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Abstract: To address the issue of global climate change, China proposed to strive to achieve the goal of peaking carbon emissions before 2030. Sichuan Province is a major province in western China and has a significant demonstration role in the western region. Therefore, taking Sichuan Province as an example, historical data on carbon emissions and their influencing factors from 1997 to 2021 were collected by querying the website of the National Bureau of Statistics and the CEADs. The paper analyzed the carbon emission intensity in Sichuan Province, selected 7 important influencing factors by calculating the Pearson correlation coefficient between the influencing factors and carbon emissions and predicted their future values by EViews. Then, selected the optimal number of nodes through experiments, established a BP neural network model (7-3-1 structure) and predicted the carbon emissions of Sichuan Province from 2022 to 2035. The results showed that the BP neural network model had good performance, with correlation coefficient values of all samples higher than 0.99 and root mean square error RMSE of 7.6078 million tons. The prediction results showed that from 2022 to 2035, the carbon emissions in Sichuan Province would first increase and then decrease, with a peak of 281.2988 million tons in 2025.Abstract: To address the issue of global climate change, China proposed to strive to achieve the goal of peaking carbon emissions before 2030. Sichuan Province is a major province in western China and has a significant demonstration role in the western region. Therefore, taking Sichuan Province as an example, historical data on carbon emissions and their inf...Learn More
Abstract: Under the background of global energy transformation and environmental protection, the application of artificial intelligence technology has become an important trend in oil and gas field development industry. However, how to effectively utilize artificial intelligence technology to improve the efficiency and safety of oil and gas development, while addressing the environmental and economic issues it brings, is a major question that researchers need to consider. Based on the actual needs of oil and gas exploitation, the basic principles and methods of deep learning are studied, and the main models and training methods of deep learning are introduced. The basic process of oil and gas field development is described in detail, and the realization steps and principles of depth learning optimization model for oil and gas field development are studied. The main challenges of depth learning in oil and gas field development are studied, including data security, model complexity, computing resource demand and so on. The results show that as a powerful artificial intelligence tool, deep learning has great potential to improve the efficiency and security of oil and gas exploitation, but it still faces some challenges. Therefore, future research should pay more attention to these problems to promote the application of deep learning in oil and gas development.Abstract: Under the background of global energy transformation and environmental protection, the application of artificial intelligence technology has become an important trend in oil and gas field development industry. However, how to effectively utilize artificial intelligence technology to improve the efficiency and safety of oil and gas development, whil...Learn More
Abstract: Tight sandstone gas reservoir has poor physical properties and great changes in gas and water distribution, foam drainage gas production technology is the key to realize economic development. Based on the geological characteristics of tight sandstone gas reservoir, comprehensively considering geological factors such as source rock and reservoir control, sand body structure, porosity, permeability, and combined with remaining recoverable reserves, dynamic and static evaluations, fluid accumulation characteristics, a refined well selection method has been formed and foam drainage technology has been established based on the concept of integrated geological-engineering. The on-site test results of the geological engineering integrated foam drainage technology show that the cumulative gas production increasen from 25 wells exceeds 100×104m3, with an effective rate of 96%. Geological engineering multi factor analysis shows that tight sandstone gas reservoirs with open-flow rate greater than 10×104m3/d, porosity between 6-10%, permeability between 0.5-1mD, and gas saturation between 50-60% have good production increase effects. The foam drainage technology based on the concept of geological engineering integration provides technical reserves for the scientific and effective exploration and development of tight sandstone gas reservoir, and helps to achieve the beneficial development of the reservoir.Abstract: Tight sandstone gas reservoir has poor physical properties and great changes in gas and water distribution, foam drainage gas production technology is the key to realize economic development. Based on the geological characteristics of tight sandstone gas reservoir, comprehensively considering geological factors such as source rock and reservoir con...Learn More
