| dc.description.abstract | This thesis explored to what extent different supervised machine learning algorithms
can be used to label subsurface formations in wells. It was explored through empirical
study using wireline logs from the Johan Sverdrup field as inputs. The results from three
different machine learning models were compared with the addition of a benchmark model;
two LightGBM models, one LSTM model and a Logistic Regression model as a benchmark.
The data set consisted of 31 wells in the Johan Sverdrup field with a total of 406 666
labeled observations and the corresponding measured properties at different depth points
in the wells.
The two LightGBM models both performed better than the benchmark. The results
obtained from the neural network were significantly worse than both LightGBM models
and the benchmark. Due to time- and computational constraints, we were not able to
fully utilize the potential of the neural network (LSTM). Hence, additional tuning and
model stacking could potentially lead to improved results.
The best performing model was LightGBM 2, the model that utilized a stratified trainingand
validation split. Here, sequential observations from the same well were randomly
split across the training- and validation data. This model yielded an accuracy of 79.17%.
However, this model overfitted significantly to the training- and validation data. Further,
LightGBM 1, the model that utilized a customized stratified training- and validation
split, had a slightly lower accuracy of 77.58%. Here, all sequential observations from the
same well were kept in the same data set, which caused significantly less overfitting to the
training- and validation data. Based on this, we concluded that out of the models tested
in the thesis, LightGBM 1 had the highest potential to generalize on unseen data.
The classification accuracy of around 80%, and the insight gained from the interpretable
machine learning method, can be of great contribution and create significant value to
experts currently performing the labeling of the formations in a manual fashion.
Keywords – Machine Learning, Interpretable Machine Learning, SHAP, LightGBM,
Deep Learning, LSTM, Logistic Regression, Wireline Logs, Formation Prediction, Johan
Sverdrup, Stratigraphy | en_US |
