Author : Fred Aminzadeh
Publisher :
Page : 168 pages
File Size : 35,59 MB
Release : 2006
Category : Neural networks (Computer science)
ISBN :
Author : M. Nikravesh
Publisher : Elsevier
Page : 755 pages
File Size : 33,31 MB
Release : 2003-04-22
Category : Science
ISBN : 0080541321
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 : Fred Aminzadeh
Publisher : Elsevier Inc. Chapters
Page : 29 pages
File Size : 28,79 MB
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 012807681X
In most oil exploration and production problems, we deal with limited and incomplete data. We are constantly trying to extrapolate information from sparse measurements, for example, sparse well data and limited core measurements on the one hand and large volumes of seismic data with limited spatial resolution on the other hand. We resort to statistical methods to accomplish the data extrapolation and the integration of diverse data sets in constructing a coherent and meaningful model of the subsurface. Traditional statistical methods both for spatial and temporal extrapolation have been used in E&P for several decades. One of the main uses of statistics has been for reservoir characterization through integrating information and data from various sources with varying degrees of uncertainty such as log, well tests, and seismic data. Other applications include establishing relationships between measurements and reservoir properties, and reserve estimation and oil field economics along with the associated risk factors.
Author : Kalachand Sain
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 13,77 MB
Release : 2022-06-24
Category : Science
ISBN : 1119481767
Applying machine learning to the interpretation of seismic data Seismic data gathered on the surface can be used to generate numerous seismic attributes that enable better understanding of subsurface geological structures and stratigraphic features. With an ever-increasing volume of seismic data available, machine learning augments faster data processing and interpretation of complex subsurface geology. Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data. Volume highlights include: Historic evolution of seismic attributes Overview of meta-attributes and how to design them Workflows for the computation of meta-attributes from seismic data Case studies demonstrating the application of meta-attributes Sets of exercises with solutions provided Sample data sets available for hands-on exercises The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Author : Pradeep Sharma
Publisher : Educreation Publishing
Page : 256 pages
File Size : 38,76 MB
Release : 2019-01-30
Category : Education
ISBN :
This book is prepared for the engineering students pursuing degree in computer science and information technology branch. The main consideration in writing the book is to present the considerable requirements of the syllabus in a simple manner as possible. This book contains many solved examples which will help student to gain confidence in problem solving. Valuable suggestion is heartily welcome for further improvement of this book
Author : Andreas Stark
Publisher : Universal-Publishers
Page : 408 pages
File Size : 39,91 MB
Release : 2010-07
Category : Business & Economics
ISBN : 1599423499
This dissertation will discuss the uncertainty encountered in the daily operations of businesses. The concepts will be developed by first giving an overview of probability and statistics as used in our everyday activities, such as the basic principles of probability, univariate and multivariate statistics, data clustering and mapping, as well as time sequence and spectral analysis. The examples used will be from the oil and gas exploration industry because the risks taken in this industry are normally quite large and are ideal for showing the application of the various techniques for minimizing risk. Subsequently, the discussion will deal with basic risk analysis, spatial and time variations of risk, geotechnical risk analysis, risk aversion and how it is affected by personal biases, and how to use portfolios to hedge risk together with the application of real options. Next, fractal analysis and its application to economics and risk analysis will be examined, followed by some examples showing the change in the Value at Risk under Fractal Brownian Motions. Finally, a neural network application is shown whereby some of these risks and risk factors will be combined to forecast the best possible outcome given a certain knowledge base. The chapters will discuss: Basic probability techniques and uncertainty principles Analysis and diversification for exploration projects The value and risk of information in the decision process Simulation techniques and modeling of uncertainty Project valuation and project risk return Modeling risk propensity or preference analysis of exploration projects Application of fractals to risk analysis Simultaneous prediction of strategic risk and decision attributes using multivariate statistics and neural networks"
Author : Rafik Aziz Aliev
Publisher : World Scientific Publishing Company
Page : 463 pages
File Size : 15,7 MB
Release : 2001-09-06
Category : Computers
ISBN : 9813105879
The concept of soft computing is still in its initial stages of crystallization. Presently available books on soft computing are merely collections of chapters or articles about different aspects of the field. This book is the first to provide a systematic account of the major concepts and methodologies of soft computing, presenting a unified framework that makes the subject more accessible to students and practitioners. Particularly worthy of note is the inclusion of a wealth of information about neuro-fuzzy, neuro-genetic, fuzzy-genetic and neuro-fuzzy-genetic systems, with many illuminating applications and examples.
Author : W. Sandham
Publisher : Springer Science & Business Media
Page : 336 pages
File Size : 48,90 MB
Release : 2013-06-29
Category : Mathematics
ISBN : 9401702713
The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.
Author : Paolo Dell'Aversana
Publisher : Academic Press
Page : 250 pages
File Size : 35,11 MB
Release : 2017-07-14
Category : Medical
ISBN : 0128104813
Neurobiological Background of Exploration Geosciences: New Methods for Data Analysis Based on Cognitive Criteria examines the neurobiological background of earth science disciplines. It presents the fundamental features of the human brain that form the cognitive basis of exploration geophysics and investigates how their analysis can drive the development of new brain-based technologies. Crucial aspects of human cognition include the impulse to explore the environment, the ability of our brain to create mental maps and virtual images of the world, and the human ability to recognize, integrate and save patterns of information in a shared memory. Geoscience technology can be made more effective by taking the working neurobiological principles of our brains into account. This book is appropriate for multiple audiences, including neuroscientists, cognitive scientists and geoscientists, presenting both theoretical and experimental results. - Presents the neurological background of human brain function and cognition as it relates to the geosciences - Explores possible links between geophysics, neural anatomy and neural physiology - Dissects topics with a multidisciplinary approach and balanced combination of theory and applications - Examines the potential mechanism by which exploration geoscience is triggered by specific neural systems located in primordial areas of the subcortical brain - Proposes working hypotheses and possible scenarios for future research in neuroscience and the geosciences
Author : Constantin Cranganu
Publisher : Springer
Page : 298 pages
File Size : 31,22 MB
Release : 2015-04-20
Category : Technology & Engineering
ISBN : 3319165313
This book presents several intelligent approaches for tackling and solving challenging practical problems facing those in the petroleum geosciences and petroleum industry. Written by experienced academics, this book offers state-of-the-art working examples and provides the reader with exposure to the latest developments in the field of intelligent methods applied to oil and gas research, exploration and production. It also analyzes the strengths and weaknesses of each method presented using benchmarking, whilst also emphasizing essential parameters such as robustness, accuracy, speed of convergence, computer time, overlearning and the role of normalization. The intelligent approaches presented include artificial neural networks, fuzzy logic, active learning method, genetic algorithms and support vector machines, amongst others. Integration, handling data of immense size and uncertainty, and dealing with risk management are among crucial issues in petroleum geosciences. The problems we have to solve in this domain are becoming too complex to rely on a single discipline for effective solutions and the costs associated with poor predictions (e.g. dry holes) increase. Therefore, there is a need to establish a new approach aimed at proper integration of disciplines (such as petroleum engineering, geology, geophysics and geochemistry), data fusion, risk reduction and uncertainty management. These intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and mining, data analysis and interpretation, and knowledge discovery, from diverse data such as 3-D seismic, geological data, well logging, and production data. This book is intended for petroleum scientists, data miners, data scientists and professionals and post-graduate students involved in petroleum industry.
