Maximization of a Dynamic Quadratic Interpolation Model for Production Optimization

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1012-1025 ◽  
Author(s):  
Hui Zhao ◽  
Chaohui Chen ◽  
Sy Do ◽  
Diego Oliveira ◽  
Gaoming Li ◽  
...  

Summary We derive and implement a new optimization algorithm on the basis of a quadratic interpolation model (QIM) for the maximization (or minimization) of a cost or objective function. Although we have also applied the algorithm in other petroleum-engineering applications, this paper restricts the algorithm's application to the production-optimization step of closed-loop reservoir management in which the objective function is the net present value (NPV) of production from a given reservoir. The new algorithm does not require a gradient calculation with an adjoint method but does use an approximate gradient (AG). Thus, the general optimization algorithm is referred to as QIM-AG. QIM-AG represents a significant modification of an optimization algorithm—new unconstrained optimization algorithm (NEWUOA)—derived fairly recently in the mathematical literature. Production-optimization examples show that QIM-AG results in a higher NPV in fewer iterations than is obtained with NEWUOA. QIM-AG is also compared with two other optimization algorithms that use an AG—namely, a slightly improved implementation of ensemble optimization presented in this paper and a new implementation of simultaneous perturbation stochastic approximation (SPSA).

SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Faruk O. Alpak ◽  
Guohua Gao

Summary Field-development optimization and optimization at the pattern scale are crucial to maximize the value of thermal enhanced-oil-recovery (EOR) projects. Application of a field net-present-value (NPV)-based pattern optimization algorithm honoring field-scale surface and subsurface constraints for in-situ-upgrading (IUP) projects has been described in the recent past. In this paper, we describe the development and application of a novel field-development-optimization capability, including the optimization of the ramp-up phase to accelerate the production to achieve a faster cash flow and high surface-facility utilization. We integrate this new capability into a robust field NPV optimization platform. A two-stagefield-development optimization algorithm is developed in this work. First, the steady-state pattern is optimized using the field-scale pattern optimization algorithm while honoring field-scale constraints and using a combined surface and subsurface performance-indicator-driven objective function. Ramp-up pattern designs are optimized separately using a solely pattern-scaleperformance-driven objective function in this stage. A preliminary pattern-delay time optimization follows next to precondition the problem for the subsequent field-scale optimization stage. The ramp-up pattern and pattern-delay times are optimized using a constant steady-state pattern in the second step of the algorithm. An appropriately penalized field-NPV-based objective function is used in this step to enforce field-scale surface and subsurface constraints. Optimization results on a realistic example application indicate that the time to oil-rate plateau could be significantly reduced on the order of multiple years while honoring the surface production constraints. This requires the use of an optimized ramp-up pattern in conjunction with the optimal steady-state pattern. The ramp-up pattern is approximately two patterns wide and features an increased heater density to deliver production acceleration. It is also notably more robust against the effects of subsurface uncertainties.


2011 ◽  
Author(s):  
Hui Zhao ◽  
Chaohui Chen ◽  
Sy Thanh Do ◽  
Gaoming Li ◽  
Albert Coburn Reynolds

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski

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.


2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.


2020 ◽  
Vol 72 (12) ◽  
pp. 33-33
Author(s):  
Chris Carpenter

The final afternoon of the 2020 ATCE saw a wide-ranging virtual special session that covered an important but often overlooked facet of the unfolding digitalization revolution. While the rising wave of digital technology usually has been associated with production optimization and cost savings, panelists emphasized that it can also positively influence the global perception of the industry and enhance the lives of its employees. Chaired by Weatherford’s Dimitrios Pirovolou and moderated by John Clegg, J.M. Clegg Ltd., the session, “The Impact of Digital Technologies on Upstream Operations To Improve Stakeholder Perception, Business Models, and Work-Life Balance,” highlighted expertise taken from professionals across the industry. Panelists included petroleum engineering professor Linda Battalora and graduate research assistant Kirt McKenna, both from the Colorado School of Mines; former SPE President Darcy Spady of Carbon Connect International; and Dirk McDermott of Altira Group, an industry-centered venture-capital company. Battalora described the complex ways in which digital technology and the goal of sustainability might interact, highlighting recent SPE and other industry initiatives such as the GAIA Sustainability Program and reviewing the United Nations Sustainable Development Goals (SDGs). McKenna, representing the perspective of the Millennial generation, described the importance of “agile development,” in which the industry uses new techniques not only to improve production but also to manage its employees in a way that heightens engagement while reducing greenhouse-gas emissions. Addressing the fact that greater commitment will be required to remove the “tougher two-thirds” of the world’s hydrocarbons that remain unexploited, Spady explained that digital sophistication will allow heightened productivity for professionals without a sacrifice in quality of life. Finally, McDermott stressed the importance of acknowledging that the industry often has not rewarded shareholders adequately, but pointed to growing digital components of oil and gas portfolios as an encouraging sign. After the initial presentations, Clegg moderated a discussion of questions sourced from the virtual audience. While the questions spanned a range of concerns, three central themes included the pursuit of sustainability, with an emphasis on carbon capture; the shape that future work environments might take; and how digital technologies power industry innovation and thus affect public perception. In addressing the first of these, Battalora identified major projects involving society-wide stakeholder involvement in pursuit of a regenerative “circular economy” model, such as Scotland’s Zero Waste Plan, while McKenna cited the positives of CO2-injection approaches, which he said would involve “partnering with the world” to achieve both economic and sustainability goals. While recognizing the importance of the UN SDGs in providing a global template for sustainability, McDermott said that the industry must address the fact that many investors fear rigid guidelines, which to them can represent limitations for growth or worse.


2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  

Abstract Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.


2014 ◽  
Vol 986-987 ◽  
pp. 1954-1957
Author(s):  
Hai Feng Sun ◽  
Xiao Ming Wu ◽  
Chen Da Zheng ◽  
Xiao Qian Liu

This paper presents a new modeling method, based on the physical structure of the IGBT module, considering the distribution parameters of high frequency.This method is simple and the wideband model has physical meaning, by choosing suitable model initial parameters as well as the objective function which use the iterative optimization algorithm to solve the model parameters.Comparing the wideband model with the measured results ,the wideband model maintain high accuracy in the range of 100KHz ~ 20MHz.


2014 ◽  
Vol 986-987 ◽  
pp. 1151-1154
Author(s):  
Feng Ping Cao

Regards the compound auto transmission as the object of research, an optimal-power ratio optimization algorithm that takes into account the efficiency of CVT was presented in the paper. Firstly, the numerical models of drivetrain were established, which included the engine output torque model and the efficiency model of CVT. Then, the driving power was chosen as the dynamic target, and the optimized objective function was built. According to the requirement of shift gear, the ratio of shuttle transmission components was determined. Based on the Simulation X, the simulation car model with the compound auto transmission was design to confirm the validity and effectiveness of the proposed method.


2010 ◽  
Vol 132 (4) ◽  
Author(s):  
A. J. Marston ◽  
K. J. Daun ◽  
M. R. Collins

This paper presents an optimization algorithm for designing linear concentrating solar collectors using stochastic programming. A Monte Carlo technique is used to quantify the performance of the collector design in terms of an objective function, which is then minimized using a modified Kiefer–Wolfowitz algorithm that uses sample size and step size controls. This process is more efficient than traditional “trial-and-error” methods and can be applied more generally than techniques based on geometric optics. The method is validated through application to the design of three different configurations of linear concentrating collector.


2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.


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