An Integrated Approach for Well Design Optimization with MPD and its Impact on Value

Abstract

Abstract In high pressure high temperature (HPHT) reservoirs and exploratory wells, especially in deep water, there is a higher degree of uncertainty, which can increase the operational costs due to non-productive time (NPT) and operational problems due to the unpredictable nature of these wells. For these challenging wells with narrow windows, Managed Pressure Drilling (MPD) techniques offer cost-effective tools to increase the odds for achieving well and cost objectives assurance. There are significant benefits from early implementation of MPD in the project life cycle. These benefits include from improving operational efficiency to risk mitigation and safety enhancement. However, there is an enormous potential that many operators have been missing. This is related to the incorporation of MPD as a driver in optimizing the well design, which could greatly increase the possibilities of reaching target depth, and potentially prepare to eliminate one or more casing strings. Current well design process hinges on the ability to manage uncertainties by company or regulatory requirements, such as kick tolerance and safety factors. This work addresses the value added from implementing MPD in early stages in the project life cycle through the analysis of case studies. The cost savings from the impact on the well design are also discussed. This work also presents a in depth discussion on the benefits, and enablers of this approach. Furthermore, it presents considerations by taking advantage of dynamic processes facilitated with MPD. Finally, new guiding criteria to aim to constitute a systematic and integrated approach to ensure well integrity and optimize well design while also considering the operational implications and integral cost benefits is proposed to the industry. This paper represents the initial phase of a compressive long-term project to integrate two main components of well design. These are MPD adaptive well design, and statistical analysis based on variations of load and/or strength.