The digital oilfield technology is typically associated with high level of field automation and instrumentation, as well as advanced petroleum engineering modelling.

This paper discusses the application of digital oilfield to large brown fields based on real, but anonymous cases, where the level of instrumentation is low, production models might not be available, and the local expertise might be limited.

In such situation, the principles of digital oilfield need to be adapted. This paper presents a staged implementation methodology, where the benefits and costs can be evaluated at every step of the project, allowing to build a system with the right amount of functionality and complexity.

The first step focuses on improving data quality, even if the data is captured manually, through automated quality checks and raising awareness during the data capture process. The second step focuses on automating routine tasks, such as reporting, leading to efficiency improvement, but also increased accuracy and traceability of the reported figures. The third step focuses on developing a production monitoring platform, allowing to perform exception-based surveillance, particularly important for large fields, as well as providing a single point of access for different disciplines, hence acting as a collaborative environment. At last, the model-based more complex workflows are discussed, such as virtual metering, production optimization and short-term production forecasting.

The main conclusion of this paper is that the Digital Oilfield can deliver value for brown fields, even if they are close to their life end. The relatively low cost of these solutions, and the immediate benefits they can provide makes it meaningful, even in a short-term perspective. A staged implementation lowers both the project risks and the required initial investment, while easing the adoption process by the users.

The main differences with application to green fields is an increased focus on data quality improvement, and a lower focus on models and complex engineering workflows. The surveillance platform should also focus more intensively on exception based surveillance, allowing to pre-process large amounts of data, rather than providing extremely fine detail.

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