利用 k-nearest neighbor 协作滤波法估算测井数据中的缺失值

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Min Jun Kim , Yongchae Cho
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引用次数: 0

摘要

测井数据提供了关键的地下信息,对岩性评估和储层特征描述至关重要。然而,由于技术问题,测井数据在某些深度区间可能会有缺失值,这对数据分析不利。最好的方法是通过重新测井来重新获取缺失数据,但这会增加运营成本。因此,需要一种具有成本效益的方法来恢复丢失的数据,以解决这一问题。我们提出了一种利用协同过滤对缺失测井数据进行估算的方法,协同过滤是一种广泛用于向用户推荐新项目的算法。虽然协同过滤主要用于推荐系统,但其基本原理允许我们利用它来帮助预测丢失的测井数据。我们将该方法应用于从挪威附近北海获得的测井数据集。结果表明,协同过滤算法有可能成为一种强大的缺失测井数据估算方法,但也存在一些需要解决的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Imputation of missing values in well log data using k-nearest neighbor collaborative filtering

Well log data provide key subsurface information, which is crucial for lithology evaluation and reservoir characterization. However, due to technical issues, well log data may contain missing values at certain depth intervals, which can be detrimental for data analysis. The best method is to reacquire the missing data by relogging, but this increases operational costs. Thus, a cost-efficient method for restoring the lost data is needed to overcome this issue. We propose an imputation method for missing well log data using collaborative filtering, a widely used algorithm for making new item recommendations to users. Although collaborative filtering is mainly used in recommendation systems, its fundamental principle allows us to utilize it to help make predictions for missing log data. The method is applied to a well log dataset obtained from the North Sea near Norway. The results show that the collaborative filtering algorithm has the potential to be a powerful imputation method for missing well log data, but there are some limitations that need to be addressed.

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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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