Abstract
Subsurface uncertainty is usually greatest during the early stages of a field development. Static and dynamic well data are limited and seismic resolution is unlikely sufficient to capture the subsurface heterogeneities that exists within the reservoirs of interest. Regardless, project teams are tasked with coming up with a subsurface basis for the project field development plan.
Traditional approaches to subsurface analysis involve creating a best estimate realization (P50), as well as a low (P90) and a high side (P10) case. More often than not, the low and high side cases are just sensitivities around the same subsurface realization that underpins the best estimate case and do not take into account alternate subsurface realizations.
In addition, the impact of subsurface uncertainties with respect to choices made in the project is rarely documented and usually overlooked or understudied. Majority of the time, this leads to poor robustness of project design which becomes evident as development drilling commences and more data is acquired.
A scenario based analysis (SBA) approach is proposed to create subsurface realizations that underpins the project and helps formulate a robust data acquisition and project execution plan. When properly applied within the constraints of already acquired data and associated uncertainties, this approach highlights the impact of subsurface uncertainties on a project's design basis such as original oil in place (OIP), estimated ultimate recovery (EUR), well count, production handling capacity, and flowlines, amongst others. It also highlights key uncertainties that need to be mitigated early during project execution, requiring a detailed data acquisition and well sequencing planning.
The application of SBA to a deepwater field in order to come up with a robust design basis will be illustrated in this paper. We will show how multiple scenario models addressing key uncertainties resulted in a range of outcomes for OIP, EUR, well count and flowlines. We will also show how this has influenced our choice of early data acquisition and well sequencing to ensure the success of the project.
