Abstract
A number of new technologies introduced into the oil field over the last couple of decades now provide the hardware basis for continuous field-wide optimization (CFO). CFO will require computer integration of field hardware (e.g., downhole sensors, remotely activated completions, surface facilities) for continuous decision-making in a feedback fashion (data acquisition, data processing, actuation). We introduce a hierarchy of oil field operations that identifies various levels of detail and time-scales for decision-making processes. We propose to use this hierarchy in a multi-level / multi-scale approach to CFO. An important element in that approach is the availability of predictive models that can be used at various levels of the hierarchy, so that optimal actions can be continuously selected through optimization over a moving horizon.. In this context, artificial neural networks (ANN) are a tool that has been used to build data-driven models. This paper is structured in two parts: (1) elements of CFO, and (2) brief review of ANN basics, known ANN applications in the petroleum industry, and a critical view of ANN capabilities.
