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Summary Aera Energy LLC was formed in 1997 to be a low-cost operator and producer in California. However, the low oil prices from 1998 to 1999 forced an examination of all operations in the office and in the field. Cutting costs, improving timekeeping, or reducing the scale of operations would not be sufficient without a step-change gain in efficiency. This step-change gain came from the use of principles and concepts developed in the automobile and construction industries. Toyota's twin pillars of just-in-time production and the ability of anyone to stop production rather than pass on defects, coupled with level-loading of work processes and reducing waste, were introduced. Toyota's principles were enhanced by the addition of “Last Planner” concepts developed for the construction industry. When both were implemented for reservoir-characterization and reservoir-development work, significant process improvements resulted. The resulting improvements are now being used throughout the company to improve quality by removing waste and reducing errors, to measure processes, and to improve cycle times. The unconventional diatomite reservoirs and oil-sand reservoirs at the giant Belridge field produce 65,000 BOPD from 5,300 producing wells and 2,100 injection wells. The many drilling, completion, and workover rigs have a constant appetite for new wells. To maintain production and cost targets, everything in the office has to run reliably and efficiently at all times, as well as support field operations. Different aspects of Toyota's principles and “Lean Manufacturing” are illustrated by use of project work for reservoir characterization, day-to-day reservoir surveillance, and the development work needed to plan and schedule new wells. The processes and projects typically have multiple customers and suppliers—internal and external. All involved, including knowledge workers (those who think for a living), need to work as a single system with a manufacturing mentality and to strive for continuous improvement. Customers of the knowledge work supplied by the geoscientists and reservoir engineers have benefited greatly from the introduction of the lean processes and the resulting smoother and more-effective workflows. In 2011, the Development Team's lean activities were recognized by the Association for Manufacturing Excellence, and the team received the Manufacturing Excellence Award that recognizes “continuous improvement, best practices, creativity, and innovation.” The oil industry has a reputation of being slow to adopt new technologies and techniques. However, a Lean Manufacturing mentality introduces new ideas and ways of performing knowledge work that may change this paradigm while contributing to the bottom line with reduced cycle time and improved quality. A significant additional benefit is that geoscience and engineering professionals can spend more time doing creative work and less time fixing problems or reacting to system upsets—as they simultaneously reduce waste. However, to realize all these benefits and the step changes they provide, a thorough understanding of Toyota's principles and a Lean Manufacturing mentality are essential.
Summary Aera Energy LLC was formed in 1997 to be a low-cost operator and producer in California. However, the low oil prices from 1998 to 1999 forced an examination of all operations in the office and in the field. Cutting costs, improving timekeeping, or reducing the scale of operations would not be sufficient without a step-change gain in efficiency. This step-change gain came from the use of principles and concepts developed in the automobile and construction industries. Toyota's twin pillars of just-in-time production and the ability of anyone to stop production rather than pass on defects, coupled with level-loading of work processes and reducing waste, were introduced. Toyota's principles were enhanced by the addition of “Last Planner” concepts developed for the construction industry. When both were implemented for reservoir-characterization and reservoir-development work, significant process improvements resulted. The resulting improvements are now being used throughout the company to improve quality by removing waste and reducing errors, to measure processes, and to improve cycle times. The unconventional diatomite reservoirs and oil-sand reservoirs at the giant Belridge field produce 65,000 BOPD from 5,300 producing wells and 2,100 injection wells. The many drilling, completion, and workover rigs have a constant appetite for new wells. To maintain production and cost targets, everything in the office has to run reliably and efficiently at all times, as well as support field operations. Different aspects of Toyota's principles and “Lean Manufacturing” are illustrated by use of project work for reservoir characterization, day-to-day reservoir surveillance, and the development work needed to plan and schedule new wells. The processes and projects typically have multiple customers and suppliers—internal and external. All involved, including knowledge workers (those who think for a living), need to work as a single system with a manufacturing mentality and to strive for continuous improvement. Customers of the knowledge work supplied by the geoscientists and reservoir engineers have benefited greatly from the introduction of the lean processes and the resulting smoother and more-effective workflows. In 2011, the Development Team's lean activities were recognized by the Association for Manufacturing Excellence, and the team received the Manufacturing Excellence Award that recognizes “continuous improvement, best practices, creativity, and innovation.” The oil industry has a reputation of being slow to adopt new technologies and techniques. However, a Lean Manufacturing mentality introduces new ideas and ways of performing knowledge work that may change this paradigm while contributing to the bottom line with reduced cycle time and improved quality. A significant additional benefit is that geoscience and engineering professionals can spend more time doing creative work and less time fixing problems or reacting to system upsets—as they simultaneously reduce waste. However, to realize all these benefits and the step changes they provide, a thorough understanding of Toyota's principles and a Lean Manufacturing mentality are essential.
Drilling automation is the control of the drilling process by automatic means, ultimately reducing human intervention to a minimum. The concept of automating the drilling process has generated considerable interest, yet there is a lack of agreement on exactly what it is, what it entails, and how to implement it. As with industrial automation in the 1990s, the adoption of open standards enabling automation will have a significant impact on the underlying business model. In the oilfield-drilling industry, the business model describes the relationship between operator, drilling contractor, service company, and equipment supplier.The goal of drilling-systems automation is to increase productivity and quality, improve personnel safety, and effectively manage risk. Principal drivers of drilling automation include well complexity, data overload, efficiency, repetitive well manufacturing, access to limited expert resources, knowledge transfer as a result of the exodus of skilled employees, and health, safety, and environmental concerns. With so many drivers, and their potential economic benefits, it is understandable that there are many automation-related initiatives within the industry.Drilling through geopressured, possibly erratic, lithologies to a remote and possibly poorly defined target in a safe manner is not a simple task to automate. It is challenging. Drilling automation focuses on the drilling system and drilling operations, which entail combining various subsystems, including the downhole bottomhole assembly (BHA) and its measurement and active components, the drillstring, fluid, and drilling rig and its subassemblies. Operations include conventional overbalanced-, managed-pressure-, and underbalanced-drilling operations, and their various procedures, such as tripping and making connections. This paper examines and defines drilling-systems automation, its drivers, enablers and barriers, and its current state and goals. In particular, the paper looks at the vision of drilling-systems automation, and the role played by open, collaborative initiatives among all segments of the drilling industry. Although commitment to automation by the drilling industry appears by many to lag behind the level of commitment in other major industries, there are segments of the drilling industry that have reached a high level of automation on a commercial basis. There is also significant collaboration among interested parties in creating a standardized, open environment for data flow to foster the development of systems automation.
Standardization of drilling procedures is a key element for success in the development of unconventional oil and gas fields today. We propose approximate string matching as a data analytic technique that can be used to measure standardization across wells in a field. We describe the implementation of approximate string matching, providing details of how it can be contextualized for oil and gas drilling using operation codes, and how results can be normalized to allow for comparison between disproportionate sequences of operations. We use this technique to develop a measure for operational variability, the first objective metric for evaluating and comparing the rate and consistency of standardization across different wells, sections of wells, and rigs, informing decision-making based on the existing large streams of feedback from field operations. We provide examples of operational variability using analysis of data from about two hundred horizontal wells drilled by a single operator in a major North American unconventional field, and develop the concept of the "standardization curve" from this to demonstrate the importance of this metric in understanding learning and movement along the "learning curve" during a drilling campaign. Additionally, we outline some specific ways that these approaches can be used to automate the data-driven comparison, disaggregation, and assimilation of field learning to better manage improvement within drilling campaigns. This tool provides a foundation upon which future data analytic and machine learning techniques can build, developing learning programs for "smart" oil and gas fields that make better use of available data to enable rapid adaptation and greater overall drilling efficiency.
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