Download or read book Identification of an Appropriate Data Assimilation Approach in Seismic History Matching and Its Effect on Prediction Uncertainty written by Nureddin R. Edris and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Assimilation Methods Algorithms and Applications written by Mark Asch and published by SIAM. This book was released on 2016-12-29 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is an approach that combines observations and model output, with the objective of improving the latter. This book places data assimilation into the broader context of inverse problems and the theory, methods, and algorithms that are used for their solution. It provides a framework for, and insight into, the inverse problem nature of data assimilation, emphasizing why and not just how. Methods and diagnostics are emphasized, enabling readers to readily apply them to their own field of study. Readers will find a comprehensive guide that is accessible to nonexperts; numerous examples and diverse applications from a broad range of domains, including geophysics and geophysical flows, environmental acoustics, medical imaging, mechanical and biomedical engineering, economics and finance, and traffic control and urban planning; and the latest methods for advanced data assimilation, combining variational and statistical approaches.
Download or read book Principles of Data Assimilation written by Seon Ki Park and published by Cambridge University Press. This book was released on 2022-09-29 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.
Download or read book Stochastic and Deterministic Inversion Methods for History Matching of Production and Time Lapse Seismic Data written by Shingo Watanabe and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic history matching methods utilize various kinds of inverse modeling techniques. In this dissertation, we examine ensemble Kalman filter as a stochastic approach for assimilating different types of production data and streamline-based inversion methods as a deterministic approach for integrating both production and time-lapse seismic data into high resolution reservoir models. For the ensemble Kalman filter, we develope a physically motivated phase streamline-based covariance localization method to improve data assimilation performance while capturing geologic continuities that affect the flow dynamics and preserving model variability among the ensemble of models. For the streamline-based inversion method, we derived saturation and pressure drop sensitivities with respect to reservoir properties along streamline trajectories and integrated time-lapse seismic derived saturation and pressure changes along with production data using a synthetic model and the Brugge field model. Our results show the importance of accounting for both saturation and pressure changes in the reservoir responses in order to constrain the history matching solutions. Finally we demonstrated the practical feasibility of a proposed structured work- flow for time-lapse seismic and production data integration through the Norne field application. Our proposed method follows a two-step approach: global and local model calibrations. In the global step, we reparameterize the field permeability heterogeneity with a Grid Connectivity-based Transformation with the basis coefficient as parameters and use a Pareto-based multi-objective evolutionary algorithm to integrate field cumulative production and time-lapse seismic derived acoustic impedance change data. The method generates a suite of trade-off solutions while fitting production and seismic data. In the local step, first the time-lapse seismic data is integrated using the streamline-derived sensitivities of acoustic impedance with respect to reservoir permeability incorporating pressure and saturation effects in-between time-lapse seismic surveys. Next, well production data is integrated by using a generalized travel time inversion method to resolve fine-scale permeability variations between well locations. After model calibration, we use the ensemble of history matched models in an optimal rate control strategy to maximize sweep and injection efficiency by equalizing flood front arrival times at all producers while accounting for geologic uncertainty. Our results show incremental improvement of ultimate recovery and NPV values. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151664
Download or read book Quantifying Uncertainty in Subsurface Systems written by Céline Scheidt and published by John Wiley & Sons. This book was released on 2018-04-27 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under the Earth’s surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge. Volume highlights include: • A multi-disciplinary treatment of uncertainty quantification • Case studies with actual data that will appeal to methodology developers • A Bayesian evidential learning framework that reduces computation and modeling time Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. Read the Editors’ Vox: https://eos.org/editors-vox/quantifying-uncertainty-about-earths-resources
Download or read book Fast History Matching of Time lapse Seismic and Production Data for High Resolution Models written by Eduardo Antonio Jimenez and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrated reservoir modeling has become an important part of day-to-day decision analysis in oil and gas management practices. A very attractive and promising technology is the use of time-lapse or 4D seismic as an essential component in subsurface modeling. Today, 4D seismic is enabling oil companies to optimize production and increase recovery through monitoring fluid movements throughout the reservoir. 4D seismic advances are also being driven by an increased need by the petroleum engineering community to become more quantitative and accurate in our ability to monitor reservoir processes. Qualitative interpretations of time-lapse anomalies are being replaced by quantitative inversions of 4D seismic data to produce accurate maps of fluid saturations, pore pressure, temperature, among others. Within all steps involved in this subsurface modeling process, the most demanding one is integrating the geologic model with dynamic field data, including 4Dseismic when available. The validation of the geologic model with observed dynamic data is accomplished through a "history matching" (HM) process typically carried out with well-based measurements. Due to low resolution of production data, the validation process is severely limited in its reservoir areal coverage, compromising the quality of the model and any subsequent predictive exercise. This research will aim to provide a novel history matching approach that can use information from high-resolution seismic data to supplement the areally sparse production data. The proposed approach will utilize streamline-derived sensitivities as means of relating the forward model performance with the prior geologic model. The essential ideas underlying this approach are similar to those used for high-frequency approximations in seismic wave propagation. In both cases, this leads to solutions that are defined along "streamlines" (fluid flow), or "rays" (seismic wave propagation). Synthetic and field data examples will be used extensively to demonstrate the value and contribution of this work. Our results show that the problem of non-uniqueness in this complex history matching problem is greatly reduced when constraints in the form of saturation maps from spatially closely sampled seismic data are included. Further on, our methodology can be used to quickly identify discrepancies between static and dynamic modeling. Reducing this gap will ensure robust and reliable models leading to accurate predictions and ultimately an optimum hydrocarbon extraction.
Download or read book Data Assimilation written by Geir Evensen and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Assimilation Mathematical Concepts and Instructive Examples written by Rodolfo Guzzi and published by Springer. This book was released on 2015-09-16 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book endeavours to give a concise contribution to understanding the data assimilation and related methodologies. The mathematical concepts and related algorithms are fully presented, especially for those facing this theme for the first time. The first chapter gives a wide overview of the data assimilation steps starting from Gauss' first methods to the most recent as those developed under the Monte Carlo methods. The second chapter treats the representation of the physical system as an ontological basis of the problem. The third chapter deals with the classical Kalman filter, while the fourth chapter deals with the advanced methods based on recursive Bayesian Estimation. A special chapter, the fifth, deals with the possible applications, from the first Lorenz model, passing trough the biology and medicine up to planetary assimilation, mainly on Mars. This book serves both teachers and college students, and other interested parties providing the algorithms and formulas to manage the data assimilation everywhere a dynamic system is present.
Download or read book Data Assimilation written by William Lahoz and published by Springer. This book was released on 2014-11-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Download or read book Data Assimilation written by William Lahoz and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation methods were largely developed for operational weather forecasting, but in recent years have been applied to an increasing range of earth science disciplines. This book will set out the theoretical basis of data assimilation with contributions by top international experts in the field. Various aspects of data assimilation are discussed including: theory; observations; models; numerical weather prediction; evaluation of observations and models; assessment of future satellite missions; application to components of the Earth System. References are made to recent developments in data assimilation theory (e.g. Ensemble Kalman filter), and to novel applications of the data assimilation method (e.g. ionosphere, Mars data assimilation).
Download or read book Fast History Matching of Time lapse Seismic and Production data for High Resolution Models written by Alvaro Rey Amaya and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Seismic data have been established as a valuable source of information for the construction of reservoir simulation models, most commonly for determination of the modeled geologic structure, and also for population of static petrophysical properties (e.g. porosity, permeability). More recently, the availability of repeated seismic surveys over the time scale of years (i.e., 4D seismic) has shown promising results for the qualitative determination of changes in fluid phase distributions and pressure required for determination of areas of bypassed oil, swept volumes and pressure maintenance mechanisms. Quantitatively, and currently the state of the art in reservoir model characterization, 4D seismic data have proven distinctively useful for the calibration of geologic spatial variability which ultimately contributes to the improvement of reservoir development and management strategies. Among the limited variety of techniques for the integration of dynamic seismic data into reservoir models, streamline-based techniques have been demonstrated as one of the more efficient approaches as a result of their analytical sensitivity formulations. Although streamline techniques have been used in the past to integrate time-lapse seismic attributes, the applications were limited to the simplified modeling scenarios of two-phase fluid flow and invariant streamline geometry throughout the production schedule. This research builds upon and advances existing approaches to streamline-based seismic data integration for the inclusion of both production and seismic data under varying field conditions. The proposed approach integrates data from reservoirs under active reservoir management and the corresponding simulation models can be constrained using highly detailed or realistic schedules. Fundamentally, a new derivation of seismic sensitivities is proposed that is able to represent a complex reservoir evolution between consecutive seismic surveys. The approach is further extended to manage compositional reservoir simulation with dissolution effects and gravity-convective-driven flows which, in particular, are typical of CO2 transport behavior following injection into deep saline aquifers. As a final component of this research, the benefits of dynamic data integration on the determination of swept and drained volumes by injection and production, respectively, are investigated. Several synthetic and field reservoir modeling scenarios are used for an extensive demonstration of the efficacy and practical feasibility of the proposed developments.
Download or read book The Rock Physics Handbook written by Gary Mavko and published by Cambridge University Press. This book was released on 2009-04-30 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significantly expanded new edition of this practical guide to rock physics and geophysical interpretation for reservoir geophysicists and engineers.
Download or read book DEVELOPMENT OF AN ASSISTED HISTORY MATCHING AND UNCERTAINTY QUANTIFICATION TOOL BASED ON GAUSSIAN PROCESSES PROXY MODELS AND VARIOGRAM BASED SENSITIVITY ANALYSIS written by Sachin Rana and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: History matching is an inverse solution process in which uncertain parameters of the numerical reservoir model are tuned in an eort to minimize the mismatch between simulated production and observed production data. History matching problem can be solved as an optimization or data assimilation problem. In this research, the history matching problem is solved from the optimization point of view. Currently, many commercial history matching tools use evolutionary strategy optimization algorithms such as dierential evolution, particle swarm optimization etc. to find solutions of history matching. However, these algorithms usually require a large number of numerical simulation runs in order to converge to acceptable solutions. If each numerical simulation takes an extensive time to complete, these algorithms become inecient. In this research, a new assisted history matching tool named as GP-VARS is presented that can provide multiple solutions of history matching fewer numerical simulations. GP-VARS uses Gaussian process (GP) based proxy models to provide fast approximate forward solutions which are used in Bayesian optimization to find history match solutions in an iterative manner. An application of VARS based sensitivity analysis is applied on forward GP model to calculate the sensitivity index for uncertain reservoir parameters. The results of sensitivity analysis are used to regulate the lower and upper bounds of dierent reservoir parameters in order to achieve faster convergence. A second GP model is used to provide an inverse solution which also provides temporary history match solutions. Since the history matching problem has non-unique solutions, the uncertainty in reservoir parameters is quantified using Markov Chain Monte Carlo (MCMC ) sampling from the trained forward GP model. The collected MCMC samples are then passed to a third GP model that is trained to predict the EUR values for any combination of reservoir parameters. The GP-VARS methodology is applied to three dierent heterogeneous reservoir case studies including a benchmark PUNQ-S3 reservoir located in north sea and the M4.1 reservoir located in Gulf of Mexico. The results show that history matching can be performed in approximately four times less number of numerical simulation runs as compared to the state of the art dierential evolution algorithm. In addition, it was found that the P50 estimates of EUR are in close agreement with truth values in the presented case studies.
Download or read book Markov Chain Monte Carlo Algorithm Integrated 4D Seismic Reservoir Characterization and Uncertainty Analysis in a Bayesian Framework written by Tiancong Hong and published by . This book was released on 2008 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Assimilation and its Applications written by Maithili Sharan and published by Birkhäuser. This book was released on 2012-05-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation is a novel, versatile methodology for estimating atmospheric and oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observational data with the underlying dynamical principles governing the system under observation. This volume contains many original findings in data assimilation and its applications related to atmospheric, oceanic and environmental systems. This covers various data assimilation techniques with in Bayesian and non-Bayesian framework ranging from Least-Square, nudging, three dimensional variational (3DVAR), four-dimensional variational (4DVAR), Local Ensemble Kalman filter, Genetic algorithm etc. This also covers the applications to extreme weather event, hurricane, Asian summer monsoon, structure of the barrier layer in the equatorial Pacific ocean and identification of emission sources. This volume will be useful as a reading material in graduate level courses dealing with data assimilation and its application to meteorology, ocean and air quality. The scientific community at large especially younger scientists will find this book a useful addition to their personal and institutional libraries.
Download or read book Integrated Evaluation of Structural Uncertainty Using History Matching from Seismic Imaging Uncertainty Model written by Satomi Suzuki and published by . This book was released on 2007 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structural uncertainty is the first-order uncertainty in reservoir modeling both in terms of (1) the difficulty of structural interpretation due to limited data acquisition/quality and (2) the magnitude of its impact on uncertainty in hydrocarbon in-place and fluid flow performance. Though it is a common practice to consider structural uncertainty in the evaluation of hydrocarbon in-place in the appraisal phase of field developments, structural uncertainty is rarely considered when evaluating future production performance in the production phase. The reason is mainly the difficulty in integrating dynamic data into structural uncertainty modeling. This research aims at developing a method/workflow for structural uncertainty modeling which integrates geophysical and geological data during the process of history matching. This dissertation first proposes a semiautomatic seismic interpretation method using geostatistical pattern simulation. From the resulting multiple structural interpretations, a prior stochastic structural model is built and used as a geological/geophysical constraint for the subsequent history matching. Next, a new history matching method is proposed: numerical models are inverted from dynamic fluid flow response while honoring the previous structural model. The method consists of searching for numerical models that match production history from a large set of prior structural model realizations. To make such a search effective, a parameter space defined with a "similarity distance" is introduced. The inverse solutions are found in this space using a stochastic search method. Synthetic but realistic reservoir examples are presented to demonstrate the proposed workflow/methods.
