Reservoir evaluation using petrophysics informed machine learning: A case study

We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information can either be specific logging response equations or abstract relationships between logging data and reservoir parameters. We compare our method's performances using two datasets and evaluate the influences of multi-task learning, model structure, transfer learning, and petrophysics informed machine learning (PIML). Our experiments demonstrate that PIML significantly enhances the performance of formation evaluation, and the structure of residual neural network is optimal for incorporating petrophysical constraints. Moreover, PIML is less sensitive to noise. These findings indicate that it is crucial to integrate data-driven machine learning with petrophysical mechanism for the application of artificial intelligence in oil and gas exploration. •A Petrophysics Informed Machine Learning (PIML) method is suggested for geophysical logging reservoir evaluation.The allowable error and weight of petrophysical mechanism loss function could impact model performance. PIML has better performance and robustness compared with pure data-driven model in reservoir parameter prediction.