Characterizing Slug Flow in Offshore Gas Condensate Fields: A Fit-For-Purpose Approach for Assessing Mechanical Integrity

Abstract

Abstract This paper addresses the intricate challenges of accurately simulating slug flow in pipelines, particularly in cases exacerbated by potential chemical contamination. Such complex scenarios can be challenging to replicate with state of art simulation tools, underscoring the significance of the methodology presented in this paper. Far from being purely theoretical, this approach was applied effectively in a real-world field operation, demonstrating its practical applicability and relevance. The proposed fit-for purpose methodology encompasses three critical stages: Detailed Characterization of Slug Flow: This step is essential for gaining a thorough understanding of slug flow characteristics. Simulation Integration: Implementing the characterized slug flow into simulation software to accurately replicate observed slug behavior. Preparation of Process Data: This involves generating process data for mechanical assessment, guided by modeling that is benchmarked against actual field observations. This paper not only presents a tailored methodology for handling such complex flow scenarios but also details a real-world case study where this approach was successfully employed. In the case study, the artificial slug generation technique was pivotal in pinpointing the location and volume of the slug, thereby facilitating the preparation of crucial process data for mechanical assessments, particularly for areas of interest such as free spans. Despite the uncertainty surrounding the exact root cause of the slug flow, potentially linked to chemical contamination during a planned maintenance campaign, this methodology proved effective in overcoming the constraints of conventional simulation tools. The successful implementation of this methodology in a field operation not only validates its practicality but also suggests its potential for wider adoption in the industry. It stands as a testament to the methodology's capability to address and resolve complex flow challenges in pipeline systems.