Abstract:

Micro-minor fractures represent a key type of reservoir space in the thin biolimestones of the Shahejie formation in the Wangxuzhuang oilfield. Due to the lack of effective measurement methods and characterization techniques, it is challenging to understand these fractures, thereby hindering accurate prediction of fluid flow capacity during oil and gas development. By integrating the data of core samples, thin sections, CT scanning, formation micro-resistivity imaging (FMI) logging, and conventional logging, the development of micro-minor fractures was investigated. With a PSO-BP neural network, the fracture development and distribution in the fractured reservoirs of the study area were predicted. Then a discrete fracture network modeling approach was proposed to simulate the spatial distribution of these fractures. The results show that the biolimestone with developed micro-minor fractures exhibits significant amplitude differences between shallow and deep lateral resistivity readings. Micro-minor fractures are well developed in the biolimestones in the study area, which play a crucial role in improving reservoir physical properties and waterflood response directions. These fractures are controlled by fault zones and sedimentary microfacies of the biolimestone. Numerical simulation confirms that the dual-porosity dual-permeability model incorporating micro-minor fractures can provide a better fit for the dynamic behavior of oil-water relations.

Key words: Wangxuzhuang oilfield, Shahejie formation, biolimestone, micro-minor fracture, particle swarm optimization algorithm, BP neural network, discrete fracture network model