scholarly journals Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

2021 ◽  
Vol 14 (4) ◽  
Author(s):  
Jing Sun ◽  
Yuting Zhao ◽  
ChangQing Yang ◽  
Xuanlong Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Oil Sands ◽  
Back Propagation ◽  
Songliao Basin ◽  
Source Rocks ◽  
Western Slope ◽  
Oil Sand ◽  
Neural Network Method ◽  
Network Method

AbstractHigh-quality hydrocarbon source rocks are present in the upper Cretaceous layer in the western slope of the Songliao Basin. Oil and gas have accumulated in these rocks at the shallow edge of the basin, which has led to the formation of oil sand resources. This study uses the back-propagation (BP) neural network method to predict the distribution of oil sand reservoirs and is the first study of its kind in China. First, based on the basic data collected by core sample, well log and geochemical analyses, and the reasonable selection of samples, the cores are divided into mudstone, siltstone, fine sandstone, medium sandstone, and sand, according to lithology. Second, a three-layer BP neural network model is constructed with two hidden S-type layers and one linear output layer. Third, through a comparison of the effect of different numbers of training sessions of the sample data, we demonstrate that the accuracy of the model can be increased to 90% after training the network 100,000 times. Then, the log-derived data of rocks with unknown lithologies are input into the neural network to predict whether they contain oil sands. We show that the BP neural network method can predict the distribution of oil sand reservoirs in the target horizon of the study area, and the results are consistent with research results on the corresponding sand reservoirs and sedimentary facies. Thus, we conclude that it is feasible to use the BP neural network method to predict the distribution of oil sand reservoirs.

Nursing Scheme Based on Back Propagation Neural Network and Probabilistic Neural Network in Chronic Kidney Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 416-421 ◽  
Author(s):  
Xuxia Ying ◽  
Bibo Tang ◽  
Canxin Zhou
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Bp Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Model Errors ◽  
Neural Network Method ◽  
Network Method ◽  
Case Data

Objective: The purpose of graded care for chronic kidney disease is to share expert experience, so that doctors can more accurately diagnose chronic kidney disease, so that patients with chronic kidney disease can understand their condition in time and collect case data. The collected case data is established into a data warehouse, the data quality is evaluated, and the BP neural network method is used for data mining to analyze the data. Methods: The paper studied BP neural network and probabilistic neural network (PNN), and used 75% of the samples to compare the models. The model errors were analyzed including maximum, minimum, expectation, variance and running time to get Adaboost. The accuracy and robustness of the -PNN model and the IGABP model are good. Results: BP neural network model and probabilistic neural network method can achieve higher application of graded care for chronic kidney disease. The method is capable of quickly predicting disease grading and providing a standardized treatment care regimen. The method realizes the main functions of querying, managing, and collecting data of medical records. Conclusion: The external expansion function of BP neural network and probabilistic neural network can achieve accurate data analysis, which can effectively improve the diagnosis time and grade prediction accuracy of chronic kidney disease, and provide opinions for graded nursing.

Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network

2008 ◽  
Vol 33-37 ◽  
pp. 1283-1288 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Fuzzy Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Fuzzy Neural

Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely.

Speech Recognization Based on Support Vector Machine

2012 ◽  
Vol 433-440 ◽  
pp. 7516-7521
Author(s):  
Ling Zhang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Speech Recognition ◽  
Bp Neural Network ◽  
Speech Signal ◽  
Local Minimum ◽  
Support Vector ◽  
Neural Network Method ◽  
Network Method ◽  
Fast Classification

Aiming at the deficiency of the local minimum occurring in neural network used for speech recognition, the paper employs support vector machine (SVM) to recognize the speech signal with four different components. First, SVM is utilized to perform the speech recognition. Then, the results are compared with those obtained by the BP neural network method. The comparison shows that SVM effectively overcomes the local minimum existing in neural network and has the advantages of the accurate and fast classification, indicating that SVM looks feasible to recognize the speech signal.

scholarly journals Neural network method for control valve cost estimation on the EPC project bidding

2018 ◽  
Vol 49 ◽  
pp. 02004 ◽  
Author(s):  
Gilang Almaghribi Sarkara Putra ◽  
Rendra Agus Triyono
Keyword(s):  
Neural Network ◽  
Cost Estimation ◽  
Back Propagation ◽  
Control Valve ◽  
Back Propagation Neural Network ◽  
Neural Network Method ◽  
Price Quotation ◽  
Network Method ◽  
Alternative Choice ◽  
The Cost

Cost estimation on the bidding phase is a crucial stage that determines the success of the Engineering, Procurement and Construction (EPC) project. If the cost offered to the client is too high then it could not compete with the other bidder, but if the cost offered are too low it can reduce profit margins and result in losses for the EPC companies. This paper describe the use of Back Propagation Neural Network method to help determine cost estimation. This method is applied specifically to determine control valve cost estimation on the bidding phase so that the retrieved costs will be accurate. When there is no technical and price quotation from vendors as well as the narrowness of the bidding processing time, this method can be an alternative choice to determine the price based on previous vendor quotation. In the future, this method could be developed and applied for other instrumentation equipment such as transmitter, switch, analyzer, control system and others to achieve total cost estimation of instrumentation equipment in EPC bidding proposal.

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