Synthetic well-log generation: New approach to predict formation bulk density while drilling using neural networks and fuzzy logic

Synthetic well-log generation using artificial intelligence tools is presented as a robust solution when the logging data are not available or partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. It is measured in the field using density log tool either while drilling by logging while drilling technique (LWD) or mostly by wireline logging after the formations are drilled because of the operational limitations during the drilling process. Therefore the objective of this study is to develop a predictive tool for estimating RHOB while drilling using artificial neural networks (ANN) and Adaptive network-based fuzzy interference systems (ANFIS). The proposed models used the drilling mechanical parameters as feeding inputs and the conventional RHOB log-data as an output. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), stand-pipe pressure (SPP) and rotating speed (RPM), are usually measured while drilling and their responses vary with different formations. A dataset of 2400 actual data points obtained from horizontal well in the Middle East is used for building the proposed models. The obtained dataset is divided into 70/30 ratios for training and testing the model respectively. The optimized ANN-based model outperformed the ANFIS-based model with correlation coefficient (R) of 0.95 and average absolute percentage error (AAPE) of 0.72 % between the predicted and the measured RHOB compared to R of 0.93 and AAPE of 0.81 % for the ANFIS-based model. These results demonstrated the reliability of the developed ANN model to predict the RHOB while drilling based on the drilling mechanical parameters. Afterwards, the ANN-based model is validated using unseen data from another well within the same field. The validation process yielded AAPE of 0.5 % between the predicted and the actual RHOB values which confirmed the robustness of the developed model as an effective predictive tool.