Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation
暫譯: 智慧代理建模：人工智慧與機器學習在數值模擬中的應用

Mohaghegh, Shahab D.

Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation
  • 出版商: CRC
  • 出版日期: 2022-10-27
  • 售價: $4,210
  • 貴賓價: 9.5 折 $4,000
  • 語言: 英文
  • 頁數: 190
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032151145
  • ISBN-13: 9781032151144
  • 相關分類: 人工智慧、Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases.

  • Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine Learning

  • Details application in reservoir simulation and modeling and computational fluid dynamics

  • Includes real case studies based on commercially available simulators

Smart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics.

商品描述(中文翻譯)

數值模擬模型在所有工程學科中被用來模擬物理現象，以了解這些現象的運作方式，並識別問題及優化行為。智慧代理模型（Smart Proxy Models）提供了一個以非常高的準確度複製數值模擬的機會，並且可以在幾分鐘內在筆記型電腦上運行，從而簡化了複雜數值模擬的使用，否則這些模擬可能需要數十小時。本書專注於智慧代理建模，並提供讀者有關如何使用人工智慧（Artificial Intelligence）和機器學習（Machine Learning）開發智慧代理模型的所有必要細節，以及如何在實際案例中使用它。

- 涵蓋使用人工智慧和機器學習複製高精度數值模擬
- 詳細說明在水庫模擬、建模及計算流體力學中的應用
- 包含基於商業可用模擬器的實際案例研究

智慧代理建模非常適合石油、化學、環境和機械工程師，以及統計學家和其他從事數據驅動分析應用的人士。

作者簡介

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University (WVU) and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).

Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics - Data Driven Reservoir Modeling - Application of Data-Driven Analytics for the Geological Storage of CO2), more than 230 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE's Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE's Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011). He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

作者簡介(中文翻譯)

Shahab D. Mohaghegh 是人工智慧 (Artificial Intelligence) 和機器學習 (Machine Learning) 在勘探與生產產業應用的先驅，現任西維吉尼亞大學 (West Virginia University, WVU) 石油與天然氣工程教授，以及 Intelligent Solutions, Inc. (ISI) 的總裁兼執行長。他是 WVU-LEADS (數據科學工程應用實驗室) 的主任。

擁有超過 30 年在石油工程領域應用人工智慧和機器學習的研究與開發經驗，他著有三本書籍（《頁岩分析 (Shale Analytics)》、 《數據驅動的油藏建模 (Data Driven Reservoir Modeling)》、 《二氧化碳地質儲存的數據驅動分析應用 (Application of Data-Driven Analytics for the Geological Storage of CO2)》），發表超過 230 篇技術論文，並為獨立公司、國有公司 (NOCs) 和國際石油公司 (IOCs) 執行超過 60 個專案。他是 SPE 傑出講者 (Distinguished Lecturer)（2007 年和 2020 年），並四次被選為 SPE 石油技術期刊 (Journal of Petroleum Technology, JPT) 的傑出作者（2000 年和 2005 年）。他是 SPE 專注於人工智慧和機器學習的技術部門（Petroleum Data-Driven Analytics, 2011）的創始人。他因在墨西哥灣深水地平線 (Deepwater Horizon, Macondo) 事件後的人工智慧技術貢獻而受到美國能源部長的表彰（2011 年），並在兩屆政府中擔任美國能源部長的非常規資源技術諮詢委員會成員（2008-2014 年）。他曾代表美國參加國際標準組織 (ISO) 的碳捕集與儲存技術委員會（2014-2016 年）。

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