With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.
With the application and comprehensive development of big data technology, the need for effective research on cloud workflow management and scheduling is becoming increasingly urgent. However, there are currently suitable methods for effective analysis. To determine how to effectively manage and schedule smart cloud workflows, this article studies big data from various aspects and draws the following conclusions: Compared with the original JStorm system, the response time is shortened by a maximum of 58.26% and an average of 23.18%, CPU resource utilization is increased by a maximum of 17.96% and an average of 11.39%, and memory utilization increased by a maximum of 88.7% and an average of 71.16%. In terms of optimizing the dynamic combination of web services, the overall performance of both the MOACO and CCA algorithms is better than that of the GA algorithm, and the average performance of the MOACO algorithm is better than that of the CCA algorithm. This paper also proposes a cloud workflow scheduling strategy based on an intelligent algorithm and realizes the twotier scheduling of cloud workflow tasks by adjusting the combination strategy for cloud service resources. We have studied three representative intelligent algorithms (ACO, PSO and GA) and improved them for scheduling optimization. It can be clearly seen that in the same scenario, the optimal values of the different algorithms vary greatly for different test cases. However, the optimal solution curve is substantially consistent with the trend of the mean curve.
The transformation of new and old kinetic energy is a new requirement for the new economy when China enters into the new era. When mankind experienced mechanization, electrification, automation, and finally entered the era of digital industry, human wisdom based on information technology will become a new energy source for the new era. The trend of Digital, networked, automated and intelligent economic development has become the main driving force of energy innovation under the background of big data, while smart city construction, as a kind of infrastructure investment, assumes the function of upstream industry and social leading capital in the conversion of new and old kinetic energy. The experience of leading the construction of smart city of Weifang based on the NB-IoT unified standards, sequential upgrading development, and people’s livelihood guidance has further proved that smart city construction is not only the specific application of big data in infrastructure construction, but a high point of development in the new round of digital economy and new and old energy conversion transformation.
In recent years, hundreds of horizontal wells have been drilled with cemented casing completion in Daqing oilfield, China. Some of these wells in low permeability reservoirs have been completed by using limited entry fracturing to increase the initial oil production. The production performance of some limited entry fracturing horizontal wells is lower with decline rapidly. Temperature logs of these wells indicate parts of the perforated intervals in the horizontal wellbore are not being treated enough. That is a main reason for low production. How to enhance the production of these horizontal wells? A multiple stages refracturing treatment by using packers isolated has been performed on one well. The use of more aggressive proppant schedules and intense quality control has been designed. The treating pressure of each refracturing stage shows very different that can suggest which one is not stimulated in the initial fracturing treatment. The result from the refracture treatment was encouraging with a peak post-refracture treatment production rate in excess of the peak production rate after the original completion stimulation. This possibility makes more horizontal wells as candidates for refracturing. Introduction Daqing oilfield, the biggest oilfield located in the Songliao basin in the northeastern of China, is a huge complex consisting of many individual oil fields. As reservoir development continuing, tighter and thinner pay zones become new targets in term of production incensement for this matured field. The production of the wells in the new blocks is as low as less than 1 ton/day for some wells. How to improve the production of these areas is a major concern. From 2003, horizontal well completion and fracture stimulation have thus become applicable to improve the production. Within the first three years, to reduce the risk of treatment in horizontal well, limited entry perforation to distribute the fractures along the horizontal lateral in single-stage treatments has proven to be an effective completion method. The technique has been applied to 24 horizontal wells in Daqing Oilfield. The average initial oil production after using the limited entry fracturing in horizontal wells is 3.2 times higher than that in vertical fracturing wells. But the production performance of some horizontal wells is still lower with decline rapidly. S92P48 well in Linjiang area located in southeast part of Daqing oil field is one of them. Comparing the result of the temperature logging before and after fracturing treatment can show us significant about fractures distribution along the lateral. Figure 1 shows the comparing results of temperature logs in a limited entry fracturing horizontal well. In the well, we can see the four parts of significant low temperature areas by cold fracturing liquid being pumped into the hot formation, which indicates hydraulic fractures exist. But we can also find that there are just slight temperature changing at the limited entry perforation part near the end of the horizontal lateral, that indicate hydraulic fractures is not exist. Even in the three significant low temperature areas, the extents of temperature dropping are different, that show us the fractures in the horizontal wellbore are not being treated uniformity. Although perforations has been designed to hope to control the treatment fluid distribution uniformly, but sometimes we can't get the results as we want because of the heterogeneous lithology, great anisotropy of reservoir permeability along the average horizontal lateral length over 500 meters. That is a main reason for low production and decline rapidly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.