多油田电相分类及岩心数据改进渗透率及产能估算

Xinlei Shi, Hongbing Chen, Ruijuan Li, Xiaoyu Yang, Huan-Min Liu, Ting Li
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引用次数: 3

摘要

在行业中,通过常规岩心分析建立孔隙度-渗透率关系(poroperm)来估计连续渗透率是一种常见的做法。对于每个新油田,都需要岩心数据来建立该特定油田的渗透率模型。由于储层的非均质性,岩心衍生的孔隙度有时会导致有偏差的预测。对于那些岩心样本稀缺或储层覆盖率低的油田尤其如此。在油田开发规划中,提高渗透率模型的精度是提高产能估计的关键。1984年,Hearn等人在研究美国怀俄明州HartogDraw油田的Shannon油藏时首次提出了流动单元的概念。自Hearn提出储层流动单元概念以来,不同学者提出了各种电相分类方法(Hearn et al. 1984)。通常它们可以分为两类。一种是地质研究方法,主要利用地质岩屑和常规岩心分析计算流带指数(FZI)进行储层分类(Xinlei et al. 2017;Elphick et al. 1999;Kohonen et al. 1982)。该方法在一定程度上提高了渗透率评价的准确性,但主要依赖于常规岩心分析数据。由于延展性差,该方法在对无芯储层进行分类时存在一定的局限性。另一个是近年来石油行业比较流行的人工智能技术。随着计算机硬件的飞速发展,人工智能作为一门新兴技术越来越受到人们的欢迎。特别是以神经网络为代表的机器学习算法在石油工业技术中有着悠久的历史,它解决了石油专业中的许多问题,受到许多石油工程师的青睐。机器学习主要通过神经网络分类、k近邻分类(KNN)和多分辨率图聚类(MRGC)等数学算法对测井曲线进行聚类分析,然后结合岩心分析和切削数据建立电相与岩相的对应关系。由于该方法基于连续测井数据,因此具有较强的可扩展性,并且易于从未取芯井中学习(Xinlei et al. 2017)。在本文中,我们描述了一种新的工作流程,该流程基于在多个油田收集的电相分类和岩心数据,从常规测井中预测连续渗透率。该方法首先利用MRGC对取心剖面测井曲线的电相进行分类;其次,利用KNN算法将电相分类结果学习到未取芯剖面;最后,建立了基于电相约束的渗透率模型。与神经网络分类相比,MRGC具有运算速度快、运算结果稳定等优点。算法中的Neighbor Index (NI)参数可以快速对样本数据进行分类,Kernel Representative Index (KRI)参数可以从多次分类的结果中选择最优的类(Yunjiang et al. 2018;Ting et al. 2018)。研究区由13个油田组成,具有相同的沉积环境和矿物学特征。因此,这些油田的测井响应具有相似的特征。这些油田共采集了2122个岩心样品,并对同一口井进行了三重组合测井。根据常规岩心分析和测井特征分析,将测井响应划分为6个电相。然后利用岩心数据为每个电相建立渗透率模型,并用于在没有岩心数据的情况下对新井进行预测。利用提出的思想,对研究区一口生产井的渗透率和产能进行了重新估计。与常规方法相比,相约束渗透率与岩心测量的拟合性更好。由于渗透率的提高,该工作流程计算的产能指数与钻柱测试(DST)的估算结果相吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Permeability and Productivity Estimation with Electrofacies Classification and Core Data Collected in Multiple Oilfields
In the industry, it is a common practice to estimate continuous permeability by establishing a porosity-permeability relationship (poroperm) from conventional core analysis. For each new oilfield, core data is required to build a permeability model for this particular field. Due to reservoir heterogeneity, core derived poroperm can sometimes lead to biased predictions. This is particularly true for oilfields where core samples are scarce or provide a poor coverage of the reservoirs. Improving the accuracy of permeability models in these oilfields is key to better productivity estimation in the oilfield development planning. In 1984, Hearn et al. first proposed the concept of flow unit while studying Shannon reservoir in HartogDraw oilfield, Wyoming, USA. Since Hearn put forward the concept of reservoir flow unit, various Electrofacies classification methods have been proposed by different scholars (Hearn et al. 1984). Generally they can be divided into two categories. One is geological research method, which mainly uses geological cuttings and routine core analysis to calculate flow zone index (FZI) for reservoir classification (Xinlei et al. 2017; Elphick et al. 1999; Kohonen et al. 1982). This method improves the accuracy of permeability evaluation to a certain extent, but it mainly relies on routine core analysis data. Due to poor ductility, this method has certain limitations in the classification of uncored reservoirs. The other is the relatively popular artificial intelligence technology in the oil industry in recent years. With the rapid development of computer hardware, artificial intelligence as a new technology is becoming more and more popular. In particular, the machine learning algorithm represented by neural network has a long history in petroleum industry technology, which solves many problems in petroleum specialty and is favored by many petroleum engineers. Machine learning classifies electrofacies mainly by clustering analysis of logging curves through mathematical algorithms such as neural network classification, K-nearest neighbor classification (KNN) and Multi-Resolution Graph based Clustering (MRGC), and then the corresponding relationship between electrofacies and lithofacies is established by combining core analysis and cutting data. Since this method is based on continuous well logs, it has strong extensibility and is easy to learn from uncored wells (Xinlei et al. 2017). In this paper, we describe a novel workflow that predicts continuous permeability from conventional well logs, based on Electrofacies classification and core data collected in multiple oilfields. In this method, firstly, the MRGC is used to classify electrofacies of the logging curves in coring sections. Secondly, KNN algorithm is used to learn the results of electrofacies classification into uncored sections. Finally, the permeability model based on the electrofacies constraint is established. Compared with the neural network classification, the MRGC has the advantages of fast operation speed and stable operation results. The Neighbor Index (NI) parameter in the algorithm can quickly classify the sample data, and the Kernel Representative Index (KRI) parameter can select the optimal class from the results of multiple classifications(Yunjiang et al. 2018; Ting et al. 2018). Our study area consists of 13 oilfields with the same depositional environment and mineralogy. As a result, well log responses in these oilfields have similar characteristics. A total of 2122 core samples were collected in these oilfields and triple combo well logs are also available in the same wells. Based on routine core analysis and log feature analysis, we divide log responses into 6 electrofacies. Permeability models are then established for each electrofacies using core data and are used to make predictions in new wells without any core data. Using the proposed idea, we re-estimated the permeability and productivity in a producer well in the study area. The facies constrained permeability shows a much better match with core measurement compared to conventional methods. As a result of the improved permeability, the productivity index calculated by the workflow matches that estimated by the Drill Stem Test (DST).
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