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Wyszukujesz frazę "petroleum industry" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art
Autorzy:
Bello, O.
Holzmann, J.
Yaqoob, T.
Teodoriu, C.
Powiązania:
https://bibliotekanauki.pl/articles/91537.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
artificial intelligence
AI
petroleum exploration
production
neural network
oil industry
gas industry
sztuczna inteligencja
ropa naftowa
produkcja
sieć neuronowa
przemysł naftowy
gazownictwo
Opis:
Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses. Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more. This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 2; 121-139
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Advanced supervision of oil wells based on soft computing techniques
Autorzy:
Camargo, E.
Aguilar, J.
Powiązania:
https://bibliotekanauki.pl/articles/91828.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intelligent model of supervision
evolutionary computation
fuzzy system
oil industry
operational diagnosis
petroleum wells
gas lift method
multilayer fuzzy system
genetic algorithm
Opis:
In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production. Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 215-225
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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