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Evaluation of new method for estimation of fracture parameters using conventional petrophysical logs and ANFIS in the carbonate heterogeneous reservoirs

机译:碳酸盐异质储层中常规岩石物理原木和ANFIS估算骨折参数估计新方法的评价

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Fractures and their parameters are considered as being the most important features in evaluation of the fractured reservoirs. This may be of more importance in the heterogeneous reservoirs, when primary porosity cannot explain the high production capacity. Cores and image logs are two direct methods for fractures evaluation. However, these methods have high cost drawback. So, petrophysical logs are useful tools for fractures detection due to their low cost and accessibility in all wells. This paper proposes a reliable and inexpensive method for the evaluation of fracture parameters in the carbonate heterogeneous reservoir using the preprocessed petrophysical logs and ANFIS (Adaptive Neuro-Fuzzy Inference System). This study indicates that there are two vital limitations when using petrophysical logs for fractures evaluation; firstly, fracture distribution is a complicated process to be predicted by classical methods and none of intelligent systems can be considered as a magic for fracture parameters estimation from raw conventional logs. Secondly, fractures determination is not valuable alone, unless they are used for evaluation of porosity and permeability systems. To solve these problems, a log preprocessing method and a new statistical equation are implemented on the raw conventional logs. Also, ANFIS is used as a powerful method for fracture parameters estimation using preprocessed logs. It is shown that sonic and porosity logs are the best tools for fracture studies. Also, resistivity and gamma ray family logs may have a good evaluation of fractures in the fractured zones. Results confirm that conventional logs could be more useful tools for fractures evaluation if they preprocessed by some statistical methods and correlated by image logs or core data. Proposed method may also be usable for estimation of fractures aperture in some image tools which they are not traditionally able to measure fractures aperture (e.g., sonic image tools). Undoubtedly, fracture aperture is the most important parameter for determination of fractures effect on the porosity and permeability systems. Due to high correlation between petrophysical logs and images/cores results (R2 approximate to 0.8), the results are dependable and extensible for other carbonate and naturally fracture reservoirs.
机译:裂缝及其参数被认为是评估碎屑储层的最重要特征。当原发性孔隙率无法解释高生产能力时,这在异质储层中可能更加重要。核心和图像日志是两种用于裂缝评估的直接方法。但是,这些方法具有高成本缺点。因此,由于它们在所有井中的低成本和可访问性,岩石物理日志是裂缝检测的有用工具。本文用预处理的岩石物理原木和ANFI(自适应神经模糊推理系统)提出了一种可靠且廉价的方法,用于评估碳酸酯异质储层中的断裂参数。该研究表明,使用粪便物理原木进行裂缝评估时存在两个重要的限制;首先,裂缝分布是通过经典方法预测的复杂过程,并且智能系统都不可以被认为是从原始传统日志估算的骨折参数估计的神奇。其次,除非它们用于评估孔隙率和渗透性系统,否则骨折测定并不重要。为了解决这些问题,在原始的传统日志上实现了日志预处理方法和新的统计方程。此外,ANFIS用作使用预处理日志的断裂参数估计的强大方法。结果表明,声波和孔隙度原木是裂缝研究的最佳工具。此外,电阻率和伽马射线家庭日志可能对骨折区域的骨折进行了良好的评估。结果确认,如果通过某种统计方法预处理并通过图像日志或核心数据相关,则传统日志可能是更有用的裂缝评估工具。所提出的方法也可以可用于估计它们在一些图像工具中的裂缝孔,它们不传统上能够测量裂缝孔(例如,声波图像工具)。毫无疑问,骨折孔是用于测定孔隙率和渗透系统的裂缝影响最重要的参数。由于岩石物理日志和图像/芯的高相关(R2近似为0.8),结果是可靠的,并且对于其他碳酸盐和天然骨折储存器可伸缩。

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