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Stochastic simplex approximation gradient for reservoir production optimization: algorithm testing and parameter analysis

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期209卷 109755-109755页

作者： Xu, Jianchun Zhou, Wenxin Li, Hangyu Wang, Xiaopu Liu, Shuyang Fan, Ling China Univ Petr East China Minist Educ Key Lab Unconvent Oil & Gas Dev Qingdao 266580 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China

Production optimization is an effective technique to maximize the oil recovery or the net present value in reservoir development. Recently, the stochastic simplex approximation gradient (StoSAG) optimization algorithm draws significant attention in the optimization algorithm family. It shows high searching quality in largescale engineering problems. However, its optimization performance and features are not fully understood. This study evaluated and analyzed the influence of some key parameters related to the optimization process of StoSAG including the ensemble size to estimate the approximation gradient, the step size, the cut number, the perturbation size, and the initial position by using 47 mathematical benchmark functions. Statistical analysis was employed to diminish the randomness of the algorithm. The quality of the optimization results, the convergence, and the computational time consuming were analyzed and compared. The parameter selection strategy was presented. The results showed that a larger ensemble size was not always favorable to obtain better optimization results. The increase of the search step size was favorable to escape from the local optimum. A large step size needed to match a large cut number. The increase of cut number was beneficial to increase the local searchability, but also made the algorithm more easily fall into the local optimum. The random initial position was beneficial to find the global optimal point. Moreover, the effectiveness of the parameter selection strategy was tested by a classical reservoir production optimization example. The final net present value (NPV) for water flooding reservoir production optimization substantially increased, which indicated the excellent performance of StoSAG by adjusting the key parameters.

关键词： Stochastic simplex approximation gradient Production optimization algorithm testing Computational cost Reservoir numerical simulation

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An algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its testing Based on the Example of Variations in the CO2 Concentration in the Atmosphere

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2022年第7期58卷 681-707页

作者： Deshcherevskii, A. V. Sidorin, A. Ya. Russian Acad Sci Schmidt Inst Phys Earth Moscow 123242 Russia

An adaptive model is proposed to describe time-varying seasonal effects. The seasonal average function is constructed using an iterative algorithm that provides a neat decomposition of the signal into a generalized trend, seasonal and residual components. By a trend, we mean long-term evolutionary changes in the average signal level, both unidirectional and chaotic, in the form of a slow random drift. This algorithm allows one to obtain unbiased estimates for each of the signal components, even in the presence of a significant number of missing observations. The series length is not required to be a multiple of an integer number of years. In contrast to the usual "Climate Normals" (CN) model, the considered adaptive model of seasonal effects assumes a continuous slow change in the properties of the seasonal component over time. The degree of allowable variability in seasonal effects from year to year is entered as a tunable parameter of the model. In particular, this allows one to show the dynamics of the growth of the amplitude of seasonal fluctuations in time in the form of a continuous (smooth) function without necessarily linking these changes to predetermined calendar epochs. The algorithm was tested on the atmospheric CO2 concentration monitoring series at Barrow, Mauna Loa, Tutuila, and South Pole stations located at different latitudes. The form of the seasonal variation was estimated, and the average amplitude of the seasonal variation and the rate of its change at each station were calculated. Noticeable differences in the dynamics of the studied parameters between stations are demonstrated. Mean amplitude of seasonal variation in CO2 concentration at Barrow, Mauna Loa, Tutuila, and South Pole stations in the epoch 2010-2019 was estimated as 18.15, 7.08, 1.30, and 1.26 ppm, respectively, and the average rate of increase in the amplitude of the seasonal variation in the increase in CO2 concentration in the interval 1976-2019 is 0.085, 0.0100, 0.0165, and 0.

关键词： time series time series analysis time series decomposition iterative algorithm trend periodic components seasonal periodicity algorithm testing carbon dioxide CO2 concentration in the atmosphere

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Iterative algorithm for Time Series Decomposition into Trend and Seasonality: testing Using the Example of CO2 Concentrations in the Atmosphere

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2021年第8期57卷 813-836页

作者： Deshcherevskii, A. V. Sidorin, A. Ya. Russian Acad Sci Schmidt Inst Phys Earth Moscow Russia

An iterative algorithm for the decomposition of data series into trend and residual (including the seasonal effect) components is proposed. This algorithm is based on the approaches proposed by the authors in several previous studies and allows unbiased estimates for the trend and seasonal components for data with a strong trend containing different periodic (including seasonal) variations, as well as observational gaps and omissions. The main idea of the algorithm is that both the trend and the seasonal components should be estimated using a signal that is maximally cleaned of any other variations, which are considered a noise. In estimating the trend component, seasonal variation is a noise, and vice versa. The iterative approach allows a priori information to be more completely used in the optimization of models of both trend and seasonal components. The approximation procedure provides maximum flexibility and is fully controllable at all stages of the process. In addition, it allows one to naturally solve the problems in the case of missing observations and defective measurements without filling these dates with artificially simulated values. The algorithm was tested using data on changes in the concentration of CO2 in the atmosphere at four stations belonging to different latitudinal zones. The choice of these data is explained by the features that complicate the use of other methods, namely, high interannual variability, high-amplitude seasonal variations, and gaps in the series of observed data. This algorithm made it possible to obtain trend estimates (which are of particular importance for studying the characteristics and searching for the causes of global warming) for any time interval, including those that are not multiples of an integer number of years. The rate of increase in the CO2 content in the atmosphere has also been analyzed. It has been reliably established that in around 2016, the rate of CO2 accumulation in the atmosphere became stabilized and

关键词： time series time series analysis time series decomposition iterative algorithm trend periodic components seasonal periodicity algorithm testing CO2 concentration in the atmosphere

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Regularization of the Solution of the Inverse VES Problem by the Contrast Stabilization Method: testing the algorithm on Model Data

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SEISMIC INSTRUMENTS 2022年第5期58卷 581-600页

作者： Bobachev, A. A. Deshcherevskii, A. V. Sidorin, A. Ya. Lomonosov Moscow State Univ Moscow 119991 Russia Russian Acad Sci Schmidt Inst Phys Earth Moscow 123242 Russia

Earlier, it was shown that conventional algorithms for solving the inverse VES problem cannot achieve the accuracy required for precision monitoring of a geoelectric section, and regularized algorithms were proposed to improve the accuracy and stability of solving the inverse VES problem. In this paper, we test the resistivity contrast stabilization algorithm on synthetic data. For modeling, a geoelectric section is used, similar to the section of the Garm test site both in the set of layers and their resistivities, and in the characteristics of seasonal variations, as well as noise. It is shown that regularization of the inverse problem greatly reduces errors. The most significant effect is achieved by suppressing the buildup of resistivity. Estimates are obtained for the accuracy in solving the inverse problem, which can be achieved when working with experimental data.

关键词： VES data inversion solution regularization contrast stabilization algorithm testing model series

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An n-dimensional Rosenbrock distribution for Markov chain Monte Carlo testing

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SCANDINAVIAN JOURNAL OF STATISTICS 2022年第2期49卷 657-680页

作者： Pagani, Filippo Wiegand, Martin Nadarajah, Saralees Univ Cambridge MRC Biostat Unit Cambridge CB2 0SR England Univ Manchester Dept Math Manchester Lancs England

The Rosenbrock function is a ubiquitous benchmark problem in numerical optimization, and variants have been proposed to test the performance of Markov chain Monte Carlo algorithms on distributions with a curved and narrow shape. In this work we discuss the Rosenbrock distribution and the advantages and limitations of its current n-dimensional extensions. We then propose a new extension to arbitrary dimensions called the Hybrid Rosenbrock distribution, which addresses all the limitations that affect the current extensions. The Hybrid Rosenbrock distribution is composed of conditional normal kernels arranged in such a way that preserves the key features of the original Rosenbrock kernel. Moreover, due to its structure, the Hybrid Rosenbrock distribution is analytically tractable, and possesses several desirable properties which make it an excellent test model for computational algorithms. We conclude with numerical experiments that show how commonly used Markov chain Monte Carlo algorithms may fail to explore densities with curved correlation structure, restating the importance of a reliable benchmark problem for this class of densities.

关键词： algorithm testing benchmarking Markov chain Monte Carlo MCMC Rosenbrock

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A Comprehensive Evaluation of the DFP Method for Geometric Constraint Solving algorithm Using PlaneGCS

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第6期30卷 2026-2035页

作者： Sun, Yunlei Li, Yucong Liu, Kangping China Univ Petr East China Coll Comp Sci & Technol Qingdao Inst Software Qingdao 266580 Peoples R China

The development of open-source geometric constraint solvers is a pressing research topic, as commercially available solvers may not meet the research requirements. In this paper, we examine the use of numerical methods in PlaneGCS, an open-source geometric constraint solver within the FreeCAD CAD software. Our study focuses on PlaneGCS's constraint solving algorithms and the three built-in single-subsystem solving methods: BFGS, LM, and Dogleg. Based on our research results, the DFP method was implemented in PlaneGCS and was successfully verified in FreeCAD. To evaluate the performance of the algorithms, we used the solving state of the constraint system as a test criterion, and analysed their solving time, adaptability, and number of iterations. Our results highlight the performance differences between the algorithms and provide empirical guidance for selection of constraint solving algorithms and research based on open-source geometric constraint solvers.

关键词： algorithm testing DFP method numerical constraint solving algorithm open-source geometric constraint solver

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An enhanced distance-based parameter adaptation multi-population ensemble differential evolution

An enhanced distance-based parameter adaptation multi-popula...

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2024 International Conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Li, Xiangping Huang, Yingqi School of Information Engineering Guizhou University of traditional Chinese Medicine Guiyang China

ISBN: (纸本)9781510685840

Usually, mutation strategies and the corresponding parameter values play an important role in DE. Because different mutation strategies reveal different characteristic during the course of the evolution, combing different mutation strategies has been one of the research direction for DE improvement. In the work, a novel algorithm (eD-MPEDE) is introduced, which applies distance based parameter adaptation to produce competitive trial vectors. Furthermore, the candidate pool of eD-MPEDE contains three novel mutation strategies with different characteristics, which help to balance exploratory and convergence. Tests on CEC2017 show that eD-MPEDE is efficient to obtain promising solutions. © 2024 SPIE.

关键词： algorithm testing Data analysis Evolutionary optimization

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Research on single-phase line fault detection algorithm based on primary and secondary fusion column switches 9

Research on single-phase line fault detection algorithm base...

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9th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2024

作者： Li, Qiang Li, Wenjing Liu, Mian Hu, Yuanyuan Huang, Lvchao State Grid Information & Telecommunication Group Co. Ltd Beijing China

ISBN: (纸本)9781510683303

The accurate identification and diagnosis of single-phase line faults in distribution networks based on primary and secondary fusion column switches have long been challenging due to the lack of effective detection and protection algorithms, directly impacting the safety of distribution lines. To address this issue, this study investigates the variations in sequence current and line voltage signals on the power source side and load side of single-phase line faults based on composite sequence networks. By leveraging the easily observable characteristics of the magnitude jump of positive and negative sequence currents of the faulted phase, the fault type is indexed. Additionally, the fault location is determined based on the characteristics of line voltage magnitude at different positions along the faulted line. Through Matlab simulation and experimental testing on a test bench, the results demonstrate that the fault detection algorithm, which integrates the variations in sequence current and line voltage magnitudes, can successfully achieve fault localization, laying the foundation for the design of subsequent fault detection schemes with superior performance. © 2024 SPIE.

关键词： Detection and tracking algorithms algorithm testing Switches Circuit switching Computer simulations MATLAB Power supplies Design Transformers Telecommunications

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Revisiting Facial Age Estimation With New Insights From Instance Space Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第5期44卷 2689-2697页

作者： Smith-Miles, Kate Geng, Xin Univ Melbourne Sch Math & Stat Melbourne Vic 3010 Australia Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

When demonstrating the effectiveness of a new algorithm, researchers are traditionally encouraged to compare their algorithm's performance against existing algorithms on well-studied benchmark test suites. In the absence of more nuanced methodologies, algorithm performance is typically summarized on average across the test suite examples. This paper highlights the potential bias of conclusions drawn by analyzing "on average" performance, and the opportunities offered by a recent testing methodology known as instance space analysis. To illustrate, we revisit our 2007 comparative study of algorithms for facial age estimation, and rigorously stress-test to challenge the original conclusions. The case study demonstrates how powerful visualizations offered by instance space analysis enable greater insights into unique strengths and weaknesses, and which algorithm should be used when and why. Inspired by such insights, a new algorithm is proposed, and its unique advantage is demonstrated. The bias often hidden in well-studied datasets, and the ramifications for drawing biased conclusions, are also illustrated in this case study. While focused on facial age estimation, the methodology and lessons learned from the case study are broadly applicable to any study seeking to draw conclusions about algorithm performance based on empirical results.

关键词： Faces Prediction algorithms Shape Estimation Machine learning algorithms Clustering algorithms Feature extraction algorithm testing instance space analysis facial age estimation machine learning no-free-lunch theorem

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An enhanced differential evolution with ensemble of mutation strategies for numerical optimization

An enhanced differential evolution with ensemble of mutation...

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Li, Xiangping Huang, Yingqi College of Information Engineering Guizhou University of Traditional Chinese Medicine Guiyang China

ISBN: (纸本)9781510689312

For about two decades Differential Evolution (DE) algorithms have become one of the most successful optimization meta-heuristics. However, solving single objective real-parameter problem is still a challenging task. In the work, an enhanced differential evolution with ensemble of mutation strategies is proposed, referred to as EDEM. In EDEM, three mutation strategies are used to simultaneously execute evolution, namely "current-to-pbest/1", "current-to-pbad/1" and "rand-to-mbest/1". To balance the exploration and exploitation, a linear reduction method is used to adapt memory size and coefficient scale. To verify the feasibility and effectiveness of EDEM, 25 test functions from CEC2005 were simulated. The numerical results indicate that the proposed approach is competitive with the other state-of-the-art DE variants. © The Authors.

关键词： Evolutionary optimization Chromium algorithm testing Engineering Lithium Magnesium Mathematical optimization Matrices Medicine Stochastic processes

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