring beyond available data control. Examples from the North Sea, the Mediterranean, and the Gulf of Mexico demonstrate the global applicability of the workflows.
识别和筛选地下碳储存方案需要了解许多要素,这些要素在区域范围内进行评估可能既昂贵又耗时。为了解决这个问题,我们使用了Neftex®预测,该预测自动化了筛选其全球地质含量的过程,以便可以很容易地评估和比较碳储存通道的范围和储存资源。这将地球科学家从耗时的数据清理和地理处理工作流程中解放出来。表明储层选择是否合适的属性是基于定制的碳储存充足性指数,该指数用于评估储层、密封和操作因素。这种自动化的工作流程可以快速生成和比较组合,并优先考虑排名较高的球道,以便进一步调查。一旦确定了优先级,球道内存储潜力最大的区域就可以被识别出来,这里称之为“最佳区域”。给出了如何通过创建容量预期存储资源的分布图来实现这一点的示例。这表明Neftex预测数据集与第三方数据一起可以增强筛选地图的生成,特别是在推断可用数据控制之外的情况下。北海、地中海和墨西哥湾的例子证明了该工作流程的全球适用性。
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引用次数: 0
Integrating Energy Datasets: the MDIO Format 整合能源数据集:MDIO格式
Q3 Earth and Planetary Sciences Pub Date : 2023-10-01 DOI: 10.3997/1365-2397.fb2023084
Altay Sansal, Ben Lasscock, Alejandro Valenciano
MDIO offers a technical solution for storing and retrieving energy data in the cloud and on-premises. As an open-source framework, it incorporates high-resolution, multi-dimensional arrays that accurately represent wind resources and seismic data for multiple applications. By utilising the Zarr format, MDIO ensures efficient chunked storage and parallel I/O operations, facilitating easy data interaction in diverse infrastructures. This paper covers MDIO’s application in renewable energy (wind simulations), predictive analytics, and seismic imaging and interpretation, aiming to provide a robust technical platform for researchers navigating the evolving energy landscape.
MDIO提供了在云和本地存储和检索能源数据的技术解决方案。作为一个开源框架,它结合了高分辨率、多维阵列,可以准确地表示多种应用的风力资源和地震数据。通过使用Zarr格式,MDIO确保了高效的块存储和并行I/O操作,促进了不同基础设施中的轻松数据交互。本文涵盖了MDIO在可再生能源(风模拟)、预测分析、地震成像和解释方面的应用,旨在为研究人员提供一个强大的技术平台,帮助他们了解不断变化的能源格局。
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引用次数: 0
Accelerating Insights from 4D Seismic Data with New Multi-Dimensional Data Structures 利用新的多维数据结构加速从4D地震数据中获得见解
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023073
Bill Shea, Jorg Herwanger, Peter Harris
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引用次数: 0
Closed-Loop Single Well Geothermal Solution 闭环单井地热解决方案
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023077
K. Maver, O. Vestavik, Jørgen Peter Rasmussen, Carl-Emil Larsen
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引用次数: 0
Screening for AVA Anomalies in Siliciclastic Basins: Testing a Seismic Inversion Method in the Mississippi Canyon, Gulf of Mexico 硅碎屑盆地AVA异常的筛选:在墨西哥湾密西西比峡谷测试地震反演方法
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023076
David Went, Richard Hedley, Jon Rogers
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引用次数: 0
Multi-Scenario Deep Learning 4D Inversion: a Brazil Pre-Salt Case Study 多场景深度学习4D反演:巴西盐下案例研究
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023069
Yang Xue, D. Clarke, Kanglin Wang
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引用次数: 0
Time-Lapse Gravity and Subsidence Applied in History Matching of a Gas-Condensate Field 时移重力沉降在凝析气田历史拟合中的应用
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023075
Øystein Haugen Solbu, Asgeir Nyvoll, H. Alnes, Siri Catherine Vassvåg, M. Lien, Hugo Ruiz
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引用次数: 0
Optimising Reservoir Development and Asset Value Using Unified Ensemble Modelling 使用统一集成建模优化储层开发和资产价值
Q3 Earth and Planetary Sciences Pub Date : 2023-09-01 DOI: 10.3997/1365-2397.fb2023072
Sebastien Strebelle, Martha Stunell
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引用次数: 0
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