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Numerical tuning in reservoir simulation: it is worth the effort in practical petroleum applications

  • G. AvansiEmail author
  • V. Rios
  • D. Schiozer
Technical Paper
  • 37 Downloads

Abstract

Giant carbonate oil fields in Brazilian pre-salt area present a high level of heterogeneity. Despite the fact that geoscientists aim at modelling these complex models in a high-block cell resolution, it is important to reduce the computational effort and speed up some processes such as forecasting the production during a risk analysis in a probabilistic approach. This practice must be evaluated to keep numerical and geological consistency of reservoir models. As a result, creating a procedure to assist petroleum scientists and engineers so as to reduce computational time, without losing accuracy, is the main goal of this proposed work. A methodology based on numerical parameter evaluation, tuning technique, diagnosis and application is defined to provide a robust and effective starting point of the numerical control settings during reservoir simulation model runs. All of the analyses are based on a good understanding of the reservoir heterogeneities (geologists) and characteristics (reservoir engineering) that may result in reduced central processing unit time, number of time-step cuts, solver failures and material balance compared to the default approach considered for these problems. In addition, to check the consistency of the proposed procedure, one building risk curve application for two case studies was selected, assuming that we are executing the tuning and the application in sequence of a common reservoir application. The results of the two case applications under our proposed assumptions showed that it is possible to (1) speed the simulation runs up to three times in comparison with the base case which uses the default numerical parameters of a commercial simulator, (2) reduce the number of cuts and failures and (3) control the material balance equation error within a previously defined tolerance. Despite decreasing the simulation run time in practical reservoir study applications, we are also avoiding bad combination of numerical inputs which can result in unfeasible reservoir numerical models. To conclude, depending on the amount of applications, number of runs and complexity of reservoir numerical model, the results showed that we can save time in the reservoir engineer’s routine activities while maintaining the consistency of reservoir models by dedicating an initial time to better understand and optimise the numerical parameters. Even though the vast majority of reservoir numerical models are created in high-resolution grid cells to maintain the level of heterogeneity and, consequently, increase the computational effort, more suitable numerical model quantifications should be conducted in order to reduce the running time. Nonetheless, a consistency in the reservoir model must be maintained during the entire procedure. Thus, the most important step before starting any reservoir simulation workflow is to set the numerical parameters to avoid convergence problems, to keep the consistency of the numerical simulation results (approaching the solution of nonlinear equations to the true solution) and to assist geoengineering in starting the reservoir numerical parameters consciously. In addition, we can make sure that appropriate controls of the numerical model are then being used efficiently as a practice in reservoir simulation studies, mainly before starting to run a large amount of simulations.

Keywords

Petroleum Reservoir simulation Numerical tuning 

List of symbols

rms

Root-mean-square

\(m\)

mth grid cell

\(\Delta t\)

Time increment

\(q_{{}}\)

Production rate from the grid cell (m3/day)

\(B_{{}}\)

Formation volume factor

\(V\)

Volume of the grid cell (m3)

\(\emptyset\)

Porosity of the grid cell

\(S_{{}}\)

Saturation

\(p_{\text{b}}\)

Bubble point pressure (kPa)

TSC

Time-step cuts

SCF

Solver convergence failures

Np

Cumulative oil production (m3)

Wp

Cumulative water production (m3)

Gp

Cumulative gas production (m3)

MBE

Material balance equation

SI

Solver iterations

NC

Newtonian cycles

TSC

Number of time-step cuts

TS

Time-step

CPU

Central processing unit (s)

NT

Numerical tuning

SD

Standard deviation

M

Median

Superscripts

\(n\)

nth time-step

Subscripts

f

Fluid phase (oil, water or gas)

o

Oil

w

Water

g

Gas

Notes

Acknowledgements

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. We would like to thank Samuel Ferreira de Mello (researcher at UNISIM’s Group with expertise in PVT modelling process) for modelling the fluid.

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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Department of Energy, School of Mechanical EngineeringUniversity of CampinasCampinas, São PauloBrazil

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