A permeability prediction method from well logs for tight sandstone reservoirs based on intelligent division of flow units

In the lake-delta depositional system, tight sandstone reservoirs are characterized by complex pore structures, diverse pore types, and low permeability, and log interpretation and formation evaluation is facing the challenges. Permeability is a key parameter for reservoir evaluation and productivity prediction, and traditional calculation methods from log interpretation are not accurate and cannot meet production requirements. Aiming at this problem, two closely related controls on reservoir permeability are analyzed: microscopic pore structure and macroscopic flow unit, and a new permeability prediction method based on rock type and flow zone indicator (FZI) are proposed. First, core experimental results are analyzed, rock types are determined, core FZIs are calculated, rock types are classified using the cumulative frequency method, and the permeability model for each rock type is constructed. Then, sensitive well logs are selected to form labels, and a deep neural network (DNN) is used to predict reservoir flow unit index (FZI). Finally, log porosities and FZIs are input into the model for each type to calculate permeability. The application in low-porosity low-permeability reservoirs in the HG Formation, the XH sag, China, shows good results with logarithmic error of 0.18, which is smaller than of other DNN methods. The new method includes both data-driven machine learning methods and mechanism-based or knowledge-driven physical model construction, which embodies the idea of data and model jointly driven intelligence, and significantly improved the accuracy of permeability evaluation of tight sandstone reservoirs. Furthermore, it is also referential to permeability prediction for tight sandstone reservoirs in other lake-delta sedimentary systems.