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Automatic History Matching Method and Application of Artificial Intelligence for Fractured-Porous Carbonate Reservoirs

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PROCESSES 2024年第12期12卷 2634页

作者： Tong, Kaijun Song, Wentong Chen, Han Guo, Sheng Li, Xueyuan Sun, Zhixue CNOOC Int Ltd Beijing 100028 Peoples R China China Univ Petr Huadong Sch Petr Engn Qingdao 266580 Peoples R China

Fractured-porous carbonate reservoirs, mainly composed of dolomites and crystalline rocks with various rock types and extremely poor initial porosity and permeability, are dominated by tectonic fractures and exhibit extreme heterogeneity. The fracture system plays a predominant role in hydrocarbon fluid transport. Compared with conventional sandstone reservoirs, fracture geometry and topological structure parameters are key factors for the accuracy and computational efficiency of numerical simulation history matching in fractured reservoirs. To address the matching issue, this paper introduces an artificial intelligence history matching method combining the Monte Carlo experimental planning method with an artificial neural network and a particle swarm optimization algorithm. Taking reservoir geological parameters and phase infiltration properties as the objective function, this method performs reservoir production history matching to correct the geological model. Through case studies, it is verified that this method can accurately correct the geological model of fractured-porous reservoirs and match the observed production data. This research represents a collaborative effort among multiple disciplines, integrating advanced algorithms and geological knowledge with the expertise of computer scientists, geologists, and engineers. Currently the world's major oilfields history fitting is mainly based on reservoir engineers' experience to fit;the method is applicable to major oilfields, but the fitting accuracy and fitting efficiency is severely limited, the fitting accuracy is less than 75%, while the artificial intelligence history fitting method shows a stronger applicability;intelligent history fitting is mainly based on the integrity of the field data, and as far as the theory is concerned, the accuracy of the intelligent history fitting can be up to 100%. Therefore, AI history fitting can provide a significant foundation for mine field research. Future research cou

关键词： fractured-porous reservoir Monte Carlo test planning method particle swarm optimization algorithm artificial neural network artificial intelligence history matching

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Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

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SENSORS 2023年第3期23卷 1097-1097页

作者： Fang, Jun Deng, Zhiyang Tu, Jun Song, Xiaochun Hubei Univ Technol Sch Mech Engn Wuhan 430068 Peoples R China Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

关键词： glass panel chaos simulated annealing algorithm particle swarm optimization algorithm microwave detection quantitative identification

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Application of NIPOS-SVM Model for Evaluation of Water Richness of Coal Seam Roof Aquifer-A Case Study of the Xinhu Coal Mine in Huaibei, China

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WATER 2024年第24期16卷 3670-3670页

作者： Xing, Maolin Wang, Qiqing Xu, Jie Li, Wenping China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China CCTEG Xian Res Inst Grp Co Ltd Xian 710077 Peoples R China State Key Lab Coal Mine Disaster Prevent & Control Xian 710077 Peoples R China Shaanxi Key Lab Prevent & Control Technol Coal Min Xian 710077 Peoples R China

The sandstone roof of coal seams, with its high porosity and developed fissures, serves as a favorable reservoir for groundwater. Predicting and assessing the water-bearing capacity of the sandstone roof in coal seams is crucial for the rational development of coal tunnels, ensuring safe and efficient production in mining areas. This study targets the Cenozoic bottom aquifer of the No. 81 mining area of the Xinhu Coal Mine. By analyzing the geological and hydrogeological conditions of the mining area, it was found that the primary water-bearing strata of the coal seam roof are the Permian sandstone fracture waters. Key factors for evaluating the water richness of the sandstone aquifer were identified as aquifer thickness, aquifer depth, core recovery rate, coal seam dip angle, brittleness-plasticity ratio, and the sand-mud interlayer index. A novel particle swarm optimization algorithm incorporating improved sine chaos mapping (NIPSO) to enhance the support vector machine (SVM), thereby constructing the NIPSO-SVM model, was applied for quantitative evaluation of water richness in the study area. Experimental results indicated that the NIPSO-SVM model has high accuracy and practical engineering application value in predicting water richness, which is significant for ensuring the safe production of coal mines.

关键词： water-richness support vector machine model particle swarm optimization algorithm NIPSO GIS

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Intelligent Maintenance Scheduling System for Maximum Performance of Solar-Energy-Generating System

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SENSORS AND MATERIALS 2017年第11期29卷 1579-1588页

作者： Huang, Chung-Chi Far East Univ Dept Automat & Control Engn 49 Zhonghua Rd Tainan 74448 Taiwan

In this research, an intelligent maintenance scheduling system for gaining the maximum performance of a solar-energy-generating system is proposed. In the system, the particle swarm optimization (PSO) algorithm is used for the intelligent maintenance scheduling of the solar energy -generating system. The maintenance center receives many maintenance prescription requests from all solar-energy-generating stations. For various uncertainties, the optimal solution of maintenance scheduling is considered. To reduce maintenance costs and improve maintenance efficiency, the intelligent scheduling system using the PSO method is constructed. In the maintenance scheduling model, the maintenance time and prework time are set to be the optimal computing parameters, and the lowest cost is set to be the optimal target. By design and implementation, we can determine the advantages of the PSO-based intelligent system. It meets both requirements of real time and integration for intelligent systems. Experimental results show the advantages of the intelligent maintenance scheduling system for the maximum performance of the solar-energy-generating system;that is, such a scheduling system meets both requirements of efficiency and lowest cost for the intelligent maintenance of the solar-energy-generating system.

关键词： intelligent maintenance scheduling system particle swarm optimization algorithm solar energy

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optimization of Sports Fitness Management System Based on Internet of Health Things

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IEEE ACCESS 2020年 8卷 209556-209569页

作者： Tang, Yongquan Wang, Dehua Xinyang Normal Univ Sch Phys Educ Xinyang 464000 Peoples R China Sangmyung Univ Grad Sch Dept Phys Educ Seoul 03016 South Korea

Due to the huge scale of the Internet of Health Things (IoHT), various research methods currently applied in the field of Internet of Health Things are limited to the technical problems themselves, which lack a macro grasp of the entire Internet of Things (IoT) system. The verification of the proposed technical scheme requires the purchase of a large number of hardware devices, but as a result, only relatively limited research models can be constructed, which makes the entire system long management cycle and high management cost. Based on the research of particle swarm optimization algorithm, this paper finds its shortcomings and makes corresponding improvements. A new improved method of particle swarm optimization algorithm is proposed. By introducing the method of average particle distance and population distribution entropy in the algorithm search process, the inertia weight is dynamically changed. This is conducive to the improvement of the optimization efficiency of the particle swarm optimization algorithm, and has a good optimization accuracy. A IoHTs modeling method for component collaboration is proposed, and the IoHTs component library is designed according to the proposed modeling method. At the same time, this article discusses the XModel modeling simulation platform, and builds a IoHTs component model based on this platform, and has completed the verification of the validity of the proposed modeling method and the feasibility of the IoHTs IPv6 communication scheme. The component library designed in the XModel platform is based on the functions of IoT devices, and the components have more complete network communication features. The XModel platform specifically customizes the CMIoT component library, and supports the continuous expansion of the component library and the continuous refinement of component functions, providing a new method for communication research in the Internet of Health Things field.

关键词： Internet of Things Licenses Resource management Data models Sports Solid modeling optimization Internet of Health Things sports fitness management system XModel platform particle swarm optimization algorithm

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Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction

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ATMOSPHERE 2023年第11期14卷 1696-1696页

作者： Cai, Zijian Feng, Guolin Wang, Qiguang Yangzhou Univ Coll Phys Sci & Technol Yangzhou 225002 Peoples R China China Meteorol Adm Natl Climate Ctr Lab Climate Studies Beijing 100081 Peoples R China China Meteorol Adm China Meteorol Adm Training Ctr Beijing 100081 Peoples R China

In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved particle swarm optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R2) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.

关键词： chaotic sequence particle swarm optimization algorithm time-mode attention mechanism long short-term memory Lorenz system

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Fast reconstruction method for defect profiles of ferromagnetic materials based on metal magnetic memory technique

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MEASUREMENT 2023年第1期215卷

作者： Li, Junting Su, Sanqing Wang, Wei Liu, Xinwei Zuo, Fuliang Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

In this paper, a fast reconstruction method for surface defect profiles of ferromagnetic materials is proposed based on the metal magnetic memory technology. An improved magnetic charge model that can adapt to rectangular and V-shaped defect profiles and a new particle swarm optimization algorithm based on a chaotic initial distribution, sigmoid inertia weight coefficient, and sine cosine acceleration coefficients are established as the forward model and iterative means of the method, respectively. The proposed method is verified with theoretical and experimental data, and the influence of noise is considered. The reconstruction method has good accuracy, repeatability, and robustness.

关键词： Metal magnetic memory Defect reconstruction Magnetic charge model particle swarm optimization algorithm

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Designing a New Artificial Lift Method Using Computational Simulation and Evolutionary optimization

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IEEE ACCESS 2019年 7卷 123846-123859页

作者： Moreira, Andre Pimentel Lepikson, Herman Augusto Schnitman, Leizer Bezerra Ramalho, Geraldo Luis Fed Inst Educ Sci & Technol Ceara Dept Ind BR-60040215 Fortaleza Ceara Brazil Mfg & Technol Integrated Campus SENAI Inst Innovat Automat & Logist BR-41650010 Salvador BA Brazil Univ Fed Bahia Ctr Technol Qualificat Ind Automat BR-40210630 Salvador BA Brazil

Oil exploitation has moved into deeper reservoirs with the advances in drilling techniques and thus the development of new pumping techniques has become a challenge to improve production. The positive displacement pumping system proposed in this paper is presented as an artificial lift technique. Here we present a new pumping device, its estimated operational curves through numerical simulations and a prototype of the device, denominated a Double Acting Submersible Linear Pump (SLP), which is a hydraulic system adapted for inside well operations. Design parameters used in the tests were optimized using the particle swarm optimization (PSO) algorithm to maximize oil production while optimizing the parameters (characterstics) of the pump, such as maximum pump diameters and submersible electric motor power, pressure and volumetric displacement of the hydraulic pump. Also presented are the operating curves estimated from the numerical simulation of the power and pump modules, as well as the SLP0 and SLP1 experimental test curves in a controlled environment using the optimized parameters obtained using PSO The data obtained were compared to the computational simulations performed with the Automation Studio (TM) software. The SLP0 and SLP1 optimal design tests showed the importance to delimit the range of operation within which the SLP must operate, since the pipes and hydraulic components must be suitable for the flow of hydraulic oil and the production oil. The findings presented here play an important role in the production process of a fully operational SLP prototype.

关键词： Artificial lift technique petroleum industry particle swarm optimization algorithm computational modeling and simulation

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Shape optimization of U-shaped steel dampers subjected to cyclic loading using an efficient hybrid approach

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ENGINEERING STRUCTURES 2019年 197卷 108874-000页

作者： Khatibinia, Mohsen Jalaipour, Mehdi Gharehbaghi, Sadjad Univ Birjand Dept Civil Engn Birjand Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran

U-shaped steel damper (USSD), as an energy dissipation device, has been recommended in the literature for using in the isolation systems. This type of damper is capable of appropriately dissipating the input energy which a structure imparts from an earthquake. The capability is provided through a large range of plastic deformations occurred in the USSD. This paper aimed at presenting a methodology structured in the framework of a shape optimization problem to enhance the seismic energy dissipation and deformation capability of the USSD under cyclic loading. To achieve this goal, the straight part, thickness and height of the USSD were considered as the design variables of the optimization problem and optimized through maximizing the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain. In order to find the optimum shape of the USSD under cyclic loading, a hybrid approach consisted of two phases was applied. In the first phase, as an alternative for the time-consuming finite element analysis, a support vector machine (SVM) approach was trained, tested and used to predict the inelastic responses of the USSD. In the second phase, a modified particle swarm optimization (PSO) algorithm was adopted to find the optimum shape of the USSD subjected to two critical directions of cyclic loading. After finding the optimum shape of the USSD, the energy dissipation and deformation capability of the optimum shaped-USSD were assessed. Results demonstrate that the proposed shape optimization methodology renders an optimum-shaped USSD with significantly improved energy dissipation and deformation capability compared with those of available in the literature.

关键词： U-shaped steel damper Cyclic Loading Energy dissipation Shape optimization Support vector machine particle swarm optimization algorithm

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optimization procedures for the inversion of impedance spectra to the distribution of relaxation times

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JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年 911卷 116199-116199页

作者： Wang, Chunlin Zhu, Guangyan Zhang, Pengbo Fang, Xiaohong Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Pylon Technol Co Ltd Shanghai 201210 Peoples R China

Tikhonov regularization technology has been applied to solve the ill-posed inversion problem of electrochemical impedance spectra inversion to the distribution of relaxation times (DRT). However, the parameters contributing to the accuracy of the DRT obtained by this method, including types of the EIS data, regularization matrixes, regularization parameters, and shape factors, need sophisticated adjustment. In this work, the influences of these parameters were discussed in detail, and the results showed that the effects of some process parameters on the DRT worked complementarily. Therefore, the DRT inversion program with the particle swarm optimization algorithm (PSO), a multi-objective global optimization algorithm, was developed. It was demonstrated the accuracy, feasibility, and reliability of the proposed DRT program. The program was also applied to separate and quantify the electrochemical processes of the commercial LIBs.

关键词： Electrochemical impedance spectroscopy particle swarm optimization algorithm Distribution of relaxation time Tikhonov regularization

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