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Integrated reservoir characterization for unconventional reservoirs using seismic, microseismic and well log data.

机译：使用地震，微地震和测井数据对非常规油藏进行综合油藏表征。

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摘要

This study is aimed at an improved understanding of unconventional reservoirs which include tight reservoirs (such as shale oil and gas plays), geothermal developments, etc. We provide a framework for improved fracture zone identification and mapping of the subsurface for a geothermal system by integrating data from different sources. The proposed ideas and methods were tested primarily on data obtained from North Brawley geothermal field and the Geysers geothermal field apart from synthetic datasets which were used to test new algorithms before actual application on the real datasets. The study has resulted in novel or improved algorithms for use at specific stages of data acquisition and analysis including improved phase detection technique for passive seismic (and teleseismic) data as well as optimization of passive seismic surveys for best possible processing results. The proposed workflow makes use of novel integration methods as a means of making best use of the available geophysical data for fracture characterization. The methodology incorporates soft computing tools such as hybrid neural networks (neuro-evolutionary algorithms) as well as geostatistical simulation techniques to improve the property estimates as well as overall characterization efficacy. The basic elements of the proposed characterization workflow involves using seismic and microseismic data to characterize structural and geomechanical features within the subsurface. We use passive seismic data to model geomechanical properties which are combined with other properties evaluated from seismic and well logs to derive both qualitative and quantitative fracture zone identifiers. The study has resulted in a broad framework highlighting a new technique for utilizing geophysical data (seismic and microseismic) for unconventional reservoir characterization. It provides an opportunity to optimally develop the resources in question by incorporating data from different sources and using their temporal and spatial variability as a means to better understand the reservoir behavior. As part of this study, we have developed the following elements which are discussed in the subsequent chapters: 1. An integrated characterization framework for unconventional settings with adaptable workflows for all stages of data processing, interpretation and analysis. 2. A novel autopicking workflow for noisy passive seismic data used for improved accuracy in event picking as well as for improved velocity model building. 3. Improved passive seismic survey design optimization framework for better data collection and improved property estimation. 4. Extensive post-stack seismic attribute studies incorporating robust schemes applicable in complex reservoir settings. 5. Uncertainty quantification and analysis to better quantify property estimates over and above the qualitative interpretations made and to validate observations independently with quantified uncertainties to prevent erroneous interpretations. 6. Property mapping from microseismic data including stress and anisotropic weakness estimates for integrated reservoir characterization and analysis. 7. Integration of results (seismic, microseismic and well logs) from analysis of individual data sets for integrated interpretation using predefined integration framework and soft computing tools.
机译：这项研究旨在增进对非常规油藏的理解，包括致密油藏（例如页岩油气田），地热开发等。我们通过整合地热系统提供了一个改进的裂缝区识别和地下地层测绘的框架来自不同来源的数据。除了从综合数据集（用于在实际数据集上实际应用之前用于测试新算法）的合成数据集之外，主要对从North Brawley地热田和Geysers地热田获得的数据进行了测试。这项研究产生了新颖的或经过改进的算法，可用于数据采集和分析的特定阶段，包括改进的用于被动地震（和远程地震）数据的相位检测技术，以及优化被动地震勘探以获得最佳处理结果的方法。拟议的工作流程利用新颖的整合方法，作为充分利用可用地球物理数据进行裂缝表征的一种手段。该方法结合了诸如混合神经网络（神经进化算法）之类的软计算工具以及地统计模拟技术，以改善特性估计以及整体表征功效。提出的表征工作流程的基本要素包括使用地震和微地震数据来表征地下的结构和地质力学特征。我们使用被动地震数据对地质力学特性进行建模，然后将其与从地震和测井中获得的其他特性进行组合，以得出定性和定量的断裂带识别符。该研究产生了一个广泛的框架，突出了利用地球物理数据（地震和微地震）进行非常规储层表征的新技术。通过合并来自不同来源的数据并将其时空变化作为更好地了解储层行为的一种手段，它提供了一个优化开发相关资源的机会。作为这项研究的一部分，我们已经开发了以下元素，这些元素将在后续章节中进行讨论：1.一个用于非常规设置的集成表征框架，具有适用于数据处理，解释和分析所有阶段的自适应工作流。 2.一种新颖的自动采集工作流程，用于处理嘈杂的被动地震数据，以提高事件拾取的准确性以及改进速度模型的建立。 3.改进了被动地震勘测设计优化框架，以实现更好的数据收集和改进的属性估计。 4.广泛的叠后地震属性研究结合了适用于复杂储层环境的鲁棒方案。 5.不确定性量化和分析，以更好地量化定性解释之外的财产估计，并以量化的不确定性独立地验证观察结果，以防止错误的解释。 6.从微地震数据中绘制特性图，包括应力和各向异性弱点估计，以进行综合的储层表征和分析。 7.使用预定义的集成框架和软计算工具对单个数据集进行分析得到的结果（地震，微地震和测井）进行整合。

著录项

  • 作者

    Maity, Debotyam.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Petroleum engineering.;Geophysics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 254 p.
  • 总页数 254
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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