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Author: A. Sinvhal
Publisher: Springer Science & Business Media
ISBN: 9401125708
Category : Computers
Languages : en
Pages : 199
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Book Description
The reasons for writing this book are very simple. We use and teach com puter aided techniques of mathematical simulation and of pattern recogni tion. Life would be much simpler if we had a suitable text book with methods and computer programmes which we could keep referring to. Therefore, we have presented here material that is essential for mathematical modelling of some complex geological situations, with which earth scientists are often confronted. The reader is introduced not only to the essentials of computer modelling, data analysis and pattern recognition, but is also made familiar with the basic understanding with which they can plunge into when solving related and more complex problems. This book first makes a case for seismic stratigraphy and then for pattern recognition. Chapter 1 provides an extensive review of applications of pattern recognition methods in oil exploration. Simulation procedures are presented with examples that are fairly simple to understand and easy to use on the computer. Several geological situations can be formulated and simulated using the Monte Carlo method. The binary lithologic sequences, discussed in Chapter 2, consist of alternating layers of any two of sand, shale and coal.
Author: A. Sinvhal
Publisher: Springer Science & Business Media
ISBN: 9401125708
Category : Computers
Languages : en
Pages : 199
Get Book Here
Book Description
The reasons for writing this book are very simple. We use and teach com puter aided techniques of mathematical simulation and of pattern recogni tion. Life would be much simpler if we had a suitable text book with methods and computer programmes which we could keep referring to. Therefore, we have presented here material that is essential for mathematical modelling of some complex geological situations, with which earth scientists are often confronted. The reader is introduced not only to the essentials of computer modelling, data analysis and pattern recognition, but is also made familiar with the basic understanding with which they can plunge into when solving related and more complex problems. This book first makes a case for seismic stratigraphy and then for pattern recognition. Chapter 1 provides an extensive review of applications of pattern recognition methods in oil exploration. Simulation procedures are presented with examples that are fairly simple to understand and easy to use on the computer. Several geological situations can be formulated and simulated using the Monte Carlo method. The binary lithologic sequences, discussed in Chapter 2, consist of alternating layers of any two of sand, shale and coal.
Author: Kou-Yuan Huang
Publisher: World Scientific
ISBN: 9789812775740
Category : Technology & Engineering
Languages : en
Pages : 152
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Book Description
The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the parsing using the match primitive measure, (4) the Levenshtein distance computation, (5) the likelihood ratio test, (6) the error-correcting tree automata, and (7) a hierarchical system. Syntactic seismic pattern recognition can be one of the milestones of a geophysical intelligent interpretation system. The syntactic methods in this book can be applied to other areas, such as the medical diagnosis system. The book will benefit geophysicists, computer scientists and electrical engineers. Sample Chapter(s). Chapter 1: Introduction to Syntactic Pattern Recognition (114 KB). Contents: Introduction to Syntactic Pattern Recognition; Introduction to Formal Languages and Automata; Error-Correcting Finite-State Automaton for Recognition of Ricker Wavelets; Attributed Grammar and Error-Correcting Earley's Parsing; Attributed Grammar and Match Primitive Measure (MPM) for Recognition of Seismic Wavelets; String Distance and Likelihood Ratio Test for Detection of Candidate Bright Spot; Tree Grammar and Automaton for Seismic Pattern Recognition; A Hierarchical Recognition System of Seismic Patterns and Future Study. Readership: Geophysicists, computer scientists and electrical engineers.
Author: M. Nikravesh
Publisher: Elsevier
ISBN: 0080541321
Category : Science
Languages : en
Pages : 755
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Book Description
This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects. It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis. There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. It will also be of interest to academics to assess the importance of, and contribute to, R&D efforts in relevant areas.
Author: Ibrahim Palaz
Publisher: Springer Science & Business Media
ISBN: 1461243882
Category : Science
Languages : en
Pages : 315
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Book Description
Here is a state-of-the-art survey of artificial intelligence in modern exploration programs. Focussing on standard exploration procedures, the contributions examine the advantages and pitfalls of using these new techniques, and, in the process, provide new, more accurate and consistent methods for solving old problems. They show how expert systems can provide the integration of information that is essential in the petroleum industry when solving the complicated questions facing the modern petroleum geoscientist.
Author: Keith R. Holdaway
Publisher: John Wiley & Sons
ISBN: 1119302595
Category : Business & Economics
Languages : en
Pages : 369
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Book Description
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.
Author: Zhicheng Geng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Pattern recognition plays an important role in analyzing seismic reflection data, which contains valuable information of the subsurface geological structures, and serves as a powerful method for hydrocarbon exploration. Conventional seismic pattern recognition methods commonly involve handcrafted seismic attributes or filters that do not apply to seismic data with complex structures. On the other hand, with new seismic acquisition techniques and equipment providing an increasing amount of data, conventional methods tend to be inefficient in processing large-scale and high-dimensional datasets. Over the past decade, the improvement of computer powers and software development has promoted deep learning as an efficient and effective tool for pattern recognition, which extracts features directly from data without relying on assumptions. This dissertation presents deep learning methods for pattern recognition in seismic reflection data from various perspectives. First, I introduce a semi-supervised learning framework for salt segmentation to alleviate the burden of preparing a large amount of labeled training data. The unsupervised consistency loss enforces the convolutional neural network (CNN) to extract essential information from labeled and unlabeled data, leading to more accurate segmentation results and better generalization ability on different datasets than the supervised learning baseline. Second, I formulate relative geologic time (RGT) estimation as a regression problem and design a U-shape CNN to solve this problem. The encoder-decoder architecture with skip connections results in accurate RGT predictions directly from seismic images. Although trained on a synthetic dataset, the network generalizes well to complex field data. Third, I propose an unsupervised learning method for seismic random noise attenuation. In the proposed method, a convolutional autoencoder is trained to reconstruct clean images from noisy seismic images without supervision from labeled data. The network training is constrained by local orthogonalization loss for better signal and noise separation. Next, I apply CNNs to reconstruct subsurface velocity models from common-image gathers (CIGs), which involves depth-to-depth mapping. The focuses or the flatness of seismic events in CIGs contain valuable information about the surface velocity model. Trained with synthetic dataset migrated using wrong velocity models, the network learns the relationship between the incorrect positioning of seismic energy in CIGs and the corresponding correct velocity update. In the next chapter, I explore the possibility of employing a different network architecture, Transformers, for velocity model building. In the proposed method, velocity models are directly estimated from raw recorded seismic reflection data using a variant of Vision Transformers specially tailored for FWI (FWIT), consisting of an encoder and a decoder. The encoder of FWIT learns to extract high-level information from input shot gathers, which is further analyzed by the decoder to estimate the velocities based on the attention mechanism. The ability to learn long-dependency and the flexibility of predicting variable-length output make Transformers a more suitable architecture for FWI than CNNs. Finally, I discuss and suggest possible future research topics
Author: Kurt J. Marfurt
Publisher: SEG Books
ISBN: 1560803517
Category : Business & Economics
Languages : en
Pages : 509
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Book Description
Useful attributes capture and quantify key components of the seismic amplitude and texture for subsequent integration with well log, microseismic, and production data through either interactive visualization or machine learning. Although both approaches can accelerate and facilitate the interpretation process, they can by no means replace the interpreter. Interpreter “grayware” includes the incorporation and validation of depositional, diagenetic, and tectonic deformation models, the integration of rock physics systematics, and the recognition of unanticipated opportunities and hazards. This book is written to accompany and complement the 2018 SEG Distinguished Instructor Short Course that provides a rapid overview of how 3D seismic attributes provide a framework for data integration over the life of the oil and gas field. Key concepts are illustrated by example, showing modern workflows based on interactive interpretation and display as well as those aided by machine learning.
Author: William Edward Fitzgibbon
Publisher: SIAM
ISBN: 9780898712056
Category : Mathematics
Languages : en
Pages : 306
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Book Description
Author: Fred Aminzadeh
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 260
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Book Description
Author: Yasir Bashir
Publisher: Elsevier
ISBN: 0323918875
Category : Business & Economics
Languages : en
Pages : 310
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Book Description
Seismic Imaging Methods and Application for Oil and Gas Exploration connects the legacy of field data processing and imaging with new research methods using diffractions and anisotropy in the field of geophysics. Topics covered include seismic data acquisition, seismic data processing, seismic wave modeling, high-resolution imaging, and anisotropic modeling and imaging. This book is a necessary resource for geophysicist working in the oil and gas and mineral exploration industries, as well as for students and academics in exploration geophysics. Provides detailed methods that are used in the industry, including advice on which methods to use in specific situations Compares classical methods with the latest technologies to improve practice and application in the real world Includes case studies for further explanation of methods described in the book
