首页> 外文会议>SPWLA Annual Logging Symposium >UNLOCKING THE POTENTIAL OF UNCONVENTIONAL RESERVOIRS THROUGH NEW GENERATION NMR T1/T2 LOGGING MEASUREMENTS INTEGRATED WITH ADVANCED WIRELINE LOGS
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UNLOCKING THE POTENTIAL OF UNCONVENTIONAL RESERVOIRS THROUGH NEW GENERATION NMR T1/T2 LOGGING MEASUREMENTS INTEGRATED WITH ADVANCED WIRELINE LOGS

机译:通过新一代NMR T1 / T2记录测量来解锁非传统水库的潜力,与先进的电缆日志一体化

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The fundamental challenges in formation evaluation of unconventional shale reservoirs are the estimation of producible hydrocarbons and identification of target zones for horizontal wells. Modern gamma-ray spectroscopy tools can estimate the in-situ total organic carbon (TOC) which is an important parameter for determining the hydrocarbon potential of shale reservoirs. However, differentiating the components of TOC into kerogen and bound and free hydrocarbon remains an outstanding problem. Resistivity and dielectric measurements are sensitive only to water-filled porosities, and therefore these measurements cannot be used for partitioning the TOC components. Similarly, nuclear magnetic resonance (NMR) T2 cutoff-based methods for the estimation of clay-bound, capillary-bound, and free fluids are generally not valid because the responses of the different fluids overlap in the T2 domain. In this paper, we describe a novel methodology for quantitative estimation of fluid volumes in unconventional shale reservoirs from NMR T1-T2 measurements. The methodology is based on the mathematical concepts of data analytics for identifying underlying features and systematic relationships in the data without any a-priori information. The NMR T1-T2 data in an entire logged interval are arranged in a database matrix and subsequently resolved as a product of two positive matrices using an automatic feature extraction technique. The first matrix contains the T1-T2 signatures of the different fluids and the second matrix contains the respective volumetric fractions of each fluid type. The methodology is robust and provides accurate extraction of fluid signatures even in low porosity and low signal-to-noise ratio conditions. The extracted fluid signatures could be easily visualized for gaining further petrophysical insights. The methodology is used to predict continuous logs of fluid volumes in unconventional shale reservoir. High resolution T1-T2 measurements were obtained in an Eagle Ford shale play using a new-generation wireline NMR logging tool. The higher operating frequency of the tool and a new acquisition scheme implemented in the tool result in enhanced sensitivity to the T1-T2 contrasts of fluids and to the fast-relaxing components typical of shale reservoirs. The signatures of different fluids such as bitumen, hydrocarbons in organic matter and inter-particle pores, clay-bound and inter-particle water could be resolved using the methodology described. The fluid volumes show good comparison with core measurements obtained using Dean Stark and tight rock analysis. Integration of fluid volumes from T1-T2 measurements with other advanced wireline logs such as spectroscopy and dielectric measurements provides a complete formation evaluation in shales. A second field example from a heavy oil reservoir shows that the methodology could accurately resolve fluid signatures including heavy oil, clay-bound and capillary-bound water.
机译:非传统页岩储层的形成评估中的根本挑战是估计水平孔的生产碳氢化合物和鉴定目标区。现代伽马射线光谱工具可以估计原位总有机碳(TOC),这是确定页岩储层的烃潜力的重要参数。然而,将TOC的组分区分开进入角蛋白和结合和游离烃仍然是一个突出的问题。电阻率和电介质测量仅敏感到充满水孔的孔隙率,因此这些测量不能用于划分TOC组件。类似地,核磁共振(NMR)T2用于估计粘土,毛细血管结合和游离流体的方法通常无效,因为不同流体的反应在T2结构域中重叠。在本文中,我们描述了一种新的NMR T1-T2测量中非传统页岩储层中流体体积的定量估计方法。该方法基于数​​据分析的数学概念,用于识别数据中的底层特征和系统关系,而无需任何先验信息。整个记录间隔中的NMR T1-T2数据被布置在数据库矩阵中,随后使用自动特征提取技术作为两个正矩阵的乘积。第一矩阵含有不同流体的T1-T2签名,第二矩阵包含每个流体类型的相应体积分数。该方法是稳健的,即使在低孔隙率和低信噪比条件下也能够精确提取流体签名。提取的流体签名可以很容易地可视化以获得进一步的岩石物理洞察力。该方法用于预测非传统页岩储层中的流体体积的连续日志。使用新一代有线NMR测井工具在Eagle Ford页岩播放中获得高分辨率T1-T2测量。工具的较高工作频率和在工具中实现的新的采集方案导致对流体的T1-T2对比度的灵敏度增强以及典型的页岩储存器的快速松弛部件。可以使用所描述的方法解析不同流体如沥青,有机物质中的烃和颗粒孔中的颗粒孔,粘土结合和颗粒水的签名。流体容积显示出与使用Dean Stark和紧密岩石分析获得的核心测量的良好比较。使用其他先进的有线日志(如光谱学和电介质测量)的T1-T2测量集成了流体体积,提供了Shales的完整形成评估。来自重油储层的第二场示例表明,该方法可以准确地解析包括重油,粘土和毛细血管结合水的流体签名。

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