This paper will thoroughly examine how machine learning can improve the diagnosis of deep well pumps by analyzing the role and function of the pumps, dynamograms, sensor technologies, and diagnostic methods.Our analysis will provide insights into modern techniques and approaches for enhancing the performance and reliability of oil production systems, targeting cost reduction and increased operational efficiency.
... complex
patterns in large datasets.
66
3.XGBoost (Extreme Gradient Boosting): XGBoost is an advanced algorithm that
employs boosting techniques to enhance model accuracy. Known for its speed and
efficiency in solving classification problems, XGBoost can achieve high accuracy even
with large ...
... the organization of the tests into groups
GroupA GroupB GroupC Group D
ML algorithms Decision tree, Random forest and XGBoost AutoML Decision tree, Random forest and XGBoost Decision tree
Number of instances per class 30 30 90 180
Test set size 50,098 50,098 50,098 50,098
Number of tests 9 ...
... Transform and sparse multi-graph regularized extreme learning
10. Wang, C.; Deng, C.; Wang, S. Imbalance-XGBoost: Leveraging weighted and focal
losses for binary label-imbalanced classification with XGBoost. Pattern Recognit
11. Olson, R.S.; Moore, J.H. TPOT: A Tree-based Pipeline Optimization Tool for ...
Maja Trikić. Application of machine learning for diagnosing the operation of a deep well pump in oil production, 2024
