Hydrogen Index (HI) and Oxygen Index (OI) are two critical parameters for assessing the hydrocarbon potential and depositional environment of any source rocks. The most common method to measure these values is to use programmed pyrolysis on drill samples. However, this method can be very time consuming, expensive, and in many cases much of the well bore may be overlooked due to biased sampling. Geochemical parameter predictions from wireline logs (i.e., Passey) have been used in the past to varying success. This is largely because petrophysical predictions often attempt to solve for linear regression solutions where this may not be the case. Here we evaluate the use of a Random Forest (RF) machine learning (ML) model to predict HI and OI from four wells from the offshore east coast of Newfoundland, Canada. The model was trained and tested using programmed pyrolysis data, organic petrology techniques, and wireline logs for prediction. The model was evaluated using mean absolute error (MAE), root mean square error (RMSE), correlation of determination (R²), and Spearman's rank correlation (R₂). Excellent correlation coefficients were observed for RF model predictions for HI and OI that range 0.90 to 0.98 and 0.90 to 0.95 R² respectively. The MAE for HI and OI values range 17.30 to 52.48 and 2.82 to 12.79 respectively. The RMSE for HI and OI range 21.43 to 71.51 and 3.85 to 16.82 respectively. The Spearman's rank correlation for HI and OI range 0.87 to 0.97 and 0.90 to 0.96 respectively. This study confirms that the use of ML models can be extremely useful to predict geochemical parameter from wireline logs.

氢指数 (HI) 和氧指数 (OI) 是评估任何烃源岩的油气潜力和沉积环境的两个关键参数。测量这些值的最常用方法是对钻探样品使用程序热解。然而，这种方法可能非常耗时、昂贵，并且在许多情况下，由于采样有偏差，可能会忽略大部分井筒。来自电缆测井（即 Passey）的地球化学参数预测在过去已被使用，并取得了不同的成功。这主要是因为岩石物理预测通常试图解决线性回归解决方案，而实际情况可能并非如此。在这里，我们评估使用随机森林 (RF) 机器学习 (ML) 模型来预测加拿大纽芬兰近海东海岸四口井的 HI 和 OI。该模型使用程序化的热解数据、有机岩石学技术和用于预测的电缆测井进行了训练和测试。使用平均绝对误差 (MAE)、均方根误差 (RMSE)、测定相关性 (R² ) 和 Spearman 秩相关 (R ₂ )。对于 HI 和 OI 的 RF 模型预测，观察到极好的相关系数，其范围分别为 0.90 至 0.98 和 0.90 至 0.95 R ²。HI 和 OI 值的 MAE 范围分别为 17.30 至 52.48 和 2.82 至 12.79。HI 和 OI 的 RMSE 范围分别为 21.43 至 71.51 和 3.85 至 16.82。HI 和 OI 的 Spearman 等级相关分别为 0.87 至 0.97 和 0.90 至 0.96。这项研究证实，使用 ML 模型对于从电缆测井预测地球化学参数非常有用。