Today's climate of a general lack of resources and people, high prices and limited rig availability forces the oil industry to plan for efficient production and secondary recovery. This puts more weight on understanding reservoir architecture and fluid complexities, the main drivers of recovery. Improving confidence in reservoir architecture has taken center stage in risk management especially in high cost environments. Data availability to increase this confidence will always be limited by budget and/or operational constraints. This puts the strike on maximizing the value of acquired data and integrating all available data to help this cause.

Recent advances in sensor technology and petroleum science allows using downhole fluid analysis data to improve confidence in reservoir architecture. Mapping composition, gas oil ratio (GOR) and density across the field is common practice. These properties are based on the amount of solution gas in the liquid phase and their equilibrium distribution can be predicted by (typically) cubic equations of state (EOS). Evaluating the relative asphaltene distribution is based on different physics: the suspension of solids in the liquid phase. It is robustly assessed by the latest generation of downhole fluid analysis (DFA) tools and recent breakthroughs in science now also allow predicting equilibrium distributions by EOS. Consequently, two equations of state are used to analyze two separate fluid gradients, GOR and asphaltenes, yielding a robust method of reservoir evaluation. This new independent workflow is especially valuable when used in concert with PVT reports, well test data, static pressure gradients and other common techniques to assess reservoir architecture.

This paper presents two real-life case studies from the Norwegian continental shelf that use available DFA data to support the assumptions made from other data on reservoir architecture between wells. It shows the validity of the concept, but also highlights the limits and constraints of such a data set. These case studies lead the way to planning the data acquisition to include a more comprehensive DFA data set to address connectivity and other reservoir concerns.