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.

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