No abstract

No abstract

No abstract

No abstract

Nowadays, people’s demand for underground mineral resources is increasing, and geological disasters have occurred frequently in recent years. Geological disasters refer to geological effects or geological phenomena that are formed under the action of natural or man-made factors, causing loss of human life and property, and damage to the environment; such as landslides, collapses, mudslides, and ground subsidence. Under such a background, people must accelerate the exploration of complex geological structures. This paper is aimed at using the methods and concepts of deep reinforcement learning. Deep learning is to learn the inherent laws and representation levels of sample data. The information obtained during these learning processes is of great help to the interpretation of data such as text and images. In this way, the fine geology of complex fault-block reservoirs is modeled and studied. Geological structures and phenomena are discussed through convolutional neural network models and computer techniques. At the same time, the multitask bird recognition network is used to extract and classify geological images, so as to construct geological model maps with different spatial structures. Finally, the quality of the fault reconstruction model, the calculation of reservoir geological simulation reserves, and the evaluation of the water injection development effect of complex fault blocks are analyzed. In the evaluation of the development effect of water injection in complex fault blocks, comparing the relationship curve between the actual comprehensive water content and the oil recovery factor with the standard curve, the comprehensive water content of the initial block increased rapidly. Through timely and dynamic water allocation and comprehensive management, the water cut rising speed is controlled. The current comprehensive water cut of the reservoir is between 60% and 80%, the actual curve is between 25% and 35%, and the estimated waterflooding recovery is about 30%.