Sensitivity Analysis and Optimization of Surfactant-Polymer Flooding under Uncertainties

Douarche, F.; Veiga, Sébastirn Da; Feraille, Mathieu; Enchéry, Guillaume; Touzani, Samir; Barsalou, R.

doi:10.2516/ogst/2013166

Cited by 30 publications

(13 citation statements)

References 18 publications

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“…Proxy models, which are used to bypass the flow simulator, are a common simplification in uncertainty quantification, history matching, and probabilistic forecasting (Douarche et al, 2014;Feraille, 2013;Feraille and Marrel, 2012;Imrie and Macrae, 2016;Osterloh, 2008;Panjalizadeh et al, 2014;Scheidt et al, 2007;Touzani and Busby, 2014). However, multiple factors affect prediction accuracy of the proxy, which is not physics-based: (1) the high nonlinearity between input variables (reservoir, operational, and economic uncertainties) and output variables (production, injection, and economic forecasts) complicates proxy modeling, and (2) assumptions and approximations when modeling the proxy may introduce non-negligible errors (Trehan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

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Section: Introductionmentioning

confidence: 99%

Model-based decision analysis applied to petroleum field development and management

Schiozer

Santos

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…According to Thomas [2], EOR methods consist of chemical, electrical, miscible, steam and thermal techniques. These methods have proven to boost oil recovery from mature reservoirs when conventional methods (water flooding) no longer work [3]. Chemical enhanced oil recovery comprehends water injection with different additives to enhance the removal of oil from the reservoir.…”

Section: Introductionmentioning

confidence: 99%

Effects of EOR chemicals and superficial gas velocity on bubble size and gas holdup of a bubble column

Orlando

Barca

Klein

et al. 2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Chemical Enhanced Oil Recovery (EOR) can boost oil extraction in offshore operations, however one of the main concerns regarding its application is how the efficiency of flotation units for treating produced water is affected. The present work thus focuses on investigating the impact of EOR chemicals on the physical properties of EOR effluents and how this can affect flotation performance parameters such as bubble size and gas holdup. Design of experiments has been used to assess the influence of polymer, surfactant and sodium chloride concentrations on bubble size and gas holdup of a laboratorial bubble column. The influence of superficial gas velocity has also been assessed together with chemicals concentrations, yet at low levels in order to avoid clusters, swarms and foam. The characterization of the synthetic effluent containing polymer, surfactant and sodium chloride has indicated that the fluid behaves as a non-Newtonian fluid, what makes separation processes in flotation cells challenging. Results showed that polymer concentration of 2000 mg/L can lead to significant increases in fluid viscosity, promote a growth of more than 40% in bubble size and only increases gas holdup when surfactant is present at high concentration. Therefore, polymers are expected to be detrimental to produced water treatment. Surfactants decrease both fluid surface tension and bubble size, increasing gas holdup. For the range studied, superficial gas velocity favors gas holdup and sodium chloride concentration seems to weakly influence bubble size and gas holdup. This work highlights the fact that changes in physical properties of produced water do modify bubble size distribution and gas holdup and this must therefore be taken into account when flotation-like systems are designed to deal with EOR effluents.

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“…Optimization based on Response Surface Modeling (RSM) can help to choose an optimal design. Several approaches have been proposed for chemical EOR processes (Douarche et al, 2014;AlSofi and Blunt, 2011;Horowitz et al, 2010;Zerpa et al, 2007). Jakobsen et al (1994) applied a numerical simulation to optimize well arrangements and surfactant slug size in the North Sea sandstone reservoir.…”

Section: Introductionmentioning

confidence: 99%

“…Water production for different cases of RSM-based sensitivity analysis Alkaline production for different cases of RSM-based sensitivity analysis Surfactant production for different cases of RSM-based sensitivity analysis(7) …”

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confidence: 99%

Sensitivity Analysis and Optimization of Alkaline-Surfactant Flooding in a Thin Clastic Reservoir

Ghadami

Das

Tunio

et al. 2015

Day 4 Wed, December 09, 2015

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Chemical EOR is one of the promising methods to improve the oil recovery. However, due to high cost of the process, there are challenges to minimize the cost and maximize the oil recovery. Some influencing parameters should be taken into account in a systematic approach to find their impact on oil recovery and accordingly optimizing the process.In this study, we present a robust optimization workflow of alkaline-surfactant (AS) flooding into a thin clastic reservoir of a field in the Malay Basin. There are coreflood experiments and pilot tests on this field that can be quite helpful to provide a basis to find out the appropriate range of input parameters. Optimization work is based on response surface methodology (RSM) and particle swarm optimization (PSO) technique that aid us to indicate the optimum oil recovery from chemical flooding. In order to get the utmost advantage of this workflow, the waterflooding should be optimized prior to the chemical flooding optimization to maximize the sweep efficiency and oil recovery from the chemical flood.Evaluation of coreflood and pilot tests indicated that some parameters need supplementary evaluation to investigate their effect on reservoir performance and flow dynamics. These parameters include residual oil reduction by chemical, relative permeability curves, chemicals adsorption, chemical concentration, slug size, injection rate, and initiation time of chemical injection. Based on the result of tornado chart, residual oil reduction and injection rate exhibited highest and lowest impact on oil recovery. RSM was used to explore the relationship between input variables and objective function. Some design parameters such as chemical concentration, slug size and initiation time were examined in this stage. Afterwards, proxy models have been built using polynomial regression and neural network methods. The results showed that the proxy model by neural network method revealed better performance for prediction of the simulation results. The proxy model was used to calculate the oil recovery for any combination of input parameters. Besides, it was used to assess the parameter sensitivity and identify the impact of any input parameter on oil recovery. At the next stage, PSO method was utilized to optimize the oil recovery by chemical flooding. It was found that the optimized water injection rate and pattern for water flooding scenario need further optimization to improve the sweep efficiency and thereby oil recovery by AS flooding at later stage. Running numerous simulation cases is normally expected to optimize the process by conventional methods and the proposed PSO approach can be used to reduce the number of runs significantly. Sensitivity analysis provided a very good understanding about reserve ranges for the different influential parameters. Optimizing the cost of chemical flooding and improving oil recovery are other outcomes of this study.

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Sensitivity Analysis and Optimization of Surfactant-Polymer Flooding under Uncertainties

Cited by 30 publications

References 18 publications

Model-based decision analysis applied to petroleum field development and management

Model-based decision analysis applied to petroleum field development and management

Effects of EOR chemicals and superficial gas velocity on bubble size and gas holdup of a bubble column

Sensitivity Analysis and Optimization of Alkaline-Surfactant Flooding in a Thin Clastic Reservoir

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