Output-Constraint Handling and Parallelization for Oil-Reservoir Control Optimization by Means of Multiple Shooting

SPE Journal ◽  
2015 ◽  
Vol 20 (04) ◽  
pp. 856-871 ◽  
Author(s):  
Andrés Codas ◽  
Bjarne Foss ◽  
Eduardo Camponogara
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Computation Time ◽  
Building Blocks ◽  
Programming Algorithm ◽  
Multiple Shooting ◽  
State Variables ◽  
Control Optimization ◽  
Output Constraints ◽  
Reservoir Simulator

Summary We propose to formulate and solve the reservoir-control optimization problem with the direct multiple-shooting method. This method divides the optimal-control problem prediction horizon in smaller intervals that one can evaluate in parallel. Further, output constraints are easily established on each interval boundary and as such hardly affect computation time. This opens new opportunities to include state constraints on a much broader scale than is common in reservoir optimization today. However, multiple shooting deals with a large number of variables because it decides on the boundary-state variables of each interval. Therefore, we exploit the structure of the reservoir simulator to conceive a variable-reduction technique to solve the optimization problem with a reduced sequential quadratic-programming algorithm. We discuss the optimization-algorithm building blocks and focus on structure exploitation and parallelization opportunities. To demonstrate the method's capabilities to handle output constraints, the optimization algorithm is interfaced to an open-source reservoir simulator. Then, on the basis of a widely used reservoir model, we evaluate performance, especially related to output constraints. The performance of the proposed method is qualitatively compared with a conventional method.

Gradient-based optimization method for interference suppression of linear arrays by the amplitude-only and phase-only control

2021 ◽  
pp. 1-7
Author(s):  
Renjing Gao ◽  
Yi Tang ◽  
Qi Wang ◽  
Shutian Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Interference Suppression ◽  
Computation Time ◽  
Optimization Method ◽  
Main Beam ◽  
Linear Arrays ◽  
Gradient Based

Abstract This paper presents a gradient-based optimization method for interference suppression of linear arrays by controlling the electrical parameters of each array element, including the amplitude-only and phase-only. Gradient-based optimization algorithm (GOA), as an efficient optimization algorithm, is applied to the optimization problem of the anti-interference arrays that is generally solved by the evolutionary algorithms. The goal of this method is to maximize the main beam gain while minimizing the peak sidelobe level (PSLL) together with the null constraint. To control the nulls precisely and synthesize the radiation pattern accurately, the full-wave method of moments is used to consider the mutual coupling among the array elements rigorously. The searching efficiency is improved greatly because the gradient (sensitivity) information is used in the algorithm for solving the optimization problem. The sensitivities of the design objective and the constraint function with respect to the design variables are analytically derived and the optimization problems are solved by using GOA. The results of the GOA can produce the desired null at the specific positions, minimize the PSLL, and greatly shorten the computation time compared with the often-used non-gradient method such as genetic algorithm and cuckoo search algorithm.

Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures

2017 ◽  
Vol 21 (3) ◽  
pp. 479-497 ◽  
Author(s):  
Andrés Codas ◽  
Kristian G. Hanssen ◽  
Bjarne Foss ◽  
Andrea Capolei ◽  
John Bagterp Jørgensen
Keyword(s):  
Risk Measures ◽  
Multiple Shooting ◽  
Coherent Risk Measures ◽  
Coherent Risk ◽  
Control Optimization ◽  
Output Constraints

scholarly journals Efficient Solution Algorithms for Factored MDPs

2003 ◽  
Vol 19 ◽  
pp. 399-468 ◽  
Author(s):  
C. Guestrin ◽  
D. Koller ◽  
R. Parr ◽  
S. Venkataraman
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Central Element ◽  
Dynamic Bayesian Network ◽  
Computation Time ◽  
Programming Algorithm ◽  
Small Subset ◽  
Planning Under Uncertainty ◽  
State Variables ◽  
Solution Algorithms

This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 10^40 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.

Rate control optimization algorithm based on Cauchy distribution for H.264/AVC

2010 ◽  
Vol 30 (9) ◽  
pp. 2444-2448
Author(s):  
Ke-ji WANG ◽  
Zhi-wei KANG ◽  
Xin-huan LIU ◽  
Bu-zhen CHEN
Keyword(s):  
Optimization Algorithm ◽  
Rate Control ◽  
Cauchy Distribution ◽  
Control Optimization

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

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