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July 15, 2026Journal of Petroleum Exploration and Production TechnologyOpen Access

Application of supervised machine learning for predicting and classifying the lithology type of reservoir rock: the case study of Iraq

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Authors

MMMustafa Ali Mijlad MohammedawiATAshkan TaheriMAMohammadkazem Amiri

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Overview

Randomized trial shows automated lithology classification in Iraq, suggesting improved hydrocarbon exploration efficiency.

Key Points

  • This research aims to enhance lithology classification accuracy during subsurface exploration using machine learning techniques.
  • Utilized a dataset from Majnoon oil field in Iraq.
  • Evaluated four machine learning algorithms: decision tree, support vector machine, K-nearest neighbors, and neural network.
  • Performed feature importance analysis and error analysis using Percent Bias.
  • Decision tree model achieved the highest accuracy of 99.69% on validation data and 100% on test data.
  • KNN, SVM, and NN had accuracies of 98.62%, 99.08%, and 99.39%, respectively.
  • For the sandstone class, decision tree precision was 99.7% and recall was 99.2%.

Cite This Study

Mohammedawi et al. (2026) studied this question.

synapsesocial.com/papers/6a5723fd88b21df87548091bhttps://doi.org/10.1007/s13202-026-02182-0
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identification of Lithology from Well Log Data Using Machine Learning2024 · 2 citations
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  5. 5Enhanced Lithology Classification in Well Log Data Using Ensemble Machine Learning Techniques2024 · 1 citations