Download or read book Soft Computing on Reservoir Characterization Production Forecasting written by Chithra Chakra N C and published by . This book was released on 2017-03-19 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Soft Computing and Intelligent Data Analysis in Oil Exploration written by M. Nikravesh and published by Elsevier. This book was released on 2003-04-22 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Soft Computing for Reservoir Characterization and Modeling written by Patrick Wong and published by Physica. This book was released on 2013-11-11 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the middle of the 20th century, Genrich Altshuller, a Russian engineer, analysed hundreds of thousands of patents and scientific publications. From this analysis, he developed TRIZ (G. Altshuller, "40 Principles: TRIZ Keys to Technical Innovation. TRIZ Tools," Volume 1, First Edition, Technical Innovation Center, Inc. , Worcester, MA, January 1998; Y. Salamatov, "TRIZ: The Right Solution at the Right Time. A Guide to Innovative Problem Solving. " Insytec B. V. , 1999), the theory of inventive problem solving, together with a series of practical tools for helping engineers solving technical problems. Among these tools and theories, the substance-field theory gives a structured way of representing problems, the patterns of evolution show the lifecycle of technical systems, the contradiction matrix tells you how to resolve technical contradictions, using the forty principles that describe common ways of improving technical systems. For example, if you want to increase the strength of a device, without adding too much extra weight to it, the contradiction matrix tells you that you can use "Principle 1: Segmentation," or "Principle 8: Counterweight," or "Principle 15: Dynamicity," or "Principle 40: Composite Materials. " I really like two particular ones: "Principle 1: Segmentation," and Principle 15: Dynamicity. " "Segmentation" shows how systems evolve from an initial monolithic form into a set of independent parts, then eventually increasing the number of parts until each part becomes small enough that it cannot be identified anymore.

Download or read book Soft Computing based Modeling for Intelligent Reservoir Characterization and Geosciences Applications written by Masoud Nikravesh and published by . This book was released on 2004 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Role of Soft Computing Techniques and Geosciences for Intelligent Reservoir Characterization and Oil Exploration written by Masoud Nikravesh and published by . This book was released on 2004 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Harness Oil and Gas Big Data with Analytics written by Keith R. Holdaway and published by John Wiley & Sons. This book was released on 2014-05-05 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

Download or read book Structuring An Integrative Approach For Field Development Planning Using Artificial Intelligence And Its Application To Tombua Landana Asset In Angola written by Sarath Ketineni and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Field development studies are at the forefront of common engineering practices in petroleum industry to maximize the returns on a given asset. In early stages of reservoir depletion, it is often a challenging task to accurately determine reservoir properties that are representative of the actual field. Reservoir modeling is the traditional way that engineers performed to develop field development and depletion plans. Due to different scales of data obtained from various sources like seismic data, well logs, cores, and production data, there is a lot of uncertainty in solving the inverse problem of estimating formation rock and fluid properties from the field data. Increase in complexity of formations and scarcity of reservoir data have made reservoir characterization a challenging task. Soft computing techniques have gained popularity in petroleum industry to identify complex patterns that exist between various reservoir data collected from multiple sources and be able to successfully characterize a reservoir.In this work, a work-flow is developed for devising a comprehensive reservoir characterization tool based on artificial neural network. A case study of Chevron's Tombua Landana Asset is used in demonstrating the tenets of the work-flow. The reservoir under consideration is highly heterogeneous in terms of property distribution and is believed to be highly channelized. The ANN based tool will assistin identifying sweet spots by predicting optimal well location/path/completion parameters and production schedule.The multilayer feed forward back propagation based neural network tool developed is able to capture the correlations that exist amongst seismic data, well logs, completion data, and production data. Well logs are correlated using surface seismic attributes and geometric location of wells with an average testingerror of less than 15%. The range of testing errors is in between 1-30%. The tool enables the user to predict the entire well log suite for even a horizontal well of user defined configuration through a graphic user interface. Having correlated seismic data with well logs, synthetic well logs are generated for the entire area of seismic coverage. To predict production data, along with seismic data and well logs, schedule of production and interference factors are incorporated as functional links. Upon analyzing the relevancies of input data, functional links based on geographic location and injection wells are included to make the prediction morereliable and robust. Production performance networks comprising cumulative oil,gas and water production performance prediction modules are developed to forecast performance of wells at undrilled locations. Oil networks indicated an average error of 21% in blind testing cases. Highly variable gas production could also be correlated with the seismic data and well log data within 32% error. Water production networks indicated a high error of 46% on blind testing cases. Oil, gas and water production forecast maps are generated using production performance networks. Maps generated indicate flow paths that exist in the field.

Download or read book Reservoir Characterization written by Fred Aminzadeh and published by John Wiley & Sons. This book was released on 2022-01-06 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: RESERVOIR CHARACTERIZATION The second volume in the series, “Sustainable Energy Engineering,” written by some of the foremost authorities in the world on reservoir engineering, this groundbreaking new volume presents the most comprehensive and updated new processes, equipment, and practical applications in the field. Long thought of as not being “sustainable,” newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but it can be made “greener” and more environmentally friendly. Sustainable energy engineering is where the technical, economic, and environmental aspects of energy production intersect and affect each other. This collection of papers covers the strategic and economic implications of methods used to characterize petroleum reservoirs. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry. Truly a snapshot of the state of the art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs. This outstanding new volume: Is a collection of papers on reservoir characterization written by world-renowned engineers and scientists and presents them here, in one volume Contains in-depth coverage of not just the fundamentals of reservoir characterization, but the anomalies and challenges, set in application-based, real-world situations Covers reservoir characterization for the engineer to be able to solve daily problems on the job, whether in the field or in the office Deconstructs myths that are prevalent and deeply rooted in the industry and reconstructs logical solutions Is a valuable resource for the veteran engineer, new hire, or petroleum engineering student

Download or read book Artificial Intelligence and Data Analytics for Energy Exploration and Production written by Fred Aminzadeh and published by John Wiley & Sons. This book was released on 2022-08-26 with total page 613 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in “smart oil fields”. This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next-generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints. In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.

Download or read book Machine Learning Solutions for Reservoir Characterization Management and Optimization written by Chiazor Nwachukwu and published by . This book was released on 2018 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific progress over the last decade has been significantly facilitated by the evolution of a new breed of intelligent solutions, characterized by their ability to learn without being explicitly programmed with the governing physics. In the oil and gas industry, machine learning alternatives are becoming increasingly popular, however most solutions within this discipline are still very raw in their conceptualization and application. In this work, three major areas in petroleum engineering are addressed and resolved using machine learning: well placement evaluation and optimization, time-series output prediction, and geological modeling. Simultaneous optimization of well placements and controls is a recurring problem in reservoir management and field development. Because of their high computational expense, reservoir simulators are limited in their applicability to joint optimization procedures requiring many evaluations. Data-driven proxies could provide inexpensive alternatives for approximating reservoir responses, however, geologic complexity of most reservoirs often makes it impossible to model or reproduce the response surface using well location data alone. We propose a machine learning approach in which the feature set is augmented by a connectivity network comprised of pairwise well-to-well connectivities for any potential well configuration. Connectivities are represented by ‘diffusive times of flight’ of the pressure front, computed using the Fast Marching Method (FMM). The Gradient Boosting Method is then used to build intelligent models for making reservoir-wide predictions such as net present value, given any set of well locations and control values. Accurate prediction of future reservoir performance and well production rates is important for optimizing oil recovery strategies. In the absence of geologic models, this could purely be considered as a time-series analysis problem. The premise of this class of problems is that relationships between input and output sequences can be learned from historical data and used to predict future output. However, because the state of the reservoir changes with time, the value of a future output variable such as production rate also depends on its own history. We introduce a novel scheme to predict reservoir output during recovery processes using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. The method was applied to two case studies wherein predictive models were built to forecast well production using historical rate data, yielding satisfactory results. A synthetic demonstration showed that the proposed method outperformed Capacitance Resistance Modeling (CRM) in terms of prediction accuracy. Spatial interpolation and geologic modeling of petrophysical properties are traditionally performed using conventional geostatistical algorithms. The most common techniques include the Sequential Gaussian Simulation (SGS) for continuous variable modeling, and multiple-point simulation (MPS) for facies or categorical variable modeling. These techniques produce adequate results but are prone to subjectivity and could rely heavily on the modeler’s intuition. Machine learning techniques provide a more automated alternative for geologic modeling, and have the ability to more accurately predict petrophysical properties outside the data locations. We propose a new hybridized method in which Bayesian Neural Network (BNN) predictions are used as kriging covariates in conjunction with SGS. The hybridized models show improved prediction accuracy in comparison with kriging and SGS, while retaining geological realism and producing exact estimates

Download or read book Practical Reservoir Engineering and Characterization written by Richard O. Baker and published by Gulf Professional Publishing. This book was released on 2015-04-30 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Reservoir Characterization expertly explains key technologies, concepts, methods, and terminology in a way that allows readers in varying roles to appreciate the resulting interpretations and contribute to building reservoir characterization models that improve resource definition and recovery even in the most complex depositional environments. It is the perfect reference for senior reservoir engineers who want to increase their awareness of the latest in best practices, but is also ideal for team members who need to better understand their role in the characterization process. The text focuses on only the most critical areas, including modeling the reservoir unit, predicting well behavior, understanding past reservoir performance, and forecasting future reservoir performance. The text begins with an overview of the methods required for analyzing, characterizing, and developing real reservoirs, then explains the different methodologies and the types and sources of data required to characterize, forecast, and simulate a reservoir. Thoroughly explains the data gathering methods required to characterize, forecast, and simulate a reservoir Provides the fundamental background required to analyze, characterize, and develop real reservoirs in the most complex depositional environments Presents a step-by-step approach for building a one, two, or three-dimensional representation of all reservoir types

Download or read book Simplicity Complexity and Modelling written by Mike Christie and published by John Wiley & Sons. This book was released on 2011-10-19 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.

Download or read book Data Analytics in Reservoir Engineering written by Sathish Sankaran and published by . This book was released on 2020-10-29 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Download or read book Reservoir Characterization II written by Larry W. Lake and published by Academic Press. This book was released on 1991-04-28 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for petroleum engineers, geologists and hydrologists, this book provides a detailed survey of the current practices, problems, research and trends in the field of reservoir characterization. Topics discussed include mesoscopic, macroscopic and megascopic scales.

Download or read book Reservoir Characterization written by and published by . This book was released on 1995 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cenozoic Foreland Basins of Western Europe written by A. Mascle and published by Geological Society of London. This book was released on 1998 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume provides a modern synthesis of foreland basin stratigraphy and structural geology. It covers the foothills and foreland basins of the northwestern Alps, the Pyrenees and the Betic thrust belt. The multidisciplinary approach includes both sedimentological and structural studies, plus numerical modelling as a tool to quantify and integrate the geological data. This book reults from the Integrated Basin Studies Project, which was funded by the European Commission. More than 200 researchers from 38 institutions in 15 countries have collaborated in the IBS project. Several papers from outside the project have also been included to provide the reader with a more comprehensive overview of Western Europe's Cenozoic foreland basins. This volume concentrates on scientific research, but many oil companies are actively exploring the foothills of thrust belts throughout the world.

Download or read book Recent Advances in Soft Computing and Data Mining written by Rozaida Ghazali and published by Springer Nature. This book was released on 2022-05-03 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book unfolds ways to transform data into innovative solutions perceived as new remarkable and meaningful value. It offers practical views of the concepts and techniques readers need to get the most out of their large-scale research and data mining projects. It strides them through the data-analytical thinking, circumvents the difficulty in deciphering complex data systems and obtaining commercialization value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad spectrum, an interdisciplinary field of scientific methods and processes. The book, Recent Advances in Soft Computing and Data Mining, delivers sufficient knowledge to tackle a wide range of issues seen in complex systems. This is done by exploring a vast combination of practices and applications by incorporating these two domains. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must choose the best design to approach the problem with the most efficient tools and techniques. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must understand the design choice and options of these approaches, thus to better appreciate the concepts, tools, and techniques used.