Uncertainty Quantification of Estimated Ultimate Recovery Prediction in Shale Reservoirs Using Quantile Random Forest with Prediction Interval Calibration

This study uses quantile regression forest (QRF), which is based on random forest (RF), to estimate the estimated ultimate recovery of shale oil in the Eagle Ford Shale and quantify uncertainty.Conventional prediction intervals using the P90 and P10 quantiles from the RF quantile model yielded higher prediction interval coverage probability (PICP) than the target of 80%, leading to uncertainty overestimation.A calibration method based on modified conformalized quantile regression (CQR) was used for the prediction intervals to solve this problem.By generating adjusted P90 and P10 values, the calibrated model ensured a PICP of 80% and a more appropriate uncertainty representation.The median prediction remained nearly unchanged before and after calibration, confirming the structural stability of the model.The proposed method enhances the reliability and practical applicability of uncertainty quantification in forecasting production from shale reservoirs.