
Abstract Today’s sophisticated data acquisition systems for both surface and downhole drilling data have greatly enhanced the understanding of the basic drilling mechanics and helped in the identification of major dysfunctions particularly harmful downhole vibrations. Various methods are now being used to optimize drilling practices by monitoring efficiency indicators such as mechanical specific energy (MSE) and preventing harmful vibrations by adjusting drilling parameters real time. However, focusing on the efficiency of components and the prevention of dysfunctions is only a partial solution. It is the magnitude of mechanical power delivered to the bit that defines the capacity of the system and directly affects efficiencies, operating practices and performance. This paper will show how the power at the bit and drilling efficiency vary as a function of drill string and BHA configuration, bit selection and drilling parameters. Field examples will illustrate the magnitude of parasitic losses caused by well geometry and the downhole environment. In many cases it is striking how little power is left for the drill bit which in the end dictates the potential rate of penetration and thus the cost per foot and economics of the drilling process. Particular emphasis has been put on new and intuitive ways to visually display the power at the surface and downhole. It makes it possible to quantify system limits and identify key dysfunctions in one, simple graph that can be used in both pre or post well analysis and real time. The better understanding of the entire drilling system and the functional relationship of its parts can be a strong driver for both short and long-term improvements in system design, drilling practices and bit selection which ultimately results in higher productivity and lower well construction cost.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 30 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
