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

Abstract

Abstract The global oil and gas industry will experience intensive well decommissioning activities, with tens of thousands of well plug and abandonment (P&A) operations forecast to be executed worldwide, over the next few 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's toolbox to ensure safe and cost-effective solutions, especially in today's environment. This paper introduces a 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&A design scenarios and tracks the long-term evolution of hydrocarbon movement in the P&A well over several thousand years. A visualization engine identifies the dominant hydrocarbon flow-paths, while Monte Carlo methods are used to account for uncertainties in the model input parameters; allowing multiple design options 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) to be developed, e.g. evolution of hydrocarbon saturations within the P&A well over time and the time hydrocarbons reach the surface. These KPIs are not provided by steady-state P&A models. Results presented in 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, location and 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 design options perform for given well/reservoir conditions.Identify critical modelling parameters and optimally allocate data gathering resources to reduce uncertainty. The "likelihood of occurrence" of each cement defect type is one such critical, but very uncertain, model input parameter.