Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga oilfield, Algeria

Porosity is a key variable for hydrocarbon reservoirs evaluation. It can be directly determined in laboratory tests using core samples or calculated indirectly from well logs. However, these methods are expensive and time consuming affecting the cost of supply of oil produced. Monitoring drilling variables in real time as a borehole is drilled can provide a cheap, initial insight to various rock formation properties. Investigating the relationships between rock porosity and drilling variables is therefore important. This study applies machines learning (ML) relate rock porosity to a range of measured drilling variables in several types of reservoir. Four ML models are evaluated: K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGBoost). A measured drilling and porosity dataset from the Rhourd Chegga oilfield (SE Algeria) is compiled from its multi-zone reservoir system. The drilling variables from nine vertical wellbores considered are: weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration and its inverse, pump rate and bit depth. The recorded data is derived from three distinct reservoir zones: Trias (T1), Rhourd Chegga sandstone (RDC), and the Quartzite Hamra tight reservoir (QH). Porosity predictions were derived in two ways: (1) for each reservoir separately; (2) for the three reservoirs combined. Prediction error analysis was conducted in terms of the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE). The XGBoost model yielded the best porosity predictions for the individual and combined reservoirs. Prediction errors generated for reservoir T1 were (R2 0.865, RMSE 0.866, MAE 0.692); for reservoir RDC were R2 0.935, RMSE 0.848, and MAE 0.772; and, for reservoir QH were R2 0.743, RMSE 0.794, and MAE 0.573. For the combined (multi-zone) reservoir (T1+RDC + QH), porosity predictions were less accurate, and the XGBoost model generated prediction errors of R2 0.76, RMSE 1.259, and MAE 1.027.

Automated summary

This study investigates the relationship between rock porosity and drilling variables using machine learning models, specifically the K-nearest neighbor, random forest, support vector regression, and eXtreme Gradient Boosting models. The XGBoost model provides the most accurate porosity predictions for the individual and combined reservoirs.