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A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset. (arXiv:2209.01607v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
This study presents machine learning models that forecast and categorize lost
circulation severity preemptively using a large class imbalanced drilling
dataset. We demonstrate reproducible core techniques involved in tackling a
large drilling engineering challenge utilizing easily interpretable machine
learning approaches.
We utilized a 65,000+ records data with class imbalance problem from Azadegan
oilfield formations in Iran. Eleven of the dataset's seventeen parameters are
chosen to be used in the classification of five lost circulation events. To
generate classification models, we …
arxiv case case study classification dataset events machine machine learning machine learning techniques study