Uncertainty quantification and sensitivity analysis for relative permeability models of two-phase flow in porous media

Valdez, Andrés; Rocha, Bernardo Martins; Chapiro, Grigori; Santos, Rodrigo Weber dos

doi:10.1016/j.petrol.2020.107297

Cited by 40 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where p is the set of parameters to be estimated. Inverse UQ techniques are used to estimate the PDFs and corresponding uncertainties of the input parameters or coefficients of the model during model calibration [39] . In this work, for each parameter of the model, we estimated its PDF from the fitting procedure using the DE method.…”

Section: Model Calibration and Uncertainty Quantificationmentioning

confidence: 99%

Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil

Reis

Quintela

Campos

et al. 2020

Chaos, Solitons & Fractals

Self Cite

130

104

View full text Add to dashboard Cite

By April 7th, 2020, the Coronavirus disease 2019 (COVID-19) has infected one and a half million people worldwide, accounting for over 80 thousand of deaths in 209 countries and territories around the world. The new and fast dynamics of the pandemic are challenging the health systems of different countries. In the absence of vaccines or effective treatments, mitigation policies, such as social isolation and lock-down of cities, have been adopted, but the results vary among different countries. Some countries were able to control the disease at the moment, as is the case of South Korea. Others, like Italy, are now experiencing the peak of the pandemic. Finally, countries with emerging economies and social issues, like Brazil, are in the initial phase of the pandemic. In this work, we use mathematical models with time-dependent coefficients, techniques of inverse and forward uncertainty quantification, and sensitivity analysis to characterize essential aspects of the COVID-19 in the three countries mentioned above. The model parameters estimated for South Korea revealed effective social distancing and isolation policies, border control, and a high number in the percentage of reported cases. In contrast, underreporting of cases was estimated to be very high in Brazil and Italy. In addition, the model estimated a poor isolation policy at the moment in Brazil, with a reduction of contact around 40%, whereas Italy and South Korea estimated numbers for contact reduction are at 75% and 90%, respectively. This characterization of the COVID-19, in these different countries under different scenarios and phases of the pandemic, supports the importance of mitigation policies, such as social distancing. In addition, it raises serious concerns for socially and economically fragile countries, where underreporting poses additional challenges to the management of the COVID-19 pandemic by significantly increasing the uncertainties regarding its dynamics.

show abstract

Section: Model Calibration and Uncertainty Quantificationmentioning

confidence: 99%

Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil

Reis

Quintela

Campos

et al. 2020

Chaos, Solitons & Fractals

Self Cite

130

104

View full text Add to dashboard Cite

show abstract

“…However, in recent years, multiple authors have approached the standard multiphase problem by using a variety of tools and different settings [17]. Other applications of uncertainty quantification techniques to multiphase flows can be found in [18][19][20][21], but these concern different scenarios where the the goal is to estimate the uncertainty of expensive computational fluid dynamics (CFD) models, which are quite different from the data-driven models discussed here. However, despite their diversity, the ideas behind the uncertainty estimation tools are quite similar.…”

Section: Previous Workmentioning

confidence: 99%

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

et al. 2021

View full text Add to dashboard Cite

In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6).

show abstract

“…Relative permeability curves can describe the seepage state of multiphase fluid in porous media and play important roles in many aspects, such as history matching and the design of oil field development schemes. Studies on oil-water relative permeability [11][12][13][14][15][16][17][18][19][20][21][22] and oil-gas relative permeability [23][24][25][26][27] were carried out in recent years. For the acquisition of oil-water relative permeability curves, except that a few studies used some parameters to predict [16][17][18], most of them were measured by experiments [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Calculating the Relative Permeability Curve of Polymer Flooding Based on the Viscosity Variation Law of Polymer Transporting in Porous Media

Jiang

Hou

et al. 2022

Molecules

View full text Add to dashboard Cite

Relative permeability of polymer flooding plays a very important role in oil field development. This paper aimed to measure and calculate the relative permeability curves of polymer flooding more accurately. First, viscosity variation law of polymer in porous media was studied. Rock particles of different diameters and cementing agent were used to make artificial cores and hydrophobically associating polymer solutions were prepared for experiments. Polymer solutions were injected into the cores filled with crude oil and irreducible water. In the process of polymer flooding, produced fluid was collected at different water saturations and locations of the core. Polymer solutions were separated and their viscosities were measured. With the experimental data, the viscosity variation rule of polymer transporting in porous media was explored. The result indicates that the viscosity retention rate of polymer solutions transporting in porous media has power function relationship with the water saturation and the dimensionless distance from the core inlet. Finally, the relative permeability curves of polymer flooding were measured by unsteady state method and the viscosity variation rule was applied to the calculation of the relative permeability curves.

show abstract

Uncertainty quantification and sensitivity analysis for relative permeability models of two-phase flow in porous media

Cited by 40 publications

References 17 publications

Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil

Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

A New Method for Calculating the Relative Permeability Curve of Polymer Flooding Based on the Viscosity Variation Law of Polymer Transporting in Porous Media

Contact Info

Product

Resources

About