人工智能/机器学习

机器学习技术对岩屑岩性进行分类、量化

本文的作者描述了一个项目，旨在通过机器学习和人工智能技术，在岩性识别和岩性丰度的定量估计方面，实现岩屑描述任务的自动化。

每种岩性的训练模型预测示例。
图 1' 是每种岩性的训练模型预测示例。
资料来源：IPTC 22867

井场地质学家将大约 70% 的时间花在岩屑描述上。此外，通常会指派两到三名井场地质学家参与钻井活动，并在轮班结束时进行更换。机器学习（ML）和人工智能（AI）技术因其在预测速度、客观性和一致性方面的优势而有可能解决这些问题。作者的目标是利用这些技术自动完成插条描述任务。

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AI/machine learning

Machine-Learning Techniques Classify, Quantify Cuttings Lithology

The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.

Examples of trained-model prediction for each lithology.
Fig. 1—Examples of trained-model prediction for each lithology.
Source: IPTC 22867

Wellsite geologists spend approximately 70% of their time on cuttings descriptions. In addition, two or three wellsite geologists generally are assigned to a drilling campaign, to be replaced at the end of a shift. Machine-learning (ML) and artificial-intelligence (AI) techniques have the potential to solve these issues because of their advantages in prediction speed, objectivity, and consistency. The authors’ aim is to automate the task of cuttings descriptions with these techniques.

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