Machine Learning for Subsurface Characterization

To continue to meet demand while keeping costs down, petroleum and reservoir engineers know it is critical to utilize their asset's data through more complex modeling methods, and machine learning and data analytics is the known alternative approach to accurately represent the complexity of fluid-filled rocks. With a lack of training resources available, Machine Learning for Subsurface Characterization focuses on the development and application of neural networks, deep learning, unsupervised learning, reinforcement learning, and clustering methods for subsurface characterization under constraints. Such constraints are encountered during subsurface engineering operations due to financial, operational, regulatory, risk, technological, and environmental challenges. This reference teaches how to do more with less. Used to develop tools and techniques of data-driven predictive modelling and machine learning for subsurface engineering and science, engineers will be introduced to methods of generating subsurface signals and analyzing the complex relationships within various subsurface signals using machine learning. Algorithmic procedures in MATLAB, R, PYTHON, and TENSORFLOW are displayed in text and through online instructional video to assist training and learning. Field cases are also presented to understand real-world applications, with a particular focus on examples involving shale reservoirs. Explaining the concept of machine learning, advantages to the industry, and applications applied to complex subsurface rocks, Machine Learning for Subsurface Characterization delivers a missing piece to the reservoir engineer's toolbox needed to support today's complex operations. Focus on applying predictive modelling and machine learning from real case studies and Q&A sessions at the end of each chapter Learn how to develop codes such as MATLAB, PYTHON, R, and TENSORFLOW with step-by-step guides includedVisually learn code development with video demonstrations included INDICE: 1. Comparative study of shallow learning methods and their applications 2. Comparative study of shallow vs. deep learning methods and their applications 3. Use of artificial neural networks and k-means clustering for near-wellbore characterization of Bakken shale 4. Use of artificial neural networks for generating Dielectric Dispersion Logs in Permian Basin 5. Use of artificial neural networks for generating various geomechanical moduli of shale formations 6. Use of artificial neural networks for quantifying electrical anisotropy in shale formations 7. Use of unsupervised learning methods in shale rock typing 8. Generation of in-situ NMR T2 distribution using deep neural networks 9. Reinforcement-learning for large-scale reservoir characterization

• ISBN: 978-0-12-817736-5
• Editorial: Gulf Professional Publishing
• Encuadernacion: Rústica
• Páginas: 230
• Fecha Publicación: 01/06/2019
• Nº Volúmenes: 1
• Idioma: Inglés