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 petrophysic...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.展开更多
基金supported by the Strategic Cooperation Technology Projects of CNPC and CUPB (ZLZX2020-03)National Key Research and Development Program (2019YFA0708301)+1 种基金National Key Research and Development Program (2023YFF0714102)Science and Technology Innovation Fund of CNPC (2021DQ02-0403).
文摘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.
