Standard

APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION. / Лю, Цзе; Фань, Цзяцзе; Давыденко, Александр Александрович.

в: ПРОЦЕССЫ УПРАВЛЕНИЯ И УСТОЙЧИВОСТЬ, Том 12, № 1, 31.03.2025, стр. 447-454.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Лю, Ц, Фань, Ц & Давыденко, АА 2025, 'APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION', ПРОЦЕССЫ УПРАВЛЕНИЯ И УСТОЙЧИВОСТЬ, Том. 12, № 1, стр. 447-454.

APA

Лю, Ц., Фань, Ц., & Давыденко, А. А. (2025). APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION. ПРОЦЕССЫ УПРАВЛЕНИЯ И УСТОЙЧИВОСТЬ, 12(1), 447-454.

Vancouver

Лю Ц, Фань Ц, Давыденко АА. APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION. ПРОЦЕССЫ УПРАВЛЕНИЯ И УСТОЙЧИВОСТЬ. 2025 Март 31;12(1):447-454.

Author

Лю, Цзе ; Фань, Цзяцзе ; Давыденко, Александр Александрович. / APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION. в: ПРОЦЕССЫ УПРАВЛЕНИЯ И УСТОЙЧИВОСТЬ. 2025 ; Том 12, № 1. стр. 447-454.

BibTeX

@article{d39a12bc2d584d5fb76366d1898598dc,
title = "APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION",
abstract = "This study introduces a multi-agent reinforcement learning approach using the MegaAgent framework to optimize decision-making in oil and gas extraction. Traditional methods struggle with optimizing parameters like well pressure and extraction rates. By employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, this research enhances efficiency through intelligent optimization of pressure and extraction controls. The framework supports efficient agent collaboration via task decomposition and parallel execution, improving overall system performance. Validation in a simulated environment shows that the proposed method surpasses traditional single-agent reinforcement learning techniques by better coordinating multiple agents, thus boosting oil extraction effectiveness and reducing equipment wear.",
keywords = "MEGAAGENT, MADDPG, RL, OIL AND GAS EXTRACTION",
author = "Цзе Лю and Цзяцзе Фань and Давыденко, {Александр Александрович}",
year = "2025",
month = mar,
day = "31",
language = "English",
volume = "12",
pages = "447--454",
journal = "Процессы управления и устойчивость",
issn = "2313-7304",
publisher = "Смирнов Николай Васильевич",
number = "1",

}

RIS

TY - JOUR

T1 - APPLYING MEGAAGENT FRAMEWORK FOR OIL AND GAS EXTRACTION OPTIMIZATION

AU - Лю, Цзе

AU - Фань, Цзяцзе

AU - Давыденко, Александр Александрович

PY - 2025/3/31

Y1 - 2025/3/31

N2 - This study introduces a multi-agent reinforcement learning approach using the MegaAgent framework to optimize decision-making in oil and gas extraction. Traditional methods struggle with optimizing parameters like well pressure and extraction rates. By employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, this research enhances efficiency through intelligent optimization of pressure and extraction controls. The framework supports efficient agent collaboration via task decomposition and parallel execution, improving overall system performance. Validation in a simulated environment shows that the proposed method surpasses traditional single-agent reinforcement learning techniques by better coordinating multiple agents, thus boosting oil extraction effectiveness and reducing equipment wear.

AB - This study introduces a multi-agent reinforcement learning approach using the MegaAgent framework to optimize decision-making in oil and gas extraction. Traditional methods struggle with optimizing parameters like well pressure and extraction rates. By employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, this research enhances efficiency through intelligent optimization of pressure and extraction controls. The framework supports efficient agent collaboration via task decomposition and parallel execution, improving overall system performance. Validation in a simulated environment shows that the proposed method surpasses traditional single-agent reinforcement learning techniques by better coordinating multiple agents, thus boosting oil extraction effectiveness and reducing equipment wear.

KW - MEGAAGENT

KW - MADDPG

KW - RL

KW - OIL AND GAS EXTRACTION

UR - https://www.elibrary.ru/item.asp?id=82486868

M3 - Article

VL - 12

SP - 447

EP - 454

JO - Процессы управления и устойчивость

JF - Процессы управления и устойчивость

SN - 2313-7304

IS - 1

ER -

ID: 154582140