Abstract 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.

中文翻译：

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使用极端梯度提升 (XGBoost) 从钻井和随钻测井数据生成伪密度日志

摘要 密度测井数据是用于通过量化和限定井眼中的岩性属性来表征储层的关键物理属性之一。密度测井最常在钻井后通过地球物理电缆测井技术获得。然而，在大斜度井中可能难以执行电缆测井，并且诸如随钻测井 (LWD) 之类的替代数据采集可能非常昂贵。本文描述了使用瞬时钻井属性和机器学习算法极端梯度提升 (XGBoost) 来生成伪密度日志的过程。平均绝对误差 (MAE) 和均方根误差 (RMSE) 用作评估指标。案例研究使用来自澳大利亚苏拉特盆地的六口煤层气（或煤层气）井的数据进行。输入包括钻井数据 [钻压 (WOB)、每分钟转数 (RPM)、扭矩、真实垂直深度 (TVD) 和钻速 (ROP)] 和 LWD 数据（即自然伽马射线和孔径）。大多数井的伪密度测井MAE在0.08～0.11 g/cc之间（4井除外0.16 g/cc），平均误差率小于5%。发现 TVD、伽马射线、ROP 和孔径是四个最重要的特征。进一步的实验表明，具有这四个重要特征的模型与具有所有特征的模型的性能几乎相同。所提出的机器学习方法可以通过实时从正在进行的 LWD 和钻井数据生成伪密度测井来帮助石油工程师和地质学家进行储层表征。