Agradecimentos: This work was conducted with the support of Energi Simulation and in association with the ongoing Project registered as "BG-32 – Análise de Risco para o Desenvolvimento e Gerenciamento de Campos de Petróleo e Potencial uso de Emuladores" (UNICAMP/Shell Brazil/ANP) funded by Shell...

Agradecimentos: This work was conducted with the support of Energi Simulation and in association with the ongoing Project registered as "BG-32 – Análise de Risco para o Desenvolvimento e Gerenciamento de Campos de Petróleo e Potencial uso de Emuladores" (UNICAMP/Shell Brazil/ANP) funded by Shell Brazil, under the ANP R&D levy as "Compromisso de Investimentos com Pesquisa e Desenvolvimento". The authors also thank UNISIM, DE-FEM-UNICAMP, CEPETRO and PETROBRAS for supporting this work and CMG, Emerson and Schlumberger for software licenses

Numerical reservoir simulation is a valuable tool to support the decision-making process in oil field projects. For this purpose, current reservoir engineering studies request numerous simulations runs for complex workflows, such as numerical reservoir characterization, data assimilation process,...

Numerical reservoir simulation is a valuable tool to support the decision-making process in oil field projects. For this purpose, current reservoir engineering studies request numerous simulations runs for complex workflows, such as numerical reservoir characterization, data assimilation process, strategy optimisation, well placement studies, production management and supplementary project support. Thus, providing efficient and effective simulation numerical models is a critical task in reservoir simulation studies before starting the daily run demands. In this work, we propose, test and evaluate a practical workflow to improve the numerical performance of time-consuming reservoir simulation models. We provide a procedure to select representative numerical submodels and representative snapshots interval from simulation time (RSIST) to reduce the total time spent in numerical optimisation. The first approach allows the optimisation of the numerical parameters without the drawback of simulating long execution runtime. The second enables to select a range of simulation time where typical convergence problems of timestep cuts occur. The numerical parameters are then optimised using submodels and RSIST, where we highlight a workflow to reduce runtime of the optimisation of slow reservoir models. Following, the resultant set of parameters is applied to the entire (original) reservoir numerical model, improving its performance and allowing more effective execution of several simulation-runs in reservoir engineering studies.
To conclude, we provide a workflow to be applied to any reservoir simulation model, especially those with complex structural grid and high-time consuming to run. Our results demonstrated remarkable improvements in the numerical performance of simulation models, with a high potential of saving days of work during probabilistic evaluations. Furthermore, we recommend this workflow as a first step before starting studies using reservoir simulation to avoid unphysical and time-demanding simulation runs that may affect future decisions

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