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.

January 1, 2024

Journal of Petroleum Technology

Fig. 1—Examples of trained-model prediction for each lithology.

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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