Strategic Evaluation of Managed Pressure Drilling: An Application on Brazilian Dynamic-Positioning Rigs
- Pedro Esteves Aranha (Petrobras) | Danilo Colombo (Petrobras) | André Alonso Fernandes (Petrobras) | Guilherme Siqueira Vanni (Petrobras) | Reinaldo Akio Tomita (Petrobras) | Cláudio Benevenuto de Campos Lima (Petrobras) | Gilson Brito Alves Lima (Federal Fluminense University) | Júlio César de Faria Alvim Wasserman (Federal Fluminense University)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- April 2019
- Document Type
- Journal Paper
- 2019.Society of Petroleum Engineers
- MPD, Monte Carlo Simulation, Loss Zone, Managed Pressure Drilling
- 6 in the last 30 days
- 50 since 2007
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The demand for ultradeepwater scenarios invoked the frequent application of managed pressure drilling (MPD) in the last few years. In an ultradeepwater scenario, oil companies face issues such as narrow pressure windows and severe loss zones. Many wells are considered undrillable without the aid of MPD technology. MPD operations need to be correctly evaluated with consideration given to increased time and cost/benefit analysis. In this paper, we propose a probabilistic model to evaluate MPD demand by estimating the optimal number of rigs equipped with MPD and a rotating control device (RCD), and we analyze which intervention strategy is the most cost- and time-effective. Reducing uncertainty is an important factor when making decisions about drilling. We adopted a Monte Carlo simulation using loss-zone estimation, probability of prediction error, the number of rigs equipped with MPD, and several strategies. Better MPD strategies were determined on the basis of available data and the optimal number of rigs equipped with an MPD system and RCD equipment, reducing subjectivity in the decision-making process. The originality of our paper lies in the new quantitative approach to dealing with uncertainty in the prediction of fluid losses and the cost and duration of different MPD strategies, numerically simulating the possible scenarios.
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