AGER, 2018, 2(1): 1-13 基于水力流动单元的页岩油储层渗透率评价

宁坤

2018年第一篇论文

基于水力流动单元的页岩油储层渗透率评价

张鹏飞1,2，卢双舫1,*，李俊乾1,*，张婕1,2，薛海涛1，陈晨1,2

1. 中国石油大学（华东） 非常规油气与新能源研究院 山东 青岛，266580

2. 中国石油大学（华东） 地球科学与技术学院 山东 青岛，266580

        渗透率是页岩最重要的储层物性参数之一，控制着流体由页岩基质流向水力裂缝，以及页岩油气的产量和最终采收率。目前，已有众多方法揭示页岩储层渗透率分布特征，但尚无方法能够准确评价非均质性强且孔喉结构复杂的页岩油储层全井段渗透率分布。而水力流动单元（HFU）可将储层划分为渗流特征不同的层段，同时在每个层段内孔隙度和渗透率具有很好的相关性。本文东营凹陷页岩油储层为例，应用水力流动单元，结合BP神经网络模型建立了渗透率测井评价方法。通过岩心孔隙度、渗透率建立东营凹陷页岩储层水力流动单元划分方案，获取不同流动单元页岩的孔渗关系，并建立孔隙度和流动带指数（FZI）BP神经网络测井评价模型。然后，根据FZI计算结果划分全井段页岩油储层水力流动单元类型，基于对应孔渗关系由孔隙度计算得到不同水力流动单元页岩渗透率。计算的渗透率曲线可有效指示页岩油渗流有利层段分布。

AGER, 2018, 2(1): 1-13 网址：http://www.astp-agr.com/index.php/Index/Index/detail?id=44

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Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach

Pengfei Zhang, Shuangfang Lu, Junqian Li, Jie Zhang, Haitao Xue, Chen Chen

(Published: 2018-01-08)

Corresponding Author and Email: Shuangfang Lu, lushuangfang@upc.edu.cn; Junqian Li, lijunqian1987@126.com

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Citation: Zhang, P., Lu, S., Li, J., Zhang, J., Xue, H., Chen, C. Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach. Advances in Geo-Energy Research, 2018, 2(1): 1-13, doi: 10.26804/ager.2018.01.01.

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Article Type: Original article

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Abstract:

Permeability is one of the most important petrophysical properties of shale reservoirs, controlling the fluid flow from the shale matrix to artificial fracture networks, the production and ultimate recovery of shale oil/gas. Various methods have been used to measure this parameter in shales, but no method effectively estimates the permeability of all well intervals due to the complex and heterogeneous pore throat structure of shale. A hydraulic flow unit (HFU) is a correlatable and mappable zone within a reservoir, which is used to subdivide a reservoir into distinct layers based on hydraulic flow properties. From these units, correlations between permeability and porosity can be established. In this study, HFUs were identified and combined with a back propagation neural network to predict the permeability of shale reservoirs in the Dongying Depression, Bohai Bay Basin, China. Well data from three locations were used and subdivided into modeling and validation datasets. The modeling dataset was applied to identify HFUs in the study reservoirs and to train the back propagation neural network models to predict values of porosity and flow zone indicator (FZI). Next, a permeability prediction method was established, and its generalization capability was evaluated using the validation dataset. The results identified five HFUs in the shale reservoirs within the Dongying Depression. The correlation between porosity and permeability in each HFU is generally greater than the correlation between the two same variables in the overall core data. The permeability estimation method established in this study effectively and accurately predicts the permeability of shale reservoirs in both cored and un-cored wells. Predicted permeability curves effectively reveal favorable shale oil/gas seepage layers and thus are useful for the exploration and the development of hydrocarbon resources in the Dongying Depression.

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Keywords: Permeability, porosity, shale, hydraulic flow units, back propagation neural network.

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