Research of actual information on well casing using machine learning and neural networks

2021 ◽  
pp. 108-119
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattahov ◽  
A. D. Shalyapina ◽  
V. G. Kuznetsov
Keyword(s):  
Machine Learning ◽  
Probabilistic Models ◽  
Western Siberia ◽  
Network Modeling ◽  
Horizontal Wells ◽  
Categorical Variables ◽  
Neural Network Modeling ◽  
New Approach ◽  
Attachment Process

In domestic and world practice, despite the measures applied and developed to improve the quality of well casing, there is a problem of leaky structures in almost 50 % of completed wells. The study of actual data using classical methods of statistical analysis (regression and variance analyses) doesn't allow us to model the process with sufficient accuracy that requires the development of a new approach to the study of the attachment process. It is proposed to use the methods of machine learning and neural network modeling to identify the most important parameters and their synergistic impact on the target variables that affect the quality of well casing. The formulas necessary for translating the numerical values of the results of acoustic and gamma-gamma cementometry into categorical variables to improve the quality of probabilistic models are determined. A database consisting of 93 parameters for 934 wells of fields located in Western Siberia has been formed. The analysis of fastening of production columns of horizontal wells of four stratigraphic arches is carried out, the most weighty variables and regularities of their influence on target indicators are established. Recommendations are formulated to improve the quality of well casing by correcting the effects of acoustic and gamma-gamma logging on the results.

scholarly journals Enhancement of energy consumption estimation for electric vehicles by using machine learning

2021 ◽  
Vol 10 (1) ◽  
pp. 215
Author(s):  
Adnane Cabani ◽  
Peiwen Zhang ◽  
Redouane Khemmar ◽  
Jin Xu
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Optimal Path ◽  
Influence Factors ◽  
Air Conditioner ◽  
Driver Behaviour ◽  
Variable Parameters ◽  
New Approach

<p>Three main classes are considered of significant influence factors when predicting the energy consumption rate of electric vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to improve the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EV energy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air conditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimated of energy-consumption. The proposed method is used in order to find the optimal path between two points (departure-destination) in terms of energy consumption.</p>

PREDICTING SOFTWARE QUALITY, DURING TESTING, USING NEURAL NETWORK MODELS: A COMPARATIVE STUDY

1994 ◽  
Vol 01 (03) ◽  
pp. 303-319 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
ROBERT M. SZABO
Keyword(s):  
Neural Network ◽  
Software Quality ◽  
Principal Components ◽  
Network Modeling ◽  
Network Models ◽  
Neural Nets ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Predictive Quality

The application of statistical modeling techniques has been an intensely pursued area of research in the field of software engineering. The goal has been to model software quality and use that information to better understand the software development process. Neural network modeling methods have recently been applied to this field. The results reported indicate that neural network models have better predictive quality than some statistical models when predicting reliability and the number of faults. In this paper, we will investigate the application of principal components analysis to neural network modeling as a way of improving the predictive quality of neural network quality models. Using data we collected from a large commercial software system, we developed a multiple regression model using the principal components. Then, we trained two neural nets, one with raw data, and one with principal components. Then, we compare the predictive quality of the three competing models for a variety of quality measures.

Statistical modelling and inference

2019 ◽  
pp. 93-132
Author(s):  
Max A. Little
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Joint Distribution ◽  
Probabilistic Models ◽  
Optimal Parameter ◽  
Statistical Modelling ◽  
Statistical Machine Learning ◽  
Central Task ◽  
Parameter Values

The modern view of statistical machine learning and signal processing is that the central task is one of finding good probabilistic models for the joint distribution over all the variables in the problem. We can then make `queries' of this model, also known as inferences, to determine optimal parameter values or signals. Hence, the importance of statistical methods to this book cannot be overstated. This chapter is an in-depth exploration of what this probabilistic modeling entails, the origins of the concepts involved, how to perform inferences and how to test the quality of a model produced this way.

RESEARCH OF THE FACTORS DETERMINING THE PATTERNS OF ENTREPRENEURSHIP DEVELOPMENT

2020 ◽  
Vol 1 (9) ◽  
pp. 105-113
Author(s):  
E. V. BONDARENKO ◽  
Keyword(s):  
Neural Network ◽  
Gross Domestic Product ◽  
Network Modeling ◽  
Entrepreneurial Activity ◽  
Neural Network Modeling ◽  
Entrepreneurship Development ◽  
Fixed Assets ◽  
Main Factors ◽  
Investment In Fixed Assets

The paper investigates the patterns of entrepreneurship development by analyzing such indicators as: gross domestic product and turnover of small and medium-sized businesses. The following factors were selected and analyzed as the main factors that determine the tendencies of changes in entrepreneurial activity: investments in fixed assets at actual prices, as well as the purchase of goods and payment for services – consumption. The functions of indicators of consumption, investment in fixed assets, GDP and SME turnover have been formulated. A neural network modeling of the above functions and patterns of entrepreneurship development has been performed. The quality of the constructed models and the influence of factors determining the patterns of changes in entrepreneurial activity are assessed.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

Sign in / Sign up

Export Citation Format

Share Document