Journal
INTERNATIONAL JOURNAL OF COAL GEOLOGY
Volume 220, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.coal.2020.103416
Keywords
Density logging; Pseudo density log; Machine learning; Drilling; Coalbed methane (CBM); Coal seam gas (CSG); Extreme Gradient Boosting (XGBoost)
Categories
Funding
- Arrow Energy through The University of Queensland Centre for Natural Gas
- APLNG through The University of Queensland Centre for Natural Gas
- Santos through The University of Queensland Centre for Natural Gas
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Density log data is one of the key physical attributes used for reservoir characterization by quantifying and qualifying lithological attributes in a wellbore. The density log is most often acquired by geophysical wireline logging techniques after drilling. However, wireline logging can be difficult to execute in highly deviated wells and alternative data acquisition such as logging-while-drilling (LWD) can be very costly. This paper describes the process of using instantaneous drilling attributes together with a machine learning algorithm, extreme gradient boosting (XGBoost), to generate a pseudo density log. The mean absolute error (MAE) and root mean square error (RMSE) are used as the evaluation metrics. Case studies are performed using data from six coalbed methane (or coal seam gas) wells in the Surat Basin, Australia. The inputs include drilling data [weight on bit (WOB), rotations per minute (RPM), torque, true vertical depth (TVD) and rate of penetration (ROP)] and LWD data (i.e., natural gamma ray and hole diameter). The MAE of pseudo density log for most wells is between 0.08 and 0.11 g/cc (except Well 4 is 0.16 g/cc), which results in an average error rate less than 5%. It is found that TVD, gamma ray, ROP, and hole diameter are four most important features. Further experiment shows that the model with these four important features has almost the same performance as the model with all features. The proposed machine learning methodology can assist petroleum engineers and geologists in reservoir characterization by generating pseudo density logs from ongoing LWD and drilling data in real time. It can potentially mitigate the need to run wireline logging tools after drilling or costly LWD techniques whilst drilling.
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