An Empirical Multi-Disciplinary Workflow to Maximize Brown Field Reservoir Surveillance

Abstract

Abstract Reservoir surveillance data acquisition and reliability is a critical component to ensure optimum reservoir management is practiced to secure long term reservoir health and productivity. This study will focus on securing surveillance data achievements through the application of a systematic workflow and methodology. The subject field is highly brown with offshore surface and wellbore indfrastructure dating to the 1960s. Currently, well data is acquired through physical wellhead tower visits to divert wells or run monitoring equipment. The field is a drop-shaped low-relief anticline, east to west orientation with a rounded dome-like eastern part and an elongated flatter western part. The General dip of the structure is one to two degrees to the west, and close to four degrees to the east. It is divided into two superimposed reservoirs, which are vertically isolated by a 100 feet thick non-reservoir dense interval. Both reservoirs are highly distinct in terms of sedimentology, architecture, and diagenesis. The field is highly challenging through numerous bottlenecks to achieve reservoir surveillance including highly brown infrastructure, logistics, and high production targets. The resources currently available include three barges, two test vessels, five MPFM crews, two logging units, and nine wireline units with accompanying crews. These resources are required to perform operations for multiple disciplines including flow assurance teams for corrosion monitoring, well integrity teams, drilling teams for well securing, and reservoir management teams for surveillance data acquisition. Prior to implementing the systematic methodology, these teams were not synchronized and functioning independently to secure individual team KPIs. The different multi-disciplinary requirements are illustrated below.