Agradecimentos: We gratefully acknowledge the support of EPIC – Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by FAPESP – São Paulo Research Foundation (grant number 2017/15736-3). We acknowledge the support and funding from Equinor Brazil and the...

Agradecimentos: We gratefully acknowledge the support of EPIC – Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by FAPESP – São Paulo Research Foundation (grant number 2017/15736-3). We acknowledge the support and funding from Equinor Brazil and the support of ANP (Brazil's National Agency of Oil, Natural Gas and Biofuels) through the R&D levy regulation. We also acknowledge the funding from Energi Simulation, the Coordination for the Improvement of Higher Education Personnel (CAPES) and Petrobras, and the support of the Center for Petroleum Studies (CEPETRO) and the School of Mechanical Engineering at UNICAMP. We also thank the Computer Modelling Group Ltd. (CMG) for software licenses and technical support

Development decisions for petroleum fields are costly and difficult because they involve long-term projects with complex systems and high level of uncertainties. Decision makers may reduce reservoir uncertainties with the acquisition of additional information or protecting the system against...

Development decisions for petroleum fields are costly and difficult because they involve long-term projects with complex systems and high level of uncertainties. Decision makers may reduce reservoir uncertainties with the acquisition of additional information or protecting the system against uncertainty with flexibility and robustness. Although often preferred, acquiring additional information may be insufficient to mitigate all uncertainties or may be suboptimal when compared to robustness and flexibility. This study proposes a decision framework to estimate the best approach to manage uncertainty at early stages of field development considering the combination of information, robustness, and flexibility. With a predefined set of uncertain scenarios and specialized production strategies optimized for representative models, we can automate the process. We identify the best way to manage uncertainties by assessing system sensitivity and what controls production strategy selection. We improve the traditional estimation of the expected value of information (EVoI), robustness (EVoR), and flexibility (EVoF), by accounting for all changes in the risk curve and weighing the decision maker's attitude. Our proposal was validated in a controlled environment where the reference response is known (a reference model, called UNISIM-I-R, that is not part of the uncertain ensemble, called UNISIM-I-D). The best approach to manage uncertainty in UNISIM-I-D combined features of robust and specialized strategies, resulting in a flexible strategy with robustness. This solution increased oil recovery and the economic return in UNISIM-I-R when compared to taking no action to manage uncertainty, but was suboptimal. These results agree with the rationale behind investing in actions to manage uncertainty (such as flexible and robust strategies), which is to mitigate risks or exploit upsides of uncertainty (i.e., they are intended to prevent or explore extreme outcomes). Investing in such actions is a risky decision in itself because they may not be needed, depending on the features of the real reservoir. Our results in UNISIM-I-D demonstrate the EVoI, EVoR, and EVoF are not additive. Our results in UNISIM-I-R reveal that inherent flexibility in operations (such as in drilling of horizontal wells) may mitigate some risks, reducing the EVoI, EVoR, and EVoF. Our proposal is a good starting point for more quantitative and objective decision-making at the early stages of field development and ultimately prevents discarding attractive solutions based on biases or insufficient metrics. Some improvements may be necessary for EVoI, EVoR, and EVoF estimates depending on the stage of the lifetime of the field, among other factors

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

2017/15736-3

COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES

Fechado