An artificial neural network model for the prediction of the bulk density log

Formation evaluation requires the analysis of well-log data that are not available for all wells, especially old wells. In the case of an unstable borehole well, the formation bulk density (RHOB) log cannot reflect the true values of the formation density, and we cannot record it another time. However, the use of artificial intelligence approaches, such as artificial neural networks (ANNs), which are strong tools for real-time prediction without additional costs, can provide acceptable results with high accuracy. This work, by leveraging ANNs for continuous and reliable in-situ prediction from other wireline logs, enables the prediction of RHOBs without additional costly logging operations and overcomes the limitations of traditional methods in terms of availability, accuracy, cost, and real-time applicability. The dataset of two wells was used to train and test the model. Unseen data from another well within the same field were utilised for validation. The findings revealed that the predicted RHOB values significantly matched the actual values with a coefficient of determination of 0.98 and root-mean-square error of 0.12. This score confirms the generalisation capability of the model, overcoming, thus, limitations of traditional logging methods that require direct measurement and stable boreholes. This demonstrates the advantage of ANN in filling data gaps and enhancing well-log interpretation under challenging conditions.