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Core Analysis: A Best Practice Guide is a practical guide to the design of core analysis programs. Written to address the need for an updated set of recommended practices covering special core analysis and geomechanics tests, the book also provides unique insights into data quality control diagnosis and data utilization in reservoir models. The book's best practices and procedures benefit petrophysicists, geoscientists, reservoir engineers, and production engineers, who will find useful information on core data in reservoir static and dynamic models. It provides a solid understanding of the core analysis procedures and methods used by commercial laboratories, the details of lab data reporting required to create quality control tests, and the diagnostic plots and protocols that can be used to identify suspect or erroneous data. - Provides a practical overview of core analysis, from coring at the well site to laboratory data acquisition and interpretation - Defines current best practice in core analysis preparation and test procedures, and the diagnostic tools used to quality control core data - Provides essential information on design of core analysis programs and to judge the quality and reliability of core analysis data ultimately used in reservoir evaluation - Of specific interest to those working in core analysis, porosity, relative permeability, and geomechanics
This best-seller can help anyone whose role is to try to find specific causes for failures. It provides detailed steps for solving problems, focusing more heavily on the analytical process involved in finding the actual causes of problems. It does this using figures, diagrams, and tools useful for helping to make our thinking visible. This increases our ability to see what is truly significant and to better identify errors in our thinking. In the sections on finding root causes, this second edition now includes: more examples on the use of multi-vari charts; how thought experiments can help guide data interpretation; how to enhance the value of the data collection process; cautions for analyzing data; and what to do if one can’t find the causes. In its guidance on solution identification, biomimicry and TRIZ have been added as potential solution identification techniques. In addition, the appendices have been revised to include: an expanded breakdown of the 7 M’s, which includes more than 50 specific possible causes; forms for tracking causes and solutions, which can help maintain alignment of actions; techniques for how to enhance the interview process; and example responses to problem situations that the reader can analyze for appropriateness.
This book offers a compact guide to geological core analysis, covering both theoretical and practical aspects of geological studies of reservoir cores. It equips the reader with the knowledge needed to precisely and accurately analyse cores. The book begins by providing a description of a coring plan, coring, and core sampling and continues with a sample preparation for geological analysis. It then goes on to explain how the samples are named, classified and integrated in order to understand the geological properties that dictate reservoir characteristics. Subsequently, porosity and permeability data derived from routine experiments are combined to define geological rock types and reduce reservoir heterogeneity. Sequence stratigraphy is introduced for reservoir zonation. Core log preparation is also covered, allowing reservoirs to be analysed even more accurately. As the study of core samples is the only way to accurately gauge reservoir properties, this book provides a useful guide for all geologists and engineers working with subsurface samples.
In this volume, recent advances in analytical and logging technology and their application to the analysis of sediment cores are presented. Developments in providing access to core data and associated datasets, and advances in data mining technology in order to integrate and interpret new and legacy datasets within the wider context of seafloor studies are also discussed.
Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval. Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.
This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.
Core analysis is an essential source of quantitative data on reservoir properties. These data are used for modeling the distribution and flow of oil and gas reservoirs, CO2 and hydrogen storage, and flow behavior in geothermal reservoirs. While several books cover core analysis for practitioners, this book fills a gap through introducing laboratory equipment and procedures used in core analysis and the theoretical aspects of the parameters. It offers detailed instructions on experimental execution for those with limited or no experience including risk analysis for high safety. Emphasizes the basic characterization of porous materials for multiphase flow, specifically consolidated natural materials Features methods commonly used in the special core analysis within the oil and gas industry, extending to the emerging field of gas storage with occasional references to research-oriented equipment Offers detailed descriptions of laboratory exercises and instructions on data analysis suitable for student work Includes solutions to some exercises to demonstrate the application of measured parameters in reservoir evaluation Provides a unique combination of brief discussions on the basic theory of parameters, explanations of experimental principles, detailed experimental procedures according laboratory standards, and data analysis, with multiple laboratory-related example problems This concise and practical workbook is written for everyone interested in practical measurements of parameters needed for analysis of fluid flow in porous media, specifically students, or for starting staff in the laboratory, preferably with a background in geosciences or fluid mechanics or related topics.