Salvador Pintos | University of Zulia (Universidad del Zulia) (original) (raw)

Papers by Salvador Pintos

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia, Dec 1, 2008

Page 1. Rev. Téc. Ing. Univ. Zulia. Vol. 31, Edición Especial, 50 - 60, 2008 An efficient respons... more Page 1. Rev. Téc. Ing. Univ. Zulia. Vol. 31, Edición Especial, 50 - 60, 2008 An efficient response surface approach for the optimization of ASP flooding processes Luis E. Zerpa1, Néstor V. Queipo1, Salvador Pintos1, Edwin Tillero2 ...

Journal of Petroleum Science and Engineering, Jun 1, 2005

After conventional waterflood processes the residual oil in the reservoir remains as a discontinu... more After conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline-surfactant-polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases.

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia, Dec 1, 2008

11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006

Surrogate-based optimization (SBO) for engineering design has become popular in the optimization ... more Surrogate-based optimization (SBO) for engineering design has become popular in the optimization of engineering systems (e.g., aerospace, automotive, oil industries) requiring expensive computer simulations. SBO proceed in design cycles, each cycle consisting of gathering input/output data using computer simulations, construction of a surrogate based on these data, estimation of the optimum using the surrogate, and a simulation at that optimum (PBS). However, due to time and cost constraints, the design optimization is limited to a small number of cycles (short cycle SBO) and rarely allowed to proceed to convergence. The current frontier of surrogate-based engineering design lacks statistically rigorous procedures for assessing the merit of investing in another cycle of analysis versus accepting the PBS. This paper presents a methodology to address this issue. The proposed methodology establishes an estimate of the probability of improving a specified target if another cycle (with a given set of points) is undertaken. It relies on three components: i) a covariance model (structure and parameters) obtained from available input/output data, ii) a surrogate model such as those provided by polynomial regression, kriging, and support vector regression, and iii) the assumption that the points in the next cycle are a realization of a Gaussian process with a covariance matrix and mean specified using i) and ii). Gaussian processes are frequently used for problems of regression and classification and are closely related to a variety of surrogate modeling approaches including neural networks, kriging, and generalized radial basis functions. In this study, a particular form of kriging is used to evaluate the proposed methodology considering that capturing a covariance model is at the core of this surrogate modeling approach. Validation results obtained using elements of statistical inference in the context of the SBO of the Branin-Hoo test function, and its application in the optimization of alkali-surfactant-polymer flooding of petroleum reservoirs is also discussed.

Engineering with Computers, 2004

Proceedings of Latin American & Caribbean Petroleum Engineering Conference, 2007

... An Efficient Response Surface Approach for the Optimization of ASP Flooding Processes: ASP Pi... more ... An Efficient Response Surface Approach for the Optimization of ASP Flooding Processes: ASP Pilot Project LL-03 Reservoir Luis E. Zerpa, SPE, Nestor V. Queipo, SPE, and ... 3. Meyers, JJ, Pitss, MJ, Wyatt, K. Alkaline–surfactant– polymer flood of the West Kiehl, Minnelusa Unit. ...

11th European Conference on the Mathematics of Oil Recovery, 2008

Engineering with Computers, 2003

Typically, the optimization of oil production systems is conducted as a non-systematic effort in ... more Typically, the optimization of oil production systems is conducted as a non-systematic effort in the form of trial and error processes for determining the combination of variables that leads to an optimal behavior of the system under consideration. An optimal or near optimal selection of oil production system parameters could significantly decrease costs and add value. This paper presents a solution methodology for the optimization of integrated oil production systems at the design and operational levels, involving the coupled execution of simulation models and optimization algorithms (SQP and DIRECT). The optimization refers to the maximization of performance measures such as revenue present value or cumulative oil production as objective functions, and tubing diameter, choke diameter, pipeline diameter, and oil flow rate as optimization variables. The reference configuration of the oil production system includes models for the reservoir, tubing, choke, separator, and business economics. The optimization algorithms Sequential Quadratic Programming (SQP) and DIRECT are considered as state-of-the-art in non-linear programming and global optimization methods, respectively. The proposed solution methodology effectively and efficiently optimizes integrated oil production systems within the context of synthetic case studies, and holds promise to be useful in more general scenarios in the oil industry.

Structural and Multidisciplinary Optimization, 2009

Structural and Multidisciplinary Optimization, 2008

This paper presents a general approach toward the optimal selection and ensemble (weighted averag... more This paper presents a general approach toward the optimal selection and ensemble (weighted average) of kernel-based approximations to address the issue of model selection. That is, depending on the problem under consideration and loss function, a particular modeling scheme may outperform the others, and, in general, it is not known a priori which one should be selected. The surrogates for the ensemble are chosen based on their performance, favoring non-dominated models, while the weights are adaptive and inversely proportional to estimates of the local prediction variance of the individual surrogates. Using both well-known analytical test functions and, in the surrogate-based modeling of a field scale alkalisurfactant-polymer enhanced oil recovery process, the ensemble of surrogates, in general, outperformed the best individual surrogate and provided among the best predictions throughout the domains of interest.

Mathematical Geosciences, 2011

... DP) coefficient, the most popular hetero-geneity static measure among petroleum engineers, ma... more ... DP) coefficient, the most popular hetero-geneity static measure among petroleum engineers, may exhibit significant sam-pling errors. ... 2009; Bossie-Codreanu and Le Gallo 2004; Adewusi 2002), reservoir heterogeneity clas-sification (Mergany 2007) and upscaling techniques ...

Journal of Petroleum Science and Engineering, 2007

After conventional waterflooding processes the residual oil in the reservoir remains as a discont... more After conventional waterflooding processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method so-called Alkaline-Surfactant-Polymer (ASP) flooding has been proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through reduction of interfacial tension and mobility ratio between oil and water phases.

Journal of Petroleum Science and Engineering, 2010

When constructing surrogate models, time/cost constraints make the designer frequently face the d... more When constructing surrogate models, time/cost constraints make the designer frequently face the dilemma of whether to use a small sample of data obtained from, for example, high fidelity/computationally expensive computer simulations, or, a large one but with low fidelity values. More generally, variable fidelity samples can be the result of: i) different physical/mathematical representations (e.g., inviscid/Euler versus viscous/Navier-Stokes calculations), ii) alternative resolution models (e.g., fine/coarse grids), or, iii) experiments. Ideally, surrogate models should allow: a) the integration of variable-fidelity samples, and, b) provide estimation and appraisal (error) information consistent with the amount and fidelity level of the available data. While there have been significant progress in this area through deterministic modeling and optimization approaches (e.g., correction surfaces, and space mapping), a stochastic perspective such as those provided by the branch of spatial statistics known as geostatistics offers distinctive advantages when satisfying the above referenced requirements (a & b). This paper discusses the effectiveness and requirements of geostatistical methods such as classic and collocated Cokriging for the integration of variable fidelity models. The discussion is illustrated using wellknown analytical functions and, alternative resolution models, in the surrogate-based modeling of a field scale alkali-surfactant-polymer (ASP) enhanced oil recovery (EOR) process. Nomenclature 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 10 -12 September 2008, Victoria, British Columbia Canada

Journal of Petroleum Science and Engineering, 2002

... The proposed methodology provides a global optimization method, hence avoiding the potential ... more ... The proposed methodology provides a global optimization method, hence avoiding the potential problem of convergence to a local minimum in the objective function exhibited by the commonly used Gauss–Newton methods Tan, 1995 and Landa and Horne, 1997, and ...

Journal of Petroleum Science and Engineering, 2002

TX 75083-3836, U.S.A., fax 01-972-952-9435.

Journal of Petroleum Science and Engineering, 2005

After conventional waterflood processes the residual oil in the reservoir remains as a discontinu... more After conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline-surfactant-polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases.

Journal of Optimization Theory and Applications, 1986

In this work, we study a differentiable exact penalty function for solving twice continuously dif... more In this work, we study a differentiable exact penalty function for solving twice continuously differentiable inequality constrained optimization problems. Under certain assumptions on the parameters of the penalty function, we show the equivalence of the stationary points of this function and the Kuhn-Tucker points of the restricted problem as well as their extreme points. Numerical experiments are presented that corroborate the theory, and a rule is given for choosing the parameters of the penalty function.

Journal of Hypertension, 2005

Objectives This study presents a reliable index inspired by the total variability concept of real... more Objectives This study presents a reliable index inspired by the total variability concept of real analysis in mathematics, called average real variability (ARV), for the prognostic significance of blood pressure variability (BPV) overcoming the pitfalls of the commonly used standard deviation (SD).

Journal of Global Optimization, 2013

In the context of surrogate-based optimization, most designers have still very little guidance on... more In the context of surrogate-based optimization, most designers have still very little guidance on when to stop considering optimum estimates are seldom available. Hence, cycles are typically stopped when resources run out (e.g., number of objective function evaluations/time) or convergence is perceived. This work presents an approach for estimating the minimum (target) of the objective function at a given cycle using concepts from extreme order statistics. It is assumed that the sample inputs are uniformly distributed so the outputs can be considered a random variable, whose density function is bounded, with the minimum as its lower bound. An estimate of the minimum (a density function bound) is then obtained through the moment matching method. The proposed approach is independent of the surrogate and optimization strategies and can be tailored to fit a variety of risk attitudes and design environments. The effectiveness of the proposed approach was evaluated using well-known analytical optimization test functions (F2 and Hartmann 6D). The results revealed that: a) the density function (from a catalog) with the best match to the function outputs distribution, was the same for both large and reduced samples, b) the true optimum value was always within a 95% confidence interval of the estimated minimum distribution, and c) the estimated minimum represents a significant improvement over the present best solution and a excellent approximation of the true optimum value.

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia, Dec 1, 2008

Page 1. Rev. Téc. Ing. Univ. Zulia. Vol. 31, Edición Especial, 50 - 60, 2008 An efficient respons... more Page 1. Rev. Téc. Ing. Univ. Zulia. Vol. 31, Edición Especial, 50 - 60, 2008 An efficient response surface approach for the optimization of ASP flooding processes Luis E. Zerpa1, Néstor V. Queipo1, Salvador Pintos1, Edwin Tillero2 ...

Journal of Petroleum Science and Engineering, Jun 1, 2005

After conventional waterflood processes the residual oil in the reservoir remains as a discontinu... more After conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline-surfactant-polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases.

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia, Dec 1, 2008

11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006

Surrogate-based optimization (SBO) for engineering design has become popular in the optimization ... more Surrogate-based optimization (SBO) for engineering design has become popular in the optimization of engineering systems (e.g., aerospace, automotive, oil industries) requiring expensive computer simulations. SBO proceed in design cycles, each cycle consisting of gathering input/output data using computer simulations, construction of a surrogate based on these data, estimation of the optimum using the surrogate, and a simulation at that optimum (PBS). However, due to time and cost constraints, the design optimization is limited to a small number of cycles (short cycle SBO) and rarely allowed to proceed to convergence. The current frontier of surrogate-based engineering design lacks statistically rigorous procedures for assessing the merit of investing in another cycle of analysis versus accepting the PBS. This paper presents a methodology to address this issue. The proposed methodology establishes an estimate of the probability of improving a specified target if another cycle (with a given set of points) is undertaken. It relies on three components: i) a covariance model (structure and parameters) obtained from available input/output data, ii) a surrogate model such as those provided by polynomial regression, kriging, and support vector regression, and iii) the assumption that the points in the next cycle are a realization of a Gaussian process with a covariance matrix and mean specified using i) and ii). Gaussian processes are frequently used for problems of regression and classification and are closely related to a variety of surrogate modeling approaches including neural networks, kriging, and generalized radial basis functions. In this study, a particular form of kriging is used to evaluate the proposed methodology considering that capturing a covariance model is at the core of this surrogate modeling approach. Validation results obtained using elements of statistical inference in the context of the SBO of the Branin-Hoo test function, and its application in the optimization of alkali-surfactant-polymer flooding of petroleum reservoirs is also discussed.

Engineering with Computers, 2004

Proceedings of Latin American & Caribbean Petroleum Engineering Conference, 2007

... An Efficient Response Surface Approach for the Optimization of ASP Flooding Processes: ASP Pi... more ... An Efficient Response Surface Approach for the Optimization of ASP Flooding Processes: ASP Pilot Project LL-03 Reservoir Luis E. Zerpa, SPE, Nestor V. Queipo, SPE, and ... 3. Meyers, JJ, Pitss, MJ, Wyatt, K. Alkaline–surfactant– polymer flood of the West Kiehl, Minnelusa Unit. ...

11th European Conference on the Mathematics of Oil Recovery, 2008

Engineering with Computers, 2003

Typically, the optimization of oil production systems is conducted as a non-systematic effort in ... more Typically, the optimization of oil production systems is conducted as a non-systematic effort in the form of trial and error processes for determining the combination of variables that leads to an optimal behavior of the system under consideration. An optimal or near optimal selection of oil production system parameters could significantly decrease costs and add value. This paper presents a solution methodology for the optimization of integrated oil production systems at the design and operational levels, involving the coupled execution of simulation models and optimization algorithms (SQP and DIRECT). The optimization refers to the maximization of performance measures such as revenue present value or cumulative oil production as objective functions, and tubing diameter, choke diameter, pipeline diameter, and oil flow rate as optimization variables. The reference configuration of the oil production system includes models for the reservoir, tubing, choke, separator, and business economics. The optimization algorithms Sequential Quadratic Programming (SQP) and DIRECT are considered as state-of-the-art in non-linear programming and global optimization methods, respectively. The proposed solution methodology effectively and efficiently optimizes integrated oil production systems within the context of synthetic case studies, and holds promise to be useful in more general scenarios in the oil industry.

Structural and Multidisciplinary Optimization, 2009

Structural and Multidisciplinary Optimization, 2008

This paper presents a general approach toward the optimal selection and ensemble (weighted averag... more This paper presents a general approach toward the optimal selection and ensemble (weighted average) of kernel-based approximations to address the issue of model selection. That is, depending on the problem under consideration and loss function, a particular modeling scheme may outperform the others, and, in general, it is not known a priori which one should be selected. The surrogates for the ensemble are chosen based on their performance, favoring non-dominated models, while the weights are adaptive and inversely proportional to estimates of the local prediction variance of the individual surrogates. Using both well-known analytical test functions and, in the surrogate-based modeling of a field scale alkalisurfactant-polymer enhanced oil recovery process, the ensemble of surrogates, in general, outperformed the best individual surrogate and provided among the best predictions throughout the domains of interest.

Mathematical Geosciences, 2011

... DP) coefficient, the most popular hetero-geneity static measure among petroleum engineers, ma... more ... DP) coefficient, the most popular hetero-geneity static measure among petroleum engineers, may exhibit significant sam-pling errors. ... 2009; Bossie-Codreanu and Le Gallo 2004; Adewusi 2002), reservoir heterogeneity clas-sification (Mergany 2007) and upscaling techniques ...

Journal of Petroleum Science and Engineering, 2007

After conventional waterflooding processes the residual oil in the reservoir remains as a discont... more After conventional waterflooding processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method so-called Alkaline-Surfactant-Polymer (ASP) flooding has been proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through reduction of interfacial tension and mobility ratio between oil and water phases.

Journal of Petroleum Science and Engineering, 2010

When constructing surrogate models, time/cost constraints make the designer frequently face the d... more When constructing surrogate models, time/cost constraints make the designer frequently face the dilemma of whether to use a small sample of data obtained from, for example, high fidelity/computationally expensive computer simulations, or, a large one but with low fidelity values. More generally, variable fidelity samples can be the result of: i) different physical/mathematical representations (e.g., inviscid/Euler versus viscous/Navier-Stokes calculations), ii) alternative resolution models (e.g., fine/coarse grids), or, iii) experiments. Ideally, surrogate models should allow: a) the integration of variable-fidelity samples, and, b) provide estimation and appraisal (error) information consistent with the amount and fidelity level of the available data. While there have been significant progress in this area through deterministic modeling and optimization approaches (e.g., correction surfaces, and space mapping), a stochastic perspective such as those provided by the branch of spatial statistics known as geostatistics offers distinctive advantages when satisfying the above referenced requirements (a & b). This paper discusses the effectiveness and requirements of geostatistical methods such as classic and collocated Cokriging for the integration of variable fidelity models. The discussion is illustrated using wellknown analytical functions and, alternative resolution models, in the surrogate-based modeling of a field scale alkali-surfactant-polymer (ASP) enhanced oil recovery (EOR) process. Nomenclature 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 10 -12 September 2008, Victoria, British Columbia Canada

Journal of Petroleum Science and Engineering, 2002

... The proposed methodology provides a global optimization method, hence avoiding the potential ... more ... The proposed methodology provides a global optimization method, hence avoiding the potential problem of convergence to a local minimum in the objective function exhibited by the commonly used Gauss–Newton methods Tan, 1995 and Landa and Horne, 1997, and ...

Journal of Petroleum Science and Engineering, 2002

TX 75083-3836, U.S.A., fax 01-972-952-9435.

Journal of Petroleum Science and Engineering, 2005

After conventional waterflood processes the residual oil in the reservoir remains as a discontinu... more After conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline-surfactant-polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases.

Journal of Optimization Theory and Applications, 1986

In this work, we study a differentiable exact penalty function for solving twice continuously dif... more In this work, we study a differentiable exact penalty function for solving twice continuously differentiable inequality constrained optimization problems. Under certain assumptions on the parameters of the penalty function, we show the equivalence of the stationary points of this function and the Kuhn-Tucker points of the restricted problem as well as their extreme points. Numerical experiments are presented that corroborate the theory, and a rule is given for choosing the parameters of the penalty function.

Journal of Hypertension, 2005

Objectives This study presents a reliable index inspired by the total variability concept of real... more Objectives This study presents a reliable index inspired by the total variability concept of real analysis in mathematics, called average real variability (ARV), for the prognostic significance of blood pressure variability (BPV) overcoming the pitfalls of the commonly used standard deviation (SD).

Journal of Global Optimization, 2013

In the context of surrogate-based optimization, most designers have still very little guidance on... more In the context of surrogate-based optimization, most designers have still very little guidance on when to stop considering optimum estimates are seldom available. Hence, cycles are typically stopped when resources run out (e.g., number of objective function evaluations/time) or convergence is perceived. This work presents an approach for estimating the minimum (target) of the objective function at a given cycle using concepts from extreme order statistics. It is assumed that the sample inputs are uniformly distributed so the outputs can be considered a random variable, whose density function is bounded, with the minimum as its lower bound. An estimate of the minimum (a density function bound) is then obtained through the moment matching method. The proposed approach is independent of the surrogate and optimization strategies and can be tailored to fit a variety of risk attitudes and design environments. The effectiveness of the proposed approach was evaluated using well-known analytical optimization test functions (F2 and Hartmann 6D). The results revealed that: a) the density function (from a catalog) with the best match to the function outputs distribution, was the same for both large and reduced samples, b) the true optimum value was always within a 95% confidence interval of the estimated minimum distribution, and c) the estimated minimum represents a significant improvement over the present best solution and a excellent approximation of the true optimum value.