Emerging Techniques in Measuring Capillary Pressure and Permeability Using NMR and AI

A. Ghamdi, A. Isah, M. Elsayed, Kareem Garadi, A. Abdulraheem
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引用次数: 1

Abstract

Measurement of Special Core Analysis (SCAL) parameters is a costly and time-intensive process. Some of the disadvantages of the current techniques are that they are not performed in-situ, and can destroy the core plugs, e.g., mercury injection capillary pressure (MICP). The objective of this paper is to introduce and investigate the emerging techniques in measuring SCAL parameters using Nuclear Magnetic Resonance (NMR) and Artificial Intelligence (Al). The conventional methods for measuring SCAL parameters are well understood and are an industry standard. Yet, NMR and Al - which are revolutionizing the way petroleum engineers and scientists describe rock/fluid properties - have yet to be utilized to their full potential in reservoir description. In addition, integration of the two tools will open a greater opportunity in the field of reservoir description, where measurement of in-situ SCAL parameters could be achieved. This paper shows the results of NMR lab experiments and Al analytics for measuring capillary pressures and permeability. The data set was divided into 70% for training and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared well with the permeability and capillary pressure data measured from the conventional methods. Specifically, the model predicted permeability 10% error. Similarly, for the capillary pressures, the model was able to achieve an excellent match. This active research area of prediction of capillary pressure, permeability and other rock properties is a promising emerging technique that capitalizes on NMR/AI analytics. There is significant potential is being able to evaluate wettability in-situ. Core-plugs undergoing Amott-Harvey experiment with NMR measurements in the process can be used as a building block for an NMR/AI wettability determination technique. This potential aspect of NMR/AI analytics can have significant implications on field development and EOR projects The developed NMR-Al model is an excellent start to measure permeability and capillary pressure in-situ. This novel approach coupled with ongoing research for better handling of in-situ wettability measurement will provide the industry with enormous insight into the in-situ SCAL measurements which are currently considered as an elusive measurement with no robust logging technique to evaluate them in-situ.
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利用核磁共振和人工智能测量毛细管压力和渗透率的新技术
特殊核心分析(SCAL)参数的测量是一个昂贵且耗时的过程。当前技术的一些缺点是它们不是在原位进行的,并且可能破坏岩心塞,例如汞注入毛细管压力(MICP)。本文的目的是介绍和研究利用核磁共振(NMR)和人工智能(Al)测量SCAL参数的新兴技术。测量SCAL参数的常规方法是很容易理解的,并且是一个行业标准。然而,核磁共振和人工智能正在彻底改变石油工程师和科学家描述岩石/流体性质的方式,但它们在油藏描述中尚未充分发挥其潜力。此外,这两种工具的集成将为储层描述领域提供更大的机会,在该领域可以实现原位SCAL参数的测量。本文介绍了用核磁共振实验室实验和Al分析方法测量毛细管压力和渗透率的结果。数据集分为70%用于训练,30%用于验证。采用人工神经网络(ANN)方法,与常规方法测得的渗透率和毛管压力数据进行了比较。具体来说,模型预测渗透率误差为10%。同样,对于毛细管压力,该模型能够实现极好的匹配。这一活跃的研究领域预测毛管压力、渗透率和其他岩石性质,是一项利用核磁共振/人工智能分析的新兴技术。在现场评估润湿性方面有很大的潜力。在过程中进行amot - harvey实验和核磁共振测量的岩心塞可以作为核磁共振/人工智能润湿性测定技术的基础。核磁共振/人工智能分析的这一潜在方面可以对油田开发和提高采收率项目产生重大影响。开发的核磁共振-人工智能模型是一个很好的开始,可以在现场测量渗透率和毛细管压力。这种新颖的方法与目前正在进行的更好地处理原位润湿性测量的研究相结合,将为行业提供对原位SCAL测量的深刻见解,目前SCAL测量被认为是一种难以捉摸的测量方法,没有可靠的测井技术来对其进行原位评估。
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