Data Analytics for Drilling Engineering
Data Analytics for Drilling Engineering

Author: Qilong Xue

Publisher: Springer Nature

Published: 2019-12-30

Total Pages: 312

ISBN-13: 303034035X

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This book presents the signal processing and data mining challenges encountered in drilling engineering, and describes the methods used to overcome them. In drilling engineering, many signal processing technologies are required to solve practical problems, such as downhole information transmission, spatial attitude of drillstring, drillstring dynamics, seismic activity while drilling, among others. This title attempts to bridge the gap between the signal processing and data mining and oil and gas drilling engineering communities. There is an urgent need to summarize signal processing and data mining issues in drilling engineering so that practitioners in these fields can understand each other in order to enhance oil and gas drilling functions. In summary, this book shows the importance of signal processing and data mining to researchers and professional drilling engineers and open up a new area of application for signal processing and data mining scientists.

Data Analytics in Reservoir Engineering
Data Analytics in Reservoir Engineering

Author: Sathish Sankaran

Publisher:

Published: 2020-10-29

Total Pages: 108

ISBN-13: 9781613998205

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Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Shale Analytics
Shale Analytics

Author: Shahab D. Mohaghegh

Publisher: Springer

Published: 2017-02-09

Total Pages: 292

ISBN-13: 3319487531

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This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.

Machine Learning and Data Science in the Oil and Gas Industry
Machine Learning and Data Science in the Oil and Gas Industry

Author: Patrick Bangert

Publisher: Gulf Professional Publishing

Published: 2021-03-04

Total Pages: 290

ISBN-13: 0128209143

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Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

Author: Kingshuk Srivastava

Publisher: CRC Press

Published: 2023-11-20

Total Pages: 187

ISBN-13: 1000995119

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This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Advanced Data Analytics for Optimized Drilling Operations Using Surface and Downhole Data
Advanced Data Analytics for Optimized Drilling Operations Using Surface and Downhole Data

Author: Rahmat Ashari

Publisher:

Published: 2022

Total Pages: 0

ISBN-13:

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Minimizing well construction cost continues to be a dominant performance motivator for operators who rely on subsurface energy. Drilling optimization is a primary approach that drilling engineers use to lower costs, and this can be achieved by focusing on two goals: maximizing rate of penetration (ROP) and minimizing non-productive time (NPT). With the advances in sensors and computational technologies, data-driven approaches have become increasingly popular to achieve these goals. Nonetheless, experiences reveal that many operators underutilize the values that can be derived from their data. This research study develops data analytics approaches that incorporate surface and downhole data from drilling operations. In particular, two topics were studied in detail: connection recipes and bit damage tracking. The first topic aimed to develop an optimum approach to execute drillstring connections –i.e., the “connection recipes”– that yields a minimum level of vibrations so as to prevent downhole tool failures. The second topic specifically concerns drill bits, where the goal is to develop a workflow to track the state of bit damage in real time. An actionable outcome from such a workflow is the construction of a pull bit criterion, which serves as a guideline for drillers to decide whether a trip out is necessary. The data analytics workflows presented in this study will equip engineers with the capabilities to not only standardize connection-making practices to prevent downhole tools failures, but also optimize drilling performance by real-time bit damage monitoring that further helps lower NPT. For the connection recipes project, the data studied presented several unfavorable practices relating to surface rotational speed and weight on bit promoting the occurrences of stick-slip events, whirling, and shocks when going back to bottom and going off-bottom. Based on these observations, safer connection practices were identified. For the bit damage tracking project, bit wear and tooth wear metrics were computed. When applied on historical real-time drilling data, they revealed trip outs that were conducted either too early or too late. Based on several case studies, a bit pull criterion was subsequently developed. The two projects leveraged surface and downhole drilling data to produce insights and workflows that are deployable in real-time for drilling optimization

Methods for Petroleum Well Optimization
Methods for Petroleum Well Optimization

Author: Rasool Khosravanian

Publisher: Gulf Professional Publishing

Published: 2021-09-22

Total Pages: 554

ISBN-13: 0323902324

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Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methodsfor Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutionsspecific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches frommathematical to real-time data solutions through technological advances. Topics include digital well planning and construction,moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms,complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-basedreasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R,Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guidefor researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, suchas digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysissoftware more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization

Applied Drilling Engineering
Applied Drilling Engineering

Author: Adam T. Bourgoyne

Publisher:

Published: 1986

Total Pages: 522

ISBN-13:

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Applied Drilling Engineering presents engineering science fundamentals as well as examples of engineering applications involving those fundamentals.

Drilling Data Vortex
Drilling Data Vortex

Author: Carlos Damski

Publisher: Genesis Publishing and Services Pty Limited

Published: 2014-11-10

Total Pages: 138

ISBN-13: 9780994164209

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In today's world, traditional methods of drilling oil wells don't work. Yesterday's practices are being superseded by a universal trend towards the extensive use of historical and real-time data to understand, learn and predict all well intervention operations. This book explores the impact of data analytics on well operations. Drawn from the author's extensive experience in data analysis, it examines, in easily understandable terms, today's data management processes. The book explores issues related to: Basic concepts of data management for drilling; Methods of using data as a basis for improving and optimizing process control; Achieving a common understanding of the issues involved among information technology personnel and field engineers; A roadmap for the implementation of a drilling process improvement system; Business Intelligence as the ultimate goal of any data management process; Discussions about data acquisition, quality control, storage, retrieval and analyses; Map intelligence; Understanding operational time and trouble analyses; learning curve, technical limit and benchmarking; Real business cases to illustrate the concepts explored in the book. The book is designed for a broad audience, including drilling personnel, managers, data analysts, and all professionals involved in the use of data to improve drilling operations.

Drilling Engineering Problems and Solutions
Drilling Engineering Problems and Solutions

Author: M. E. Hossain

Publisher: John Wiley & Sons

Published: 2018-06-19

Total Pages: 551

ISBN-13: 1118998642

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Completely up to date and the most thorough and comprehensive reference work and learning tool available for drilling engineering, this groundbreaking volume is a must-have for anyone who works in drilling in the oil and gas sector. Petroleum and natural gas still remain the single biggest resource for energy on earth. Even as alternative and renewable sources are developed, petroleum and natural gas continue to be, by far, the most used and, if engineered properly, the most cost-effective and efficient, source of energy on the planet. Drilling engineering is one of the most important links in the energy chain, being, after all, the science of getting the resources out of the ground for processing. Without drilling engineering, there would be no gasoline, jet fuel, and the myriad of other "have to have" products that people use all over the world every day. Following up on their previous books, also available from Wiley-Scrivener, the authors, two of the most well-respected, prolific, and progressive drilling engineers in the industry, offer this groundbreaking volume. They cover the basic tenets of drilling engineering, the most common problems that the drilling engineer faces day to day, and cutting-edge new technology and processes through their unique lens. Written to reflect the new, changing world that we live in, this fascinating new volume offers a treasure of knowledge for the veteran engineer, new hire, or student. This book is an excellent resource for petroleum engineering students, reservoir engineers, supervisors & managers, researchers and environmental engineers for planning every aspect of rig operations in the most sustainable, environmentally responsible manner, using the most up-to-date technological advancements in equipment and processes.