scholarly journals PRODUCTION OPTIMIZATION PROBLEMS GENERALIZED UTILITY OF CONSUMPTION IN THE SOLOW MODEL WITH CONSTRAINTS OF VARIOUS KINDS

2018 ◽  
pp. 121-125
Author(s):  
A.Yu. Meerson ◽  
A.P. Chernyaev
Keyword(s):  
Optimization Problems ◽  
Production Optimization ◽  
Solow Model ◽  
Generalized Utility

scholarly journals Multicriteria Optimization of the Mechanical Properties in Laminated Seams

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2989
Author(s):  
Halina Szafranska ◽  
Ryszard Korycki
Keyword(s):  
Mechanical Properties ◽  
Optimization Problems ◽  
Comprehensive Evaluation ◽  
Relative Elongation ◽  
Weighted Average ◽  
Bending Rigidity ◽  
Elongation At Break ◽  
Lamination Process ◽  
Weighted Optimization ◽  
Generalized Utility

In order to ensure a comprehensive evaluation of laminated seams in working clothing, a series of research was carried out to determine the correlation between the parameters of the seam lamination process (i.e., the temperature, the time, the pressure) and the mechanical properties of laminated seams. The mechanical properties were defined by means of the maximum breaking force, the relative elongation at break and the total bending rigidity. The mechanical indexes were accepted as the measure of durability and stability of laminated seams. The correlation between the lamination process parameters and the final properties of the tested seams in working clothing was proposed using a three-factor plan 33. Finally, the single-criteria optimization was introduced and the objective functional is the generalized utility function U. Instead of three independent optimization problems, the single problem was applied, and the global objective function was a weighted average of partial criteria with the assumed weight values. The problem of multicriteria weighted optimization was solved using the determined weights and the ranges of acceptable/unacceptable values.

Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 105-118 ◽  
Author(s):  
Guodong Chen ◽  
Kai Zhang ◽  
Liming Zhang ◽  
Xiaoming Xue ◽  
Dezhuang Ji ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Surrogate Model ◽  
Net Present Value ◽  
Channel Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Production Optimization ◽  
Optimization Process ◽  
Promising Area ◽  
Global And Local

Summary Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.

Global and local optimization in identification of parabolic systems

2020 ◽  
Vol 28 (6) ◽  
pp. 899-913
Author(s):  
Olga Krivorotko ◽  
Sergey Kabanikhin ◽  
Shuhua Zhang ◽  
Victoriya Kashtanova
Keyword(s):  
Gradient Method ◽  
Online Social Networks ◽  
Optimization Problems ◽  
Initial Conditions ◽  
Gradient Methods ◽  
Parabolic Systems ◽  
Local Optimization ◽  
Solow Model ◽  
Unknown Parameters ◽  
Decomposition Approach

AbstractThe problem of identification of coefficients and initial conditions for a boundary value problem for parabolic equations that reduces to a minimization problem of a misfit function is investigated. Firstly, the tensor train decomposition approach is presented as a global convergence algorithm. The idea of the proposed method is to extract the tensor structure of the optimized functional and use it for multidimensional optimization problems. Secondly, for the refinement of the unknown parameters, three local optimization approaches are implemented and compared: Nelder–Mead simplex method, gradient method of minimum errors, adaptive gradient method. For gradient methods, the evident formula for the continuous gradient of the misfit function is obtained. The identification problem for the diffusive logistic mathematical model which can be applied to social sciences (online social networks), economy (spatial Solow model) and epidemiology (coronavirus COVID-19, HIV, etc.) is considered. The numerical results for information propagation in online social network are presented and discussed.

A Recurrent Neural Network–Based Proxy Model for Well-Control Optimization with Nonlinear Output Constraints

SPE Journal ◽  
2021 ◽  
pp. 1-21
Author(s):  
Yong Do Kim ◽  
Louis J. Durlofsky
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Optimization Problems ◽  
Production Optimization ◽  
Time Varying ◽  
Well Control ◽  
Control Optimization ◽  
Output Constraints ◽  
Simulation Based ◽  
Well Field

Summary In well-control optimization problems, the goal is to determine the time-varying well settings that maximize an objective function, which is often the net present value (NPV). Various proxy models have been developed to predict NPV for a set of inputs such as time-varying well bottomhole pressures (BHPs). However, when nonlinear output constraints (e.g., maximum well/field water production rate or minimum well/field oil rate) are specified, the problem is more challenging because well rates as a function of time are required. In this work, we develop a recurrent neural network (RNN)–based proxy model to treat constrained production optimization problems. The network developed here accepts sequences of BHPs as inputs and predicts sequences of oil and water rates for each well. A long-short-term memory (LSTM) cell, which is capable of learning long-term dependencies, is used. The RNN is trained using well-rate results from 256 full-order simulation runs that involve different injection and production-well BHP schedules. After detailed validation against full-order simulation results, the RNN-based proxy is used for 2D and 3D production optimization problems. Optimizations are performed using a particle swarm optimization (PSO) algorithm with a filter-basednonlinear-constraint treatment. The trained proxy is extremely fast, although optimizations that apply the RNN-based proxy at all iterations are found to be suboptimal relative to full simulation-based (standard) optimization. Through use of a few additional simulation-based PSO iterations after proxy-based optimization, we achieve NPVs comparable with those from simulation-based optimization but with speedups of 10 or more (relative to performing five simulation-based optimization runs). It is important to note that because the RNN-based proxy provides full well-rate time sequences, optimization constraint types or limits, as well as economic parameters, can be varied without retraining.

Bat Algorithm With Generalized Fly for Combinatorial Production Optimization Problems

2019 ◽  
pp. 34-66
Author(s):  
Latifa Dekhici ◽  
Khaled Guerraiche ◽  
Khaled Belkadi
Keyword(s):  
Flow Shop ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Bat Algorithm ◽  
Moment Generating Function ◽  
Production Optimization ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Discrete Version ◽  
First Case

A set of metaheuristics has proved its efficiency in solving rapidly NP-hard problems. Several combinatorial and continuous optimization areas drew profit from these powerful alternative techniques. This chapter intends to describe a discrete version of bat algorithm (BA) combined to generalized walk evolutionary (GEWA), also called bat algorithm with generalized fly or walk (BAG) in order to solve discrete industrial optimization. The first case of study is the well-known hybrid flow shop scheduling. The second one concerns the operating theatre that represents a critical manufacturing system, as the products delivered are patients. The last problem is the redundancy optimization (ROP) for series-parallel multi-state power system (MSS). Its resolution involves the selection of components with an appropriate level of redundancy to maximize system reliability with constrained cost. A universal moment generating function (UMGF) is used to estimate reliabilities. The modified bat algorithm on specific benchmarks was compared with the original one, and other results taken from the literature of each case study.

A Classification-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Production Optimization under Geological Uncertainty

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2450-2469
Author(s):  
Mengjie Zhao ◽  
Kai Zhang ◽  
Guodong Chen ◽  
Xinggang Zhao ◽  
Jun Yao ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Net Present Value ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Production Optimization ◽  
Benchmark Problems ◽  
Geological Uncertainty ◽  
Rbf Network ◽  
Multiobjective Evolutionary Algorithm ◽  
Computationally Expensive

Summary Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization under geological uncertainty that maximizes the expectation of net present value (NPV) over all possible uncertainty models and minimizes the variance simultaneously. However, the optimization process involves a large number of decision variables, and the problem is computationally expensive. Surrogate-assisted evolutionary algorithms (SAEAs), which have proved to be an effective way to solve expensive problems, design computationally inexpensive functions to approximate each objective function. On the basis of characterization, we have designed an efficient multiobjective evolutionary algorithm (MOEA) to effectively deal with computationally expensive simulation-based optimization problems. The uniqueness of this algorithm is that it incorporates a Pareto-rank-learning scheme with surrogate-assisted infill criterion. The first is to introduce a multiclass error-correcting output codes (ECOC) model that directly predicts the dominance relationship between candidate solutions and prescreens, and the second is to train a radial-basis function (RBF) network that predicts the objective functions of prescreened solutions to calculate the hypervolume (HV) improvement that maintains convergence and diversity. Compared with typical surrogate-based methods, the developed method provides a classifier first that can enhance the accuracy in high dimensions and reduce computational complexity. To the best of our knowledge, the proposed method compares with state-of-the-art surrogate frameworks for multiobjective production-optimization problems. In this paper, the proposed approach is applied to two 200D benchmark problems and two synthetic reservoir models. The results show that the proposed method can achieve more comprehensive and efficient reservoir management (RM) with a higher convergence speed compared with traditional MOEAs and surrogate-assisted optimization methods.

Use of Reduced-Order Modeling Procedures for Production Optimization

SPE Journal ◽  
2009 ◽  
Vol 15 (02) ◽  
pp. 426-435 ◽  
Author(s):  
M.A.. A. cardoso ◽  
L.J.. J. Durlofsky
Keyword(s):  
Optimization Problems ◽  
Dimensional Space ◽  
Reduced Order Modeling ◽  
Production Optimization ◽  
High Fidelity ◽  
Control Variables ◽  
Reduced Order ◽  
First Case ◽  
High Fidelity Simulations

Summary The determination of optimal well settings is very demanding computationally because the simulation model must be run many times during the course of the optimization. For this reason, reduced-order modeling procedures, which are a family of techniques that enable highly efficient simulations, may be very useful for optimization problems. In this paper, we describe a recently developed reduced-order modeling (ROM) technique that has been used in other application areas, the trajectory piecewise linearization (TPWL) procedure, and incorporate it in production-optimization computations. The TPWL methodology represents solutions encountered during the optimization runs in terms of Taylor-series expansions around previously simulated states. This requires a small number of preprocessing (training) simulations using the full (high-fidelity) model, during which pressure and saturation states and Jacobian matrices are saved. These states and matrices are then projected into a low-dimensional space using proper orthogonal decomposition (POD). Simulations in this reduced space can be performed very efficiently; in this work, we observe runtime speedups of a factor of 450. Overall speedups are, however, less because of the preprocessing overhead. We assess the TPWL representation for simulations of waterflood in a heterogeneous 3D model containing more than 20,000 gridblocks and six wells. The high degree of accuracy of the TPWL model is first demonstrated for several testing simulations in which producer- and injector-well settings differ from those used in the training runs. The TPWL representations are then used in optimizations involving the determination of optimal bottomhole pressures (BHPs) for a reservoir model with four production wells and two injection wells. A gradient-based algorithm is applied for the optimizations. In the first case, the BHPs of the producers and injectors are optimized at six different times (36 control variables) and in the second case at 15 different times (90 control variables). Results for optimized net present value (NPV) using TPWL are shown to be in consistently close agreement with those computed using high-fidelity simulations. Most significantly, when the optimal well settings obtained using the TPWL procedure are applied in high-fidelity models, the resulting NPVs are within approximately 0.5% of the values determined using the high-fidelity simulations. Our overall conclusion is that the TPWL representation may be quite useful in production-optimization problems.

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