Coordinated Development of Population, Resources, Environment, Economy, and Society under Engineering Management Combined with Bilevel Optimization Model

The in-depth analysis of the strategies for the coordinated and continuous development of population, resources, environment, economy, and society based on the engineering management model is highly important for the sustainable development of the regional economy and society. In this article, a population-economy-resources-environment bilevel optimization model is established based on the economic and social development in a provincial region. The method of bilevel optimization is adopted to introduce the specific bilevel optimization model. The concept and objectives of the bilevel optimization are explained, and its corresponding technical applications are described. In this article, the development in coordinated economic and social development of population, resources, and environment is analyzed and compared based on the bilevel optimization model. In particular, the evolution and changes before and after the implementation of engineering management are studied. Through the results, it can be observed that after the implementation of project management, the coefficient of industry location has presented a downward trend, and the coordinated development of population, resources, environment, economy, and society has become more coordinated.

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

In this paper, a high-level semantic recognition model is used to parse the video content of human sports under engineering management, and the stream shape of the previous layer is embedded in the convolutional operation of the next layer, so that each layer of the convolutional neural network can effectively maintain the stream structure of the previous layer, thus obtaining a video image feature representation that can reflect the image nearest neighbor relationship and association features. The method is applied to image classification, and the experimental results show that the method can extract image features more effectively, thus improving the accuracy of feature classification. Since fine-grained actions usually share a very high similarity in phenotypes and motion patterns, with only minor differences in local regions, inspired by the human visual system, this paper proposes integrating visual attention mechanisms into the fine-grained action feature extraction process to extract features for cues. Taking the problem as the guide, we formulate the athlete’s tacit knowledge management strategy and select the distinctive freestyle aerial skills national team as the object of empirical analysis, compose a more scientific and organization-specific tacit knowledge management program, exert influence on the members in the implementation, and revise to form a tacit knowledge management implementation program with certain promotion value. Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a group can be represented by individual representations, and the relationship between individual behaviors can be analyzed by modeling the relationship between individual representations. The performance improvement of the method on mismatched datasets is comparable between the long-short time network based on temporal information and the language recognition method with high-level semantic embedding vectors, with the two methods improving about 12.6% and 23.0%, respectively, compared with the method using the original model and with the i-vector baseline system based on the support vector machine classification method with radial basis functions, with performance improvements about 10.10% and 10.88%, respectively.

Development of well complexity calculator and its integration into standard well engineering management system/well delivery system

Oil and gas well drilling is the most important and complex task for oil and gas exploration. It is not necessary that design and execution complexity remain the same for two different wells even in the same field. It is possible to have a very complex well to drill after a very straightforward simple well being drilled earlier in the same field. Making correlation or comparison of any of the two or more than two oil and gas drilling wells is an ongoing debate in the petroleum industry. Generally, companies compare the oil and gas drilling wells on a single or two parameters, for example: time versus depth, directional trajectories, well cost and/or other single factors in disengagement of one another. In order to compare two different types of oil and gas drilling wells, having distinctive design, drilling and fluid program and challenges, a scientific rating system is required, which can relate various wells with one another. In this research paper, a calculator named Well Complexity Calculator has been developed to measure the complexity of the oil and gas well drilling by using different parameters. All these parameters are commonly affecting the drilling program and its execution. Secondly, a methodology is designed for integration of Well Complexity Calculator into standard Well Engineering Management System/Well Delivery System for better execution of drilling program. Fifty-one (51) oil and gas drilling well complexity parameters have been utilized to develop Well Complexity Calculator, where they are categorized into three main complexities types named Design Well Complexity, Geological Well Complexity and Project Well Complexity. Design and Geological Well Complexities combine to form Drilling Well Complexity, and then Drilling Well Complexity and Project Well Complexity combine to form Well Complexity. Median, Mode and Monte Carlo simulation techniques were chosen to develop the calculator where Median showed best suited results and was accordingly chosen for the final calculator. Sixty-six (66) actual oil and gas wells’ camouflaged drilling data were used to analyze and fine tune the developed Well Complexity Calculator. Output complexities of these wells were falling in different complexity levels. Moreover, it was seen that the number of low, high and medium complexity wells was different for Design, Geological, Project, Drilling and Well Complexities which is in line with the real-world scenario.The findings and the output Well Complexity Calculator can be very useful at any stage from initial planning to close-out of a well. Without the application of a system like Well Complexity Calculator, wells are categorized as low, medium or high complexity based on either two to three major parameters or based on qualitative assessment of team involved in the project. Here, step-by-step procedure is developed and explained by which any company involved in Drilling and Well Operations can develop their own Well Complexity Calculator and then accordingly integrate it into their Well Engineering Management System/Well Delivery System.