A New Method to Predict Gas Production Based on Fuzzy BP Artificial Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 688-691
Author(s):  
Ran Zhang ◽  
Jun Zhou ◽  
Cheng Yong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Gas Production ◽  
Gas Well ◽  
Well Productivity ◽  
Qualitative Factors ◽  
Productivity Prediction

BP neural network has been successfully used in the gas well productivity prediction, but as a result of neural network is sensitive to the number of input parameters, we had to ignore some factors that is less important to the gas well productivity. In addition, the existing various productivity prediction method cannot consider the influence of some important qualitative factors. This article integrated the advantages of fuzzy comprehensive evaluation and BP neural network, fuzzy comprehensive evaluation method is used to construct the BP neural network's input matrix, and BP neural network learning function is used to solve the connection weights, so as to achieve the aim of predicting gas production. This method not only can consider as many factors influence on gas well production, ut also can consider qualitative factors, so the forecast results of the new model are more realistically close to the actual production situation of reservoirs.

Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 528-533
Author(s):  
Yuan Sheng Huang ◽  
Wei Fang ◽  
Cheng Fang Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transmission Grid ◽  
Comprehensive Evaluation Method ◽  
System Method ◽  
Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

scholarly journals Data Driven Model of Gas Well Integrity and Its Application

2021 ◽  
Vol 2095 (1) ◽  
pp. 012092
Author(s):  
Jiangling Hong ◽  
Hongjie Zhang ◽  
Jiaqi Wang ◽  
Jinbo Liu ◽  
Honglei Liu ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Gas Production ◽  
Evaluation Index System ◽  
Production Plant ◽  
Fuzzy Evaluation ◽  
Gas Well ◽  
Time Dynamic ◽  
Well Integrity

Abstract In view of the problems that there are many well control risk points and the situation is grim of Xinjiang No.1 gas production plant, this paper carries out the gas well integrity evaluation and risk assessment, and establishes the comprehensive fuzzy evaluation model (FCEM) of gas well integrity. This paper analysed the integrity status of gas wells in Xinjiang No.1 gas production plant, establishes the integrity evaluation index system with well barrier components, real-time dynamic index and management organization as the main influence factors, determines the membership function of each index, calculates the weight of each index by using analytic hierarchy process(AHP), and establishes the risk degree calculation model by using fuzzy comprehensive evaluation, Quantitative analysis of gas well integrity. In this paper, a case study of a well in Xinjiang No.1 gas production plant shows that the model can quantitatively calculate the risk of gas well integrity and provide a reference for early warning of gas well integrity failure.

Risk evaluation of urban rainwater system waterlogging based on neural network and dynamic hydraulic model

2020 ◽  
Vol 39 (4) ◽  
pp. 5661-5671
Author(s):  
Cai Zhiming ◽  
Li Daming ◽  
Deng Lianbing
Keyword(s):  
Neural Network ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Rapid Development ◽  
Drainage System ◽  
Fuzzy Comprehensive Evaluation ◽  
Hydraulic Model ◽  
Risk Level ◽  
System Construction

With the rapid development of urban construction and the further improvement of the degree of urbanization, despite the intensification of the drainage system construction, the problem of urban waterlogging is still showing an increasingly significant trend. In this paper, the authors analyze the risk evaluation of urban rainwater system waterlogging based on neural network and dynamic hydraulic model. This article introduces the concept of risk into the study of urban waterlogging problems, combines advanced computer simulation methods to simulate different conditions of rainwater systems, and conducts urban waterlogging risk assessment. Because the phenomenon of urban waterlogging is vague, it is affected by a variety of factors and requires comprehensive evaluation. Therefore, the fuzzy comprehensive evaluation method is very suitable for solving the risk evaluation problem of urban waterlogging. In order to improve the scientificity of drainage and waterlogging prevention planning, sponge cities should gradually establish rainwater impact assessment and waterlogging risk evaluation systems, comprehensively evaluate the current capacity of urban drainage and waterlogging prevention facilities and waterlogging risks, draw a map of urban rainwater and waterlogging risks, and determine the risk level. At the same time, delineate drainage and waterlogging prevention zones and risk management zones to provide effective technical support for the formulation of drainage and storm waterlogging prevention plans and emergency management.

scholarly journals Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Xichun Luo ◽  
Honghao Zhao ◽  
Yan Chen
Keyword(s):  
Neural Network ◽  
Physical Exercise ◽  
Chronic Diseases ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Search Algorithm ◽  
Evaluation Process ◽  
Fuzzy Comprehensive Evaluation ◽  
Overweight And Obesity ◽  
Weighting Method

Due to the marked increase in the prevalence of overweight and obesity worldwide and an environment leading to a series of chronic diseases, physical exercise is an important way to prevent chronic diseases. Additionally, a good exercise smart bracelet can bring convenience to physical exercise. Quick and accurate evaluation of smart sports bracelets has become a hot topic and draws attention from both academic researchers and public society. In the literature, the analytic hierarchy process (AHP) and entropy weight method (EWM) were used to obtain the weights from both subjective and objective perspectives, which were integrated by the comprehensive weighting method, and furthermore the performance of sports smart bracelet was evaluated through fuzzy comprehensive evaluation. Also, to avoid complex weight calculations caused by the comprehensive weighting method, machine learning methods are used to model the structure and contribute to the comprehensive evaluation process. However, few studies have investigated all previous elements in the comprehensive evaluation process. In this study, we consider all previous parts when evaluating smart sports bracelets. In particular, we use the sparrow search algorithm (SSA) to optimize the backpropagation (BP) neural network for constructing the comprehensive score prediction model of the sports smart bracelet. Results show that the sparrow search algorithm-optimized backpropagation (SSA-BP) neural network model has good predictive ability and can quickly obtain evaluation results on the premise of effectively ensuring the accuracy of the evaluation results.

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