Uncertainty and Explainable Analysis of Machine Learning Model for
  Reconstruction of Sonic Slowness Logs
Hua Wang Yuqiong Wu Yushun Zhang Fuqiang Lai Zhou Feng Bing Xie Ailin Zhao
Logs are valuable information for oil and gas fields as they help to
determine the lithology of the formations surrounding the borehole and the
location and reserves of subsurface oil and gas reservoirs. However, important
logs are often missing in horizontal or old wells, which poses a challenge in
field applications. In this paper, we utilize data from the 2020 machine
learning competition of the SPWLA, which aims to predict the missing
compressional wave slowness and shear wave slowness logs using other logs in
the same borehole. We employ the NGBoost algorithm to construct an Ensemble
Learning model that can predicate the results as well as their uncertainty.
Furthermore, we combine the SHAP method to investigate the interpretability of
the machine learning model. We compare the performance of the NGBosst model
with four other commonly used Ensemble Learning methods, including Random
Forest, GBDT, XGBoost, LightGBM. The results show that the NGBoost model
performs well in the testing set and can provide a probability distribution for
the prediction results. In addition, the variance of the probability
distribution of the predicted log can be used to justify the quality of the
constructed log. Using the SHAP explainable machine learning model, we
calculate the importance of each input log to the predicted results as well as
the coupling relationship among input logs. Our findings reveal that the
NGBoost model tends to provide greater slowness prediction results when the
neutron porosity and gamma ray are large, which is consistent with the
cognition of petrophysical models. Furthermore, the machine learning model can
capture the influence of the changing borehole caliper on slowness, where the
influence of borehole caliper on slowness is complex and not easy to establish
a direct relationship. These findings are in line with the physical principle
of borehole acoustics.