Application of Numerical Flow Simulation Methods to Risk-Based Well Decommissioning Design

The global oil and gas industry will experience intensive well decommissioning activities, with tens ofthousands of well plug and abandonment (P&A) operations forecast to be executed worldwide, over the nextfew decades. In the North Sea alone, 2,624 wells are expected to be decommissioned over the next decade(OIL&GAS-UK 2019). With the increasing levels of activity in P&A operations, new, fit-for-purpose,P&A design tools and operational technologies have become essential elements in the petroleum engineer'stoolbox to ensure safe and cost-effective solutions, especially in today's environment. This paper introducesa novel tool to support risk-based evaluation of well P&A designs using a numerical, grid-based, fluid-flow simulation methodology. The risk-based modelling framework covers the full range of North Sea P&Adesign scenarios and tracks the long-Term evolution of hydrocarbon movement in the P&A well over severalthousand years. A visualization engine identifies the dominant hydrocarbon flow-paths, while Monte Carlomethods are used to account for uncertainties in the model input parameters; allowing multiple designoptions to be ranked with respect to the risk of a hydrocarbon leak. Transient flow modelling of the well P&A system allows new key performance indicators (KPIs) tobe developed, e.g. evolution of hydrocarbon saturations within the P&A well over time and the timehydrocarbons reach the surface. These KPIs are not provided by steady-state P&A models. Results presentedin this paper demonstrate the value derived from applications of the developed framework to: Optimally allocate resources by identifying fit-for-purpose P&A designs (e.g.The number, locationand length of wellbore barriers, and the value of remedial operations on annular flow barriers). Support risk-based decision-making via investigation and comparison of how multiple P&A designoptions perform for given well/reservoir conditions. Identify critical modelling parameters and optimally allocate data gathering resources to reduceuncertainty. The "likelihood of occurrence" of each cement defect type is one such critical, butvery uncertain, model input parameter.