低电阻率含碳酸盐:一种使用改进的Archie模型量化含水饱和度的实用方法

Ramsin Y. Eyvazzadeh, A. Al-Omair, M. Kanfar, A. Christon
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摘要

提出了一种改进的Archie方程的详细描述,以准确量化低电阻率/低对比度储层碳酸盐岩中的含水饱和度。以前关于低电阻率/低对比储层的大部分工作都集中在碎屑上,即薄层和/或粘土对电阻率测量的影响。最近的出版物强调了具有复杂孔隙结构的碳酸盐的“非阿奇”行为。介绍了几种理论模型,但没有推导出新的实际应用来解决这一问题。建立在以前的理论研究在一个整体的方法,新的模型和工作流程已经开发。具体来说,利用机器学习算法、核磁共振(NMR)、岩心和地质数据的组合,给出了计算复杂碳酸盐岩地层含水饱和度(Sw)的特定校准方程。从本质上讲,这些新模型将孔隙度划分为孔隙空间,并计算它们对每个孔隙空间含水饱和度的相对贡献。这些经过校准的方程产生的结果在储层识别、井眼布置方面具有非常重要的价值,并大大提高了管理这类油藏的能力。本文首先解释了分析开发背后的理论,说明了用于验证该方法的工作流和验证技术。进行这项研究的一个关键优势是利用机器学习算法来预测没有核磁共振数据的井的核磁共振衍生值。通过将分析结果与动态测试、地层测试和实验室测量样品进行比较,验证和展示了这种新分析的实用性。
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Low Resistivity Pay Carbonates: A Practical Approach to Quantify Water Saturation Using a Modified Archie's Model
A detailed description of a modified Archie's equation is proposed to accurately quantify water saturation within low resistivity/low contrast pay carbonates. The majority of previous work on low resistivity/low contrast reservoirs focused on clastics, namely, thin beds and/or clay effects on resistivity measurements. Recent publications have highlighted a "non-Archie" behavior in carbonates with complex pore structures. Several theoretical models were introduced, but new practical applications were not derived to solve this issue. Built upon previous theoretical research in a holistic approach, new models and workflows have been developed. Specifically, utilizing a combination of machine learning algorithms, nuclear magnetic resonance (NMR), core and geological data, field specific calibrated equations to compute water saturation (Sw) in complex carbonate formations are presented. Essentially, these new models partition the porosity into pore spaces and calculate their relative contribution to water saturation in each pore space. These calibrated equations robustly produce results that have proven invaluable in pay identification, well placement, and have greatly enhanced the ability to manage these types of reservoirs. This paper initially explains the theory behind the development of the analysis illustrating workflows and validation techniques used to qualify this methodology. A key benefit performing this research is the utilization of machine-learning algorithms to predict NMR derived values in wells that do not have NMR data. Several examples explore where results of this analysis are compared to dynamic testing, formation testing and laboratory measured samples to validate and demonstrate the utility of this new analysis.
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