The push within the oil and gas industry towards digitalization hold great promises for optimizing operations and improving efficiency. Therefore, most oil and gas companies have large initiatives within digitalization – huge efforts on data acquisition, Industrial Internet of Things, Artificial Intelligence (AI), Machine Learning (ML) etc. – all digital opportunities introduced to the industry in recent years. We look at data and models as oil and gas "assets" – much like physical assets used in operations (wells, platforms, compressors etc.). Thus, there is a perceived inherent value in data for operations. For effective operations, the interesting part of data, models and digital capabilities, is the feedback loop it allows to the physical asset itself. One wants to close/open a valve for optimal production, ensure maintenance on machinery and ensure safe scaffolding operations without sending people offshore during planning. There are two types of feedback opportunities to the physical asset – either through automation or by humans. Both humans and automation algorithms can get advice from some "co-bot" based on AI, ML or other digital opportunities. When the feedback is given automatically, we have full control over the work performed by the algorithm or cobot, while the same is not the case when humans are involved. At certain level engineers and operators know how to do the work – this is embedded into known "work processes." However, the actual interpretation of a work processes into concrete activities may lead to different tasks for team members – even for the same work. This gives lack of repeatability, inconsistency in operations, and makes effective collaborations challenging. In many ways, we lack (real time) data on the work itself, when it's done by humans. This paper addresses this issue – we explain why data on the work and how effectively it is performed should be regarded as an asset in a similar way to real-time data and reservoir models. We show technologies and examples allowing organization to assess data on the work itself. We discuss how teams can work differently, and how technology can be used to drive consistency and KPIs toward more effective operations.

Digital Transformation in oil and gas is bringing a new wave of opportunities that will transform the industry. In operations, these technologies can lead to new Ways of Working and deliver next generation Integrated Operations Centers (IOCs); empowering them to be the digital watering holes at the center of world-class operations. Several companies have publicized the effectiveness of their IOC initiatives in terms of cost savings, reduced losses and increased production. However, the success of Integrated Operations initiatives is not guaranteed; many IOCs have been deployed that have failed to live up to expectations. Based on decades of experience delivering IOCs, this paper will explore the underlying principles that should be behind every IOC program, as well as the key program elements and delivery methodology that, while not guaranteeing success, go a long way to addressing the issues common to unsuccessful projects. The principles proposed as the drivers behind an IOC initiative all support the philosophy that delivering an IOC is a long-distance race, not a sprint that ends when the center opens. The consequence of this is that any IO program should think of the center as a capability that should be delivered as manageable chunks into an operations culture that embraces evolution and change. To support these principles, the paper discusses eight key elements to include as part of the delivery program. These elements do more than support the delivery program, they also ensure the sustainability of the IOC capability. Finally, the paper discusses the advantages of adopting a delivery methodology from software development that has been shown to significantly improve the delivery of value, the engagement of stakeholders and the programs ability to cope with evolving changes.