非常规油藏地下综合表征——以阿联酋阿布扎比陆上油田Diyab碳酸盐岩烃源岩为例

Amena Alharthi, M. Baig, Hassan Al Marzooqi, Abdullah Al Hashmi, Pierre Van Laer, Trevor Brooks, A. Al Blooshi, Marco Coscia
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引用次数: 0

摘要

随着对天然气需求的不断增加,非常规天然气资源的勘探和评价活动不断扩大,对满足全球需求具有重要意义。这些非常规资源具有复杂的地球化学和岩石物理特征。了解非常规岩石的复杂性质具有挑战性,需要综合运用先进的储层表征方法。在这项研究中,设计了一个综合的岩石表征工作流程,以了解与Diyab组非常规岩石相关的挑战和不确定性。通过综合工作流程,对800多英尺的非常规岩心进行了分析,以表征Jubaila、Hanifa和Tuwaiq山地层的侏罗纪碳酸盐岩演替。工作流程包括通过被称为“非均质岩石分析”(HRA)的机器学习,基于岩心和OH测井的初始岩石分类。在HRA的基础上,进行了非常规地质力学岩心分析和先进地质力学岩心分析的样品选择、岩心资料解释、岩心-测井整合、储层质量细化等工作。该工作流程中的非常规和高级岩心分析包括但不限于以下类型:液体TRA、TOC、HAWK、镜质体反射率(VR)、岩心核磁共振T2、MICP、2D/3D SEM、Dean Stark、XRD/XRF、地质力学(巴西拉伸强度、无侧限压缩(UCS)、单(TXC)和多级三轴(MTXC)、多应力压缩(MSC)、Biot系数测试)等。对岩心分析结果进行解释并与测井资料相结合,以更好地了解和表征非常规储层的性质。使用所有可用的数据进行样本选择,以捕获岩石物理、地质力学和地球化学的变化,特别是有机质含量,以及每种确定的岩石物理岩石类别中的矿物学。岩心测井、桥塞分析和电缆数据已经整合在一起,在相关的不确定性范围内显示出良好的一致性,这些不确定性可归因于岩石密封性和分辨率的变化。地球化学(TOC、HAWK和VR)显示出高浓度的干酪根,最初为IIS型,但目前呈低HI型,成熟度反映了干气窗和可能的凝析油。孔隙度范围为2.7% ~ 8%,最大读数来自MICP数据。2D和3D SEM图像提供了一些关键的发现,这些发现与给定样品中不同的孔隙度有关,包括连接的、分离的和/或有机物质孔隙系统。这些复杂的孔隙系统不能仅用常规方法捕获。有机孔隙度很重要,因为它为基质孔隙度连通性提供了进一步的支持。将这些知识与测井、地球化学、岩石物理学和矿物学相结合,有助于完善初步表征的岩石性质。此外,对地质力学的理解进一步整合,根据分类应力状态确定压裂和测试的潜在区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subsurface Integrated Unconventional Reservoir Characterization - A Case Study of Diyab Carbonate Source Rock from Onshore Field, Abu Dhabi, UAE
As the demand for natural gas is increasing, the exploration and appraisal activities for unconventional gas resources is expanding and becoming significant to fulfill the global demand. These Unconventional resources are known to have complex geochemistry and rock physics. Understanding the complex nature of unconventional rocks is challenging and requires comprehensive integration with an advanced reservoir characterization approach. In this study, a comprehensive integrated rock characterization workflow was designed to understand the challenges and uncertainties associated with the Diyab Formation unconventional rocks. More than 800 ft of unconventional cores were analyzed to characterize the Jurassic carbonate succession of Jubaila, Hanifa and Tuwaiq Mountain Formations through an integrated workflow. The workflow includes core and OH logs based initial rock classification through machine learning known as "Heterogeneous Rock Analysis" (HRA). Based on HRA, the samples selection for Unconventional and advanced Geomechanical core analysis was applied, followed by core data interpretation, core to logs integration and refining reservoir quality. Unconventional and advanced core analysis in this workflow include but not limited to following types, liquid TRA, TOC, HAWK, Vitrinite Reflectance (VR), Core-NMR T2, MICP, 2D/3D SEM, Dean Stark, XRD/XRF, Geomechanics (Brazil Tensile Strength, Unconfined Compression (UCS), Single (TXC) and Multi Stage Triaxial (MTXC), Multi-Stress Compression (MSC), Biot coefficient test), etc. Core analysis results were interpreted and integrated with the logs to better understand and characterize the unconventional reservoir qualities. Sample selection was performed using all available data, to capture the variations in petrophysics as well as geomechanics and geochemistry, particularly organic matter content, and mineralogy within each identified petrophysical rock class. Core logs, plug analysis, and wireline data have been integrated and generally showed excellent agreement within the range of associated uncertainties, which can be attributed to rock tightness and resolution variations. Geochemistry (TOC, HAWK & VR) shows high concentration of kerogen, initially of type IIS but presently with low HI in which maturity reflects the dry gas window and possible condensate. Porosity ranges from 2.7% to 8% with a maximum reading reported from MICP data. The 2D & 3D SEM images provided some key findings, associated with different porosities either connected, isolated and/or organic matter porosity systems in given samples. These complex porosities systems cannot be captured by only conventional methods. The organic type of porosity is important as it provides further support to matrix porosity connectivity. Integrating this knowledge with logs, geochemistry, petrophysics and mineralogy helped to refine the initial characterized rock properties. In addition, the geomechanical understanding took the integration step further to identify potential zones for fracking and testing based on the classified stress regime.
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