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出版商:
Springer
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出版日期:
2026-05-20
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售價:
$8,140
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貴賓價:
9.5 折
$7,733
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語言:
英文
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頁數:
117
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裝訂:
Hardcover - also called cloth, retail trade, or trade
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ISBN:
3032266475
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ISBN-13:
9783032266477
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相關分類:
DeepLearning
商品描述
This book presents data driven approaches to improve drilling performance in geothermal, coiled tubing, and conventional operations. It begins with transformer models for forecasting rate of penetration in geothermal wells, followed by methods for predicting both penetration and downhole shock in coiled tubing drilling. A variational autoencoder framework is introduced for diagnosing resistivity tool anomalies to support reliable geosteering. Subsequent chapters examine the use of deep autoencoders and separation networks to improve electromagnetic telemetry signals. This book also details synthetic data driven models combined with physics-based degradation approaches to forecast the remaining useful life of drilling equipment. Hybrid strategies for generating synthetic data are discussed to extend model training in scenarios with limited failure records. Each chapter blends technical insights with real-world case studies, demonstrating how these methods reduce non-productive time, improve tool reliability, and strengthen decision making in drilling operations.
商品描述(中文翻譯)
本書介紹了數據驅動的方法，以改善地熱、捲管和傳統作業中的鑽探性能。內容首先探討了用於預測地熱井穿透率的變壓器模型，接著介紹了預測捲管鑽探中的穿透率和井下震動的方法。書中引入了一種變分自編碼器框架，用於診斷電阻率工具的異常，以支持可靠的地質導向。隨後的章節檢視了使用深度自編碼器和分離網絡來改善電磁遙測信號的應用。本書還詳細說明了合成數據驅動模型與基於物理的劣化方法相結合，以預測鑽探設備的剩餘使用壽命。討論了生成合成數據的混合策略，以擴展在故障記錄有限的情況下的模型訓練。每一章都將技術見解與實際案例研究相結合，展示這些方法如何減少非生產性時間、提高工具可靠性並加強鑽探作業中的決策能力。
作者簡介
Carlos Urdaneta received his Masters degree in Electrical Engineering from Rice University. He has worked for SLB in new product development since 2011. He is a Ph.D. candidate at the department of Electrical and Computer Engineering, University of Houston. His current research focuses on integrating AI models into drilling workflows, emphasizing predictive maintenance, dynamic forecasting, and telemetry signal improvement. Aamir Bader Shah received his BS. degree in Electrical Engineering at the NUST University and a Masters degree in Embedded System and Controls from the University of Leicester. He is currently a Ph.D. candidate at the department of Electrical and Computer Engineering at the University of Houston. His current research focuses on predicting remaining useful life in downhole drilling equipment. Xuqing Wu received the Ph.D. degree in Computer Science from the University of Houston. He is currently an Associate Professor of Computer Information Systems with the College of Technology, University of Houston. Prior to joining the University of Houston in 2015, he was a Data Scientist and Software Engineer of the Energy and IT industry. His research interests include scientific machine learning, probabilistic modeling, and subsurface sensing. Xin Fu received the Ph.D. degree in computer engineering from the University of Florida, Gainesville, in 2009. She is currently a Professor with the Electrical and Computer Engineering Department, University of Houston, Houston, TX, USA. Her research interests include computer architecture, high-performance computing, hardware reliability and variability, energy-efficient computing, and mobile computing. Jiefu Chen is an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston. He received the Ph.D. degree in Electrical Engineering from Duke University. From 2011 to 2015, he was a Staff Scientist with Weatherford. He has published over 100 papers in computational electromagnetics, inverse problems, machine learning, oilfield data analytics, seismic data processing, subsurface wireless communication, and well logging.
作者簡介(中文翻譯)
Carlos Urdaneta 於萊斯大學獲得電機工程碩士學位。自2011年以來，他在SLB從事新產品開發工作。他是休士頓大學電機與計算機工程系的博士候選人。他目前的研究重點是將人工智慧模型整合到鑽井工作流程中，強調預測性維護、動態預測和遙測信號改善。 Aamir Bader Shah 於NUST大學獲得電機工程學士學位，並在萊斯特大學獲得嵌入式系統與控制碩士學位。他目前是休士頓大學電機與計算機工程系的博士候選人。他目前的研究重點是預測井下鑽井設備的剩餘使用壽命。 Xuqing Wu 於休士頓大學獲得計算機科學博士學位。他目前是休士頓大學科技學院計算機資訊系的副教授。在2015年加入休士頓大學之前，他曾擔任能源和IT行業的數據科學家和軟體工程師。他的研究興趣包括科學機器學習、概率建模和地下感測。 Xin Fu 於2009年在佛羅里達大學（Gainesville）獲得計算機工程博士學位。她目前是休士頓大學電機與計算機工程系的教授。她的研究興趣包括計算機架構、高效能計算、硬體可靠性與變異性、節能計算和行動計算。 Jiefu Chen 是休士頓大學電機與計算機工程系的副教授。他於杜克大學獲得電機工程博士學位。從2011年到2015年，他曾在Weatherford擔任研究科學家。他在計算電磁學、逆問題、機器學習、油田數據分析、地震數據處理、地下無線通信和井下測井等領域發表了超過100篇論文。
