Methodology and Benefits of a Drilling Analysis Paradigm

Abstract

This paper discusses a segment of drilling engineering — drilling analysis — that is assumed to be practiced routinely, but in many cases is not given the necessary attention, time, and resources required. Routinely acquired drilling, geologic, mud-log, and logging data, are shown to be amenable to organization into a knowledge-based structure that permits:Use of statistical analysis to develop challenging but achievable technical limits, or best composite time (BCT), for similar wells - location, depth, hole size.Statistics-based best composite cost (BCC), the dollars equivalent of BCT. Both the BCC and BCT represent practical, challenging but achievable benchmarks that are continuously upgraded, providing significant steps in the process of drilling costs reduction.Specialized learning-curve analysis, trouble-time analysis, flat-time analysis, time vs. depth analysis, cost vs. depth analysis, rig and crew performance evaluation, bit-on-bottom and tripping analysis, time distribution plots, etc.Convenient drilling data cross-correlation with wireline logs, geology and mud-log data, to further delineate drilling problem zones and improve drillingoperational performance.A historical understanding of well construction that can contribute to continuing drilling improvement, and a method to provide feedback and collaboration with company drilling personnel, rig contractors, and service companies. This paper reviews existing variations, key elements, and typical field applications of drilling analysis, and puts forth the case that drilling analysis should become an integral part of drilling engineering, the same way well-test analysis is an integral part of reservoir engineering. The need for a university curriculum and training on this key discipline is stressed, along with the necessity for management to champion the use of drilling analysis as an on-going process to improve organizational drilling performance. Introduction Despite the capital intensive nature of drilling operations, especially offshore where a single well can cost $50-80 million or more, drilling analysis as a core sub-discipline of drilling engineering is not routinely practiced as it should be. This is understandable since drilling engineers are principally rewarded for well planning, (including AFE generation), and well-construction management. Implicit in this assumption is the notion that somewhere in the planning process, there was adequate and appropriate drilling analysis to aid the overall drilling process. With a few exceptions, given the daily pressures and responsibilities of engineers in well planning and drilling operations, this is not always the case. The lack of an organized knowledge-based data-management system that can seamlessly cross-correlate relevant drilling and geologic parameters is another deterrent to drilling analysis not being an integral part of drilling engineering. A literature search with the keywords "drilling analysis" reveals a scant amount of published works, even though several papers have addressed one form or another of what could be classified as drilling analysis (Fig. 1). Over the past thirty years, disciplines such as reservoir engineering evolved specialty skills and systems for pressure transient analysis, and production technologists developed skills and systems for nodal analysis. Clearly both sub-disciplines have greatly contributed to their respective technologies. Yet drilling engineering, to our knowledge, has not formalized tools and processes that would unambiguously constitute drilling analysis. Recently, there has been a formalized company initiative to develop such drilling analysis methodology and tools, with encouraging results. This paper presents some of the key processes, tools, and select field applications. Savings from dedicated applications of these concepts have already resulted in noticeable drilling cost reductions in some areas as shown in a companion paper (Ref. 1).

Uma metodologia para analise de dados de perfuração de poços de desenvolvimento

“…Este último aspecto também é destacado no trabalho de Iyoho et al (2004). Nele, é apresentada uma metodologia que pode ser utilizada para analisar o desempenho de operações executadas em campo, e que consiste em um processo de dez etapas, entre as quais a utilização de curvas de aprendizado.…”