通过使用机器学习的半监督回归聚类构建 S 波测井:北海油田案例研究

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
João Rafael B. Da Silveira , Jose J.S. De Figueiredo , Celso R.L. Lima , Jose Frank V. Gonçalvez , Marcus L. Do Amaral
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引用次数: 0

摘要

根据测井记录准确预测 S 波速度对于了解地下地质构造和油气储层至关重要。机器学习技术(包括聚类和回归)已成为间接估算 S 波测井和其他岩石属性的有效方法。在这项研究中,我们采用聚类算法来识别测井数据集之间的相似性,包括深度、声波、孔隙度、中子和表观密度,从而有助于发现不同测井之间的相关性。这些已确定的相关性为使用新颖的半监督方法预测 S 波值奠定了基础。我们的方法将聚类(特别是 k-means 聚类)与不同类型的回归器相结合,包括最小二乘回归 (LSR)、支持向量回归 (SVR) 和多层感知器 (MLP)。我们的研究结果表明,与传统回归方法相比,这种综合方法具有更优越的性能。我们使用各种参数和非参数回归技术对我们的方法进行了验证,不仅在训练区域内的油井上,而且在研究区域外的油井上都展示了其有效性。与未进行聚类的预测相比,我们的 R2 分数指标有了明显改善,从 2.22% 到 6.51%,平均平方误差 (MSE) 至少减少了 31%。这项研究强调了机器学习技术在准确预测 S 波速度和其他岩石属性方面的潜力,从而增强了我们对地下地质和油气藏的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields

Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the R2 score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.

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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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