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Logging Lithology Discrimination in the Prototype Similarity Space With Random Forest | IEEE Journals & Magazine | IEEE Xplore

Logging Lithology Discrimination in the Prototype Similarity Space With Random Forest


Abstract:

Borehole lithology discrimination is the foundation for formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimi...Show More

Abstract:

Borehole lithology discrimination is the foundation for formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimination methods, such as borehole core and drilling cutting analysis, are unable to widely apply, while logging lithology interpretation provides an alternative solution for this task. To mitigate the influence of subjective bias, several machine learning algorithms, such as neural network, support vector machine, decision tree, and random forest (RF), have already been applied for logging lithology interpretation. However, the vast majority of preceding studies are simple applications that directly apply classification algorithms to the raw input space formed by logging curve values, only limited studies involved feature extraction or learning space transformation. In this letter, we propose a hybrid algorithm that combines the mean-shift algorithm and the RF algorithm for borehole lithology discrimination in the prototype similarity space. Experiments on data collected from nine different areas demonstrate that the proposed algorithm has significant advantages in accuracy compared with other algorithms, which provides a considerable alternative way for further machine learning-assisted logging lithology interpretation.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 16, Issue: 5, May 2019)
Page(s): 687 - 691
Date of Publication: 06 December 2018

ISSN Information:


I. Introduction

For petroleum exploration and engineering, the discrimination of the borehole lithology is extremely important since it is the foundation for further formation evaluation and reservoir characterization. Through the observation and analysis of borehole cores or drilling cuttings, lithology conclusions of subsurface formation can be obtained directly. However, borehole cores are generally expensive since drill cuttings have drawbacks in measuring depth error and thin-layer resolution. As a result, borehole lithology columns obtained by these methods cannot be applied widely.

References

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