Practical workflow to improve numerical performance in time-consuming reservoir simulation models using submodels and shorter period of time

Rios, Victor de Souza; Avansi, Guilherme Daniel; Schiozer, Denis José

doi:10.1016/j.petrol.2020.107547

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Two approaches to the search for improved numerical parameters have been investigated. Firstly, CMG Designed Exploration and Controlled Evolution, a commercial algorithm, was used to tune the parameters [14,44]. Later work trained several machine learning algorithms to find a suitable oracle which could provide parameter settings based on output from previous simulations [15].…”

Section: Optimising Reservoir Simulationsmentioning

confidence: 99%

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

Saeternes,

Thune,

Rustad

et al. 2023

Preprint

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Section: Optimising Reservoir Simulationsmentioning

confidence: 99%

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

Saeternes,

Thune,

Rustad

et al. 2023

Preprint

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“…Another example of innovation over the classical optimization approach is given in [20]. The authors propose a workflow to select a representative numerical submodel (a slice of the overall grid) or a representative time interval (instead of the entire original period being simulated) that can be used in the optimization process and be representative of the whole model.…”

Section: Classical Reservoir Simulation Optimizationmentioning

confidence: 99%

“…The numerical parameter search space is given in Table 2. To select this subset of parameters, known as numerical keywords in the black-oil simulators, we use as a base the ones adopted in related works such as [16] and [20]. The default configuration refers to the values used internally by the flow simulator when the user did not explicitly provide the given parameters.…”

Section: Proposed Solutionmentioning

confidence: 99%

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

Portella,

Prats,

Rodrigues

et al. 2022

Preprint

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Reservoir simulations for petroleum fields and seismic imaging are known as the most demanding workloads for highperformance computing (HPC) in the oil and gas (O&G) industry. The optimization of the simulator numerical parameters plays a vital role as it could save considerable computational efforts. State-of-the-art optimization techniques are based on running numerous simulations, specific for that purpose, to find good parameter candidates. However, using such an approach is highly costly in terms of time and computing resources. This work presents TunaOil, a new methodology to enhance the search for optimal numerical parameters of reservoir flow simulations using a performance model. In the O&G industry, it is common to use ensembles of models in different workflows to reduce the uncertainty associated with forecasting O&G production. We leverage the runs of those ensembles in such workflows to extract information from each simulation and optimize the numerical parameters in their subsequent runs.To validate the methodology, we implemented it in a history matching (HM) process that uses a Kalman filter algorithm to adjust an ensemble of reservoir models to match the observed data from the real field. We mine past execution logs from many simulations with different numerical configurations and build a machine learning model based on extracted features from the data. These features include properties of the reservoir models themselves, such as the number of active cells, to statistics of the simulation's behavior, such as the number of iterations of the linear solver. A sampling technique is used to query the oracle to find the numerical parameters that can reduce the elapsed time without significantly impacting the quality of the results. Our experiments show that the predictions can improve the overall HM workflow runtime on average by 31%.

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“…Deeply understanding the seepage law of oil, gas, and water in reservoir porous media and the time-varying law of rock porous is the basis for improving the accuracy of reservoir numerical simulators, which has been widely studied by scholars in recent years (Xu et al, 2012;Jiang et al, 2018;Shen et al, 2019;Sun et al, 2019;Rios et al, 2020;Qiao, 2021).…”

Section: Introductionmentioning

confidence: 99%

The numerical simulation study on the dynamic variation of residual oil with water drive velocity in water flooding reservoir

Sun¹,

Liu²,

Cai³

et al. 2023

Front. Energy Res.

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The results of core displacement experiments show that increasing the water drive velocity when it is bigger than the limit value can effectively reduce the residual oil saturation and improve the oil displacement efficiency under the same PV. However, the existing commercial simulators (Eclipse, CMG et al.) cannot simulate the effect of water velocity on the relative permeability curve in the process of numerical simulation.In this article, capillary number (Ca), defined as the dimensionless ratio of viscous force to capillary force, is used to characterize the relationship between water drive velocity and residual oil. Second, a new Boltzmann (BG) equation is proposed to match the nonlinear relationship between Ca and residual oil. The BG equation is a continuous function, which is very beneficial to the stability of numerical calculation. Finally, a new reservoir numerical simulator is established which captures the dynamic variation of residual oil saturation with water drive velocity in a water flooding reservoir based on the black oil model. The new simulator was verified by comparing it with the commercial reservoir simulator ECLIPSE and experimental data. The simulation results show that compared with the common model, the model considering the dynamic variation of residual oil saturation with water drive velocity reduced the residual oil saturation near the main flow line after enhanced injection rate. The oil phase flow capacity in the model is enhanced, the water cut is decreased, and the oil recovery rate is higher. The history matching of the S oilfield in Bohai Bay is achieved with the new simulator, and the history matching accuracy is obviously higher than that of Eclipse. The findings of this study can help with a better understanding of the distribution law and flow law of remaining oil in the high water cut stage of the reservoir and have good theory and application value for water flooding offshore oilfields.

show abstract

Practical workflow to improve numerical performance in time-consuming reservoir simulation models using submodels and shorter period of time

Cited by 10 publications

References 10 publications

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads

The numerical simulation study on the dynamic variation of residual oil with water drive velocity in water flooding reservoir

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