Considering the stratum anti-drilling ability,drill bit working conditions,drill bit application effect and drill bit economic benefits,the similarity of stratum anti-drilling ability was evaluated by grey relational ...Considering the stratum anti-drilling ability,drill bit working conditions,drill bit application effect and drill bit economic benefits,the similarity of stratum anti-drilling ability was evaluated by grey relational analysis theory to screen out candidate drill bits with reference values.A new comprehensive performance evaluation model of drill bit was established by constructing the absolute ideal solution,changing the relative distance measurement method,and introducing entropy weight to work out the closeness between the candidate drill bits and ideal drill bits and select the reasonable drill bit.Through the construction of absolute ideal solution,improvement of relative distance measurement method and introduction of entropy weight,the inherent defects of TOPSIS decision analysis method,such as non-absolute order,reverse order and unreasonable weight setting,can be overcome.Simple in calculation and easy to understand,the new bit selection method has good adaptability to drill bit selection using dynamic change drill bit database.Field application has proved that the drill bits selected by the new drill bit selection method had significant increase in average rate of penetration,low wear rate,and good compatibility with the drilled formations in actual drilling.This new method of drill bit selection can be used as a technical means to select drill bits with high efficiency,long life and good economics in oilfields.展开更多
In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal perio...In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.展开更多
Amdahl’s Law painted a bleak picture for large-scale computing.The implication was that parallelism was limited and therefore so was potential speedup.While Amdahl’s contribution was seminal and important,it drove o...Amdahl’s Law painted a bleak picture for large-scale computing.The implication was that parallelism was limited and therefore so was potential speedup.While Amdahl’s contribution was seminal and important,it drove others vested in parallel processing to define more clearly why large-scale systems are critical to our future and how they fundamentally provide opportunities for speedup beyond Amdahl’s predictions.In the early 2000s,much like Amdahl,we predicted dire consequences for large-scale systems due to power limits.While our early work was often dismissed,the implications were clear to some:power would ultimately limit performance.In this retrospective,we discuss how power-performance measurement and modeling at scale led to contributions that have driven server and supercomputer design for more than a decade.While the influence of these techniques is now indisputable,we discuss their connections,limits and additional research directions necessary to continue the performance gains our industry is accustomed to.展开更多
基金Supported by China National Science and Technology Major Project(2016ZX05020-006)。
文摘Considering the stratum anti-drilling ability,drill bit working conditions,drill bit application effect and drill bit economic benefits,the similarity of stratum anti-drilling ability was evaluated by grey relational analysis theory to screen out candidate drill bits with reference values.A new comprehensive performance evaluation model of drill bit was established by constructing the absolute ideal solution,changing the relative distance measurement method,and introducing entropy weight to work out the closeness between the candidate drill bits and ideal drill bits and select the reasonable drill bit.Through the construction of absolute ideal solution,improvement of relative distance measurement method and introduction of entropy weight,the inherent defects of TOPSIS decision analysis method,such as non-absolute order,reverse order and unreasonable weight setting,can be overcome.Simple in calculation and easy to understand,the new bit selection method has good adaptability to drill bit selection using dynamic change drill bit database.Field application has proved that the drill bits selected by the new drill bit selection method had significant increase in average rate of penetration,low wear rate,and good compatibility with the drilled formations in actual drilling.This new method of drill bit selection can be used as a technical means to select drill bits with high efficiency,long life and good economics in oilfields.
基金supported by COMPETE:POCI-01-0247-FEDER-039719 and FCT-Fundação para a Ciência e Tecnologia within the Project Scope:UIDB/00127/2020.
文摘In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.
文摘Amdahl’s Law painted a bleak picture for large-scale computing.The implication was that parallelism was limited and therefore so was potential speedup.While Amdahl’s contribution was seminal and important,it drove others vested in parallel processing to define more clearly why large-scale systems are critical to our future and how they fundamentally provide opportunities for speedup beyond Amdahl’s predictions.In the early 2000s,much like Amdahl,we predicted dire consequences for large-scale systems due to power limits.While our early work was often dismissed,the implications were clear to some:power would ultimately limit performance.In this retrospective,we discuss how power-performance measurement and modeling at scale led to contributions that have driven server and supercomputer design for more than a decade.While the influence of these techniques is now indisputable,we discuss their connections,limits and additional research directions necessary to continue the performance gains our industry is accustomed to.
