Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation

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.

數值模擬模型在所有工程學科中被用於模擬物理現象，以了解這些現象的運作方式，並識別問題並優化行為。智能代理模型提供了一個機會，可以以非常高的準確性複製數值模擬，並且可以在幾分鐘內在筆記型電腦上運行，從而簡化了使用複雜的數值模擬，否則可能需要數十小時。本書專注於智能代理建模，並為讀者提供了開發智能代理模型的所有基本細節，包括使用人工智能和機器學習的方法，以及在實際案例中的應用。

本書的重點包括：
- 使用人工智能和機器學習複製高度準確的數值模擬
- 詳細介紹在油藏模擬、建模和計算流體力學中的應用
- 包含基於商業可用模擬器的真實案例研究

智能代理建模對於石油、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員非常適用。

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).

Shahab D. Mohaghegh是在勘探和生產行業應用人工智能和機器學習方面的先驅者，他是西維吉尼亞大學（WVU）石油和天然氣工程學教授，也是Intelligent Solutions, Inc.（ISI）的總裁兼首席執行官。他還是WVU-LEADS（數據科學工程應用實驗室）的主任。

他在石油工程應用人工智能和機器學習方面進行了30多年的研究和開發，撰寫了三本書（Shale Analytics - Data Driven Reservoir Modeling - Application of Data-Driven Analytics for the Geological Storage of CO2），發表了230多篇技術論文，並為獨立公司、國有公司和國際石油公司執行了60多個項目。他是SPE（石油工程師學會）的傑出講師（2007年和2020年），並四次被選為SPE《石油技術雜誌》（JPT 2000年和2005年）的傑出作者。他是SPE專門致力於人工智能和機器學習的技術部門（Petroleum Data-Driven Analytics, 2011）的創始人。他因其在墨西哥灣Deepwater Horizon（Macondo）事故（2011年）後基於人工智能的技術貢獻而獲得美國能源部的表彰，並在兩屆政府（2008-2014年）擔任美國能源部非常規資源技術顧問委員會的成員。他代表美國參與國際標準組織（ISO）的碳捕獲和儲存技術委員會（2014-2016年）。