The injection from a well to other wells can be difficult in extralow-permeability oil reservoirs. In order to address this issue, a method of cyclic huff-n-puff with surfactants based on complex fracture networks for a single horizontal well was proposed and then investigated in terms of the effects of injection and fracture parameters on the oil recovery in water-wet extralow-permeability models. Firstly, the interfacial tension (IFT) and contact angle with different surfactant concentrations were measured to determine the basic properties of the surfactants. Then, the experiments of huff-n-puff with surfactants at different threshold injection pressures and soaking time were carried out to determine the oil increasing effects and analyze the pore-scale (micropores, mesopores, and macropores) mechanisms by combining the technology of nuclear magnetic resonance (NMR), which showed that the recovery increased with threshold injection pressure mostly in mesopores and macropores, while that increased with soaking time mostly in micropores. Eventually, the experiments of cyclic huff-n-puff based on different fracture distributions were conducted in six plate-fractured models to investigate the effects of surfactants, primary fracture, and secondary fracture on each cycle of huff-n-puff. Cyclic huff-n-puff with surfactants assisted by complex fracture networks including both primary and secondary fractures would bring to a higher oil recovery. However, other methods should be taken after several cycles of huff-n-puff due to the rapid reduction of oil recovery of each cycle. The findings for the proposed method should provide a meaningful guide to the development of extralow-permeability oil reservoirs.
By analyzing the permeability controlling factors of tight sandstone reservoir in Wuhaozhuang Oil Field, the permeability is considered to be mainly controlled by porosity, clay content, irreducible water saturation and diagenetic coefficient. Because the conventional BP algorithm has its drawbacks such as slow convergence speed and easy falling into the local minimum value, an improved three-layer feed-forward BP neural network model is built by MATLAB neural network toolbox to predict permeability according to the four permeability controlling factors, while studying samples of model are selected based on the representative core analysis data. The simulation based on improved neural network model shows that the improved model has a faster convergence speed and better accuracy. The consistency between model prediction value and lab test value is good and the mean squared error is less. Therefore, the new model can meet the needs of the development geology research of oil field better in the future.
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