Prediction of shear wave velocity in shale reservoirs based on machine learning: a case from the Permian Fengcheng Formation, Mahu Depression

As an extremely important target for unconventional oil and gas resources exploration at present, shale reservoir differs significantly from conventional clastic and carbonate reservoirs due to their diverse mineral composition, complex pore characteristics, and severe heterogeneity, which makes the conventional theoretical petrophysical models not accurate enough to characterize shale reservoirs. For this reason, machine learning and deep learning methods are introduced to construct a more intelligent petrophysical modeling process, which uses a data-driven approach. And taking the shale reservoirs of the Permian Fengcheng Formation in Mahu Depression of Junggar Basin as an example, we achieve high accuracy Shear wave velocity prediction based on conventional well logs, and the mean relative error (MRE) of prediction is reduced by 2.78-3.88% and the method has good applicability and generalization compared with conventional petrophysical model.