Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.
In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reservoir models with a classification accuracy higher than 79%. In addition, the statistical analysis approves that the optimized regressors accurately perform the reservoir characterization with mean relative errors of lower than 4.5%. The minimized manual interference reduces human bias, and the models have significant noise tolerance for practical applications.
An increase of components production for the equipment intended for oil and gas production is a key factor for analyzing existing technological processes and searching for new technological solutions to improve the efficiency of the production process and the quality of components. The article presents a simulation model designed to determine the rational technological processing parameters for the production of the “Centralizer shell” part. The basis for optimizing the working cycle of a production line is synchronization based on the principle of proportionality, which involves equalizing the duration of all technological operations with the rhythm of the production line. Synchronization of technological operations on the production line is carried out by choosing rational cutting parameters for each technological transition (cutting speed, feedrate, number of working passes). The “Centralizer shell” part is made of titanium alloy VT16, which has high strength, corrosion resistance and ductility. For the part under consideration, the permissible values of the cutting parameters were determined based on the calculation of the total processing error, as well as the frequency of replacement of the worn cutting tool. The simulation model described in the article made it possible to increase the efficiency of the production process due to the synchronization of technological operations and the search for rational technological parameters, as well as to improve the manufacturing quality of the “Centralizer shell” part by analyzing the processing error at various parameters of the technological process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.