Advances in Subsurface Data Analytics

The book Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches combines the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, covering geology, geophysics, petrophysics, and reservoir engineering. It is divided into four parts and focuses on one ML algorithm with a detailed workflow for a specific application in geosciences, comparing the results with others to equip readers with different strategies for implementing automated workflows.

Format: Paperback / softback
Length: 376 pages
Publication date: 20 May 2022
Publisher: Elsevier Science Publishing Co Inc

Introduction:
The field of subsurface data analytics has witnessed significant advancements in recent years, driven by the increasing demand for efficient and accurate subsurface characterization and interpretation. Traditional and physics-based approaches to subsurface data analysis have played a crucial role in this progress, and their integration with emerging machine learning (ML) algorithms has opened up new avenues for exploring and extracting valuable insights from subsurface data.
Traditional Machine Learning Approaches:
Traditional machine learning (ML) algorithms have been widely used in subsurface data analysis for decades. These algorithms include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model to predict the target variable based on labeled data, while unsupervised learning involves identifying patterns and structures in unlabeled data. Reinforcement learning involves learning from interactions with the environment and making decisions to maximize a reward function.
Deep Learning Approaches:
Deep learning, a subset of ML, has gained significant popularity in recent years due to its ability to learn complex patterns and features from large datasets. Deep learning algorithms are particularly well-suited for subsurface data analysis, as they can handle high-dimensional data and extract meaningful insights from it. Some popular deep learning algorithms used in subsurface data analysis include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
Physics-Based Machine Learning Approaches:
Physics-based ML algorithms are designed to incorporate physical principles and equations into the learning process. These algorithms can be used to simulate complex physical processes and predict the behavior of subsurface systems. Some popular physics-based ML algorithms used in subsurface data analysis include finite element models (FEM), partial differential equations (PDEs), and Bayesian networks.
New Directions in Subsurface Data Analytics:
In addition to traditional and physics-based approaches, there are emerging new directions in subsurface data analytics. These include the use of big data, cloud computing, and artificial intelligence (AI) to enhance the efficiency and accuracy of subsurface data analysis. Big data refers to the large volumes of data generated by various sensors and instruments used in subsurface exploration and characterization. Cloud computing allows for the scalable processing and storage of large datasets, while AI can be used to automate the analysis of complex data and make informed decisions.
Conclusion:
Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging ML algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. By leveraging the power of ML, researchers and geoscientists can gain a deeper understanding of subsurface systems and make more informed decisions about exploration, development, and management.

Weight: 806g
Dimension: 192 x 235 x 24 (mm)
ISBN-13: 9780128222959