Data Science and Machine Learning Applications in Subsurface Engineering
暫譯: 地下工程中的數據科學與機器學習應用
Otchere, Daniel Asante
- 出版商: CRC
- 出版日期: 2025-10-27
- 售價: $2,790
- 貴賓價: 9.5 折 $2,651
- 語言: 英文
- 頁數: 306
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032433655
- ISBN-13: 9781032433653
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相關分類:
Machine Learning、Data-mining
海外代購書籍(需單獨結帳)
商品描述
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments.
This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
商品描述(中文翻譯)
本書涵蓋無監督學習、監督學習、聚類方法、特徵工程、可解釋的人工智慧以及多輸出回歸模型在地下工程問題中的應用。處理大量且複雜的數據集是機器學習（ML）領域的主要焦點。ML旨在開發數據驅動的方法和計算算法，這些方法和算法能夠學習識別複雜且非線性的模式，以通過分析大量數據來理解和預測變數之間的關係。儘管ML模型提供了預測的最終輸出，但為了實現準確的預測，仍需執行幾個步驟。這些步驟，包括數據預處理、特徵選擇、特徵工程和異常值移除，均包含在本書中。此外，還使用現有的ML架構和學習理論開發新模型，以提高傳統ML模型的性能，並在不進行手動調整的情況下處理小數據和大數據。
這本以研究為導向的書籍將幫助地下工程師、地球物理學家和地球科學家熟悉與地下工程相關的數據科學和ML進展。此外，它展示了數據驅動方法在鹽層識別、地震解釋、估算增產油回收因子、預測孔隙流體類型、岩石物理性質預測、估算管道壓力損失、泡點壓力預測、增強 drilling mud 損失、智能井完井和合成井日誌預測中的應用。
作者簡介
Daniel Asante Otchere is an AI/ML Scientific Engineer at the Institute of Computational and Data Sciences (ICDS) at Pennsylvania State University, USA. He holds a PhD in petroleum engineering from Universiti Teknologi PETRONAS (UTP) in Malaysia, a Master's degree in Petroleum Geoscience from the University of Manchester in UK, and a Bachelor's degree in Geological Engineering from the University of Mines and Technology in Ghana. Professionally, Daniel has extensive experience across the mining and oil and gas industry, working on several onshore and offshore projects that have had a significant impact on the industry in Africa and South East Asia. He serves as a technical committee member of the World Geothermal Congress and teaches several AI topics on his YouTube channel "Study with Dani". His expertise has resulted in numerous collaborative research efforts, yielding several articles published in renowned journals and conferences. He was recognised for excellence in teaching and research in the Petroleum Engineering Department at UTP and received the 2021 best postgraduate student and the Graduate Assistant merit award in 2021 and 2022. He enjoys watching movies, listening to Highlife and Afrobeats music, hockey, and playing football. He also excels in the realm of video games, having won numerous PlayStation-FIFA tournaments held in the United Kingdom, Ghana, and Malaysia.
作者簡介(中文翻譯)
丹尼爾·阿桑特·奧切雷是美國賓夕法尼亞州立大學計算與數據科學研究所（ICDS）的人工智慧/機器學習科學工程師。他擁有馬來西亞彼得羅納斯科技大學（Universiti Teknologi PETRONAS, UTP）石油工程博士學位、英國曼徹斯特大學石油地球科學碩士學位，以及加納礦業與科技大學地質工程學士學位。在專業上，丹尼爾在礦業及石油和天然氣行業擁有豐富的經驗，參與了多個對非洲和東南亞產業產生重大影響的陸上和海上項目。他擔任世界地熱大會的技術委員會成員，並在他的YouTube頻道「與丹尼一起學習」（Study with Dani）教授多個人工智慧主題。他的專業知識促成了多項合作研究，並在知名期刊和會議上發表了多篇文章。他因在UTP石油工程系的教學和研究卓越而受到表彰，並於2021年和2022年獲得最佳研究生獎和研究助理優異獎。他喜歡看電影、聽Highlife和Afrobeats音樂、冰球和踢足球。他在電子遊戲領域也表現出色，曾在英國、加納和馬來西亞舉辦的多場PlayStation-FIFA比賽中獲勝。
