Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization Books

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Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization


Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization
  • Author : Siddharth Misra
  • Publisher : Elsevier
  • Release : 2021-07-13
  • ISBN : 9780128214558
  • Language : En, Es, Fr & De
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Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. Includes case studies to add additional color to the presented content Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization


Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization
  • Author : Siddharth Misra
  • Publisher : Elsevier
  • Release : 2021-07-23
  • ISBN : 9780128214398
  • Language : En, Es, Fr & De
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Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. Includes case studies to add additional color to the presented content Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm

Machine Learning for Subsurface Characterization


Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra
  • Publisher : Gulf Professional Publishing
  • Release : 2019-10-12
  • ISBN : 9780128177372
  • Language : En, Es, Fr & De
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Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support

Machine Learning for Subsurface Characterization


Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra
  • Publisher : Gulf Professional Publishing
  • Release : 2019-06-15
  • ISBN : 0128177365
  • Language : En, Es, Fr & De
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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 due to financial, operational, regulatory, risk, technological and environmental challenges. The book introduces readers 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 videos to assist training and learning. Field cases are also presented to demonstrate 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, this book delivers a missing piece for the reservoir engineer's toolbox. Focuses on applying predictive modeling and machine learning from real case studies and Q&A sessions at the end of each chapter Teaches users how to develop codes, such as MATLAB, PYTHON, R and TENSORFLOW with step-by-step guides included Helps readers visually learn code development with video demonstrations

Multifrequency Crosshole EM Imaging for Reservoir Characterization FY 1994 Annual Report


Multifrequency Crosshole EM Imaging for Reservoir Characterization  FY 1994 Annual Report
  • Author :
  • Publisher :
  • Release : 1995
  • ISBN : OCLC:68393281
  • Language : En, Es, Fr & De
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Electrical conductivity of sedimentary rocks is controlled by the porosity, hydraulic permeability, temperature, saturation, and the pore fluid conductivity. These rock parameters play important roles in the development and production of hydrocarbon (petroleum and natural gas) resources. For these reasons, resistivity well logs have long been used by geologists and reservoir engineers in petroleum industries to map variations in pore fluid, to distinguish between rock types, and to determine completion intervals in wells. Reservoir simulation and process monitoring rely heavily on the physical characteristics of the reservoir model. Over a period of three years (1991-1993) there was an initial phase of crosshole EM technique development via an informal partnership between LLNL and LBL. Researchers developed field instrumentation to apply to oil field for monitoring EOR thermal processes. Specifically, a prototype single-frequency instrumentation was developed and with this system we have conducted field surveys in four separate locations. Theory and software were developed to interpret these data by providing subsurface images of the electrical conductivity. In spite of our initial success in developing practical EM techniques, we still had severe instrumentation limitations and shortcomings in interpretation for other than simple structures. The field equipment was designed to work only at a single frequency at a time and the transmitter must be opened to change frequencies. The equipment was also significantly noiser at higher frequencies. For high-resolution applications we need to take full advantage of the resolution inherent in the data. The development of a high-resolution subsurface conductivity imaging methods would have benefits far beyond the petroleum application. Such techniques would be very useful in environmental applications, mineral and geothermal exploration and for civil engineering applications.