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Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations
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Abstract
In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations. These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.
Since the appearance of computers and the internet, many enabling technologies entered the oil-patch. Over the years, various areas improved as a result of significant commercial, corporate and academic efforts. However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management.
This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned. Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions. These are presented in four interrelated areas: people, automated workflows, processes and technologies.
Management of change at various levels of the organization continues to be a critical success factor, e.g. the introduction of new systems, designing effective collaborative ways of working, training people, management support and having the right resources (time and financial) to focus on real-time oil and gas production optimization.
Automated workflows have been developed and deployed at various intensity levels. Although there is a lack of common language for defining workflows, there has been tremendous improvement in this area.
On the other hand, work processes had been an area of little improvement. Because of poor process definition and understanding, companies failed to adopt and sustain highly sophisticated workflows. In the paper, we discuss the effort required to properly describe work process that support DOF implementations. We also describe the technology challenges faced and how these can be mitigated in the future.
Title: Best Practices and Lessons Learned After 10 Years of Digital Oilfield (DOF) Implementations
Description:
Abstract
In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations.
These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.
Since the appearance of computers and the internet, many enabling technologies entered the oil-patch.
Over the years, various areas improved as a result of significant commercial, corporate and academic efforts.
However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management.
This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned.
Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions.
These are presented in four interrelated areas: people, automated workflows, processes and technologies.
Management of change at various levels of the organization continues to be a critical success factor, e.
g.
the introduction of new systems, designing effective collaborative ways of working, training people, management support and having the right resources (time and financial) to focus on real-time oil and gas production optimization.
Automated workflows have been developed and deployed at various intensity levels.
Although there is a lack of common language for defining workflows, there has been tremendous improvement in this area.
On the other hand, work processes had been an area of little improvement.
Because of poor process definition and understanding, companies failed to adopt and sustain highly sophisticated workflows.
In the paper, we discuss the effort required to properly describe work process that support DOF implementations.
We also describe the technology challenges faced and how these can be mitigated in the future.
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