In recent years, petroleum exploration in the Carboniferous volcanic rock reservoirs in the Junggar Basin has been the focus of important petroleum energy development in western China. The lithologic identification of volcanic rock reservoirs seriously restricts the accuracy of reservoir prediction and affects the success rate of oil exploration. Different types of volcanic rocks have different petrological characteristics and mineral assemblages, especially affected by the depositional environment. The volcanic rocks in different regions have their own uniqueness. This paper takes the Carboniferous volcanic reservoirs in Xiquan block, Beisantai Oilfield, Junggar Basin as the research target. Through a large number of core observations, casting slices, scanning electron microscopy, and X-ray diffraction methods, the Carboniferous volcanic rocks are analyzed. The petrology, pore characteristics, physical properties, and diagenetic evolution history of the reservoir are analyzed. The study shows that the volcanic facies in the Xiquan block can be divided into explosive facies, overflow facies, and volcanic sedimentary facies, among which the explosive facies is subdivided into empty subfacies (volcanic breccia-breccia tuff combination) and thermal base wave subfacies (tuff). The lithology of the reservoir is pyroclastic rock and volcanic lava, belonging to medium-porous and ultralow permeability reservoirs, and the storage space can be divided into three types: primary pores, secondary pores, and fractures. The lithology of key exploration is breccia tuff, followed by breccia tuff and volcanic breccia.

Block Yingmai 7 is structurally located in the western segment of the southern margin of the Kuqa Depression in the Tarim Basin. In the foreland basin, huge continental Mesozoic and Cenozoic strata have been deposited. In recent years, the Paleogene bottom sandstone section has been the main oil and gas exploration horizon. In order to further improve the oil and gas exploration effect of the Paleogene bottom sandstone in the study area and improve the accuracy of reservoir prediction, based on the related theory of sedimentology and petrology, this paper analyzes and studies the cores obtained by drilling in the Paleogene through laboratory core analysis experiments. The study shows that the Paleogene bottom sandstone is a braided river delta sedimentary system, and the sedimentary microfacies are mainly underwater distributary channels. The sandstone type is mainly light gray lithic feldspar fine sandstone, the sandstone debris particle sorting degree is medium-good, and the roundness is sub-edge-sub-circular. The microscopic characteristics of the reservoir are large pore throat radius, low displacement pressure, mainly intergranular pores, and good pore connectivity. It is a thin-layered mesoporous, medium-permeable, large-throat, and strongly heterogeneous reservoir. Finally, a classification evaluation table of reservoirs in block Yingmai 7 is established, and it is considered that the reservoirs of types I and II are enriched areas of oil and gas resources. The research results provide evidence for the prediction of oil and gas reservoirs.

In order to highlight the significance of deep learning in modern education, this article will explore the definition and features of SPOC blended learning. SPOC mixed learning, which offers flexibility, customisation, and involvement, blends offline classrooms with small-scale, restricted online courses. In order to enhance teaching efficacy and attain the best possible distribution of educational resources, this model makes full use of the benefits of both online and traditional classroom instruction. In order to demonstrate the beneficial effects of SPOC blended learning mode on enhancing student learning effectiveness and developing inventive skills, the study aims to develop an evaluation model for the promotion of deep learning through this mode. At the same time, encourage the further development of educational reform by offering specific recommendations and direction to educators.

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.

As the driving energy to deal with the decrease of interlayer pressure caused by continuous oil production, the layered water injection technology has the characteristics of inhibiting the decrease of oil production and slowing down the increase of oil/gas ratio. In engineering, water injection technology is often used to improve the properties of crude oil, such as excessive viscosity, weak liquidity, and depleted storage, to avoid the formation of dead oil. Injecting appropriate amount of water into different production horizons can effectively maintain the formation water injection pressure, improve the sustainable development speed of the oilfield, ensure the oil production and effectively control the production cost. It is of great value to petroleum engineering and has been widely concerned by the industrial and academic circles at home and abroad. With the continuous development of oilfields over the years, most oilfields have become high‐water‐cut oilfields. Through the existing layered water injection technology, there are defects such as high labor cost, low operating efficiency, and long commissioning cycle. The ratio of water injection cost to constant increase gradually decreases, and the technical shortcomings become more and more obvious, which is difficult to meet production needs. It is urgent to study and optimize water injection technology scheme to meet oilfield production and technology iteration. In recent years, electronic technology, communication technology, automatic control technology, and other advanced production technology applied to geological exploration, logging technology fields such as engineering, oilfield development is towards integration and intelligent direction, which makes the advanced control and real‐time communication intelligent power precision, and the layered water injection technology is possible. This paper summarizes the development history and status quo of oil recovery stratified water injection technology at home and abroad and points out that there are technical bottlenecks and development limitations in the development of water injection technology at present. Focusing on the current hot spots of intelligent oil recovery stratified water injection technology, the advantages and disadvantages of various intelligent water injection technology are compared and analyzed. It provides a certain theoretical reference value for the theoretical research and engineering application of intelligent stratified water injection technology to the equipment design and production of oilfield production and oil recovery technology research institutes and technology and equipment manufacturers.