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.
Petrophysical Characterization and Fluids Transport in Unconventional Reservoirs presents a comprehensive look at these new methods and technologies for the petrophysical characterization of unconventional reservoirs, including recent theoretical advances and modeling on fluids transport in unconventional reservoirs. The book is a valuable tool for geoscientists and engineers working in academia and industry. Many novel technologies and approaches, including petrophysics, multi-scale modelling, rock reconstruction and upscaling approaches are discussed, along with the challenge of the development of unconventional reservoirs and the mechanism of multi-phase/multi-scale flow and transport in these structures. - Includes both practical and theoretical research for the characterization of unconventional reservoirs - Covers the basic approaches and mechanisms for enhanced recovery techniques in unconventional reservoirs - Presents the latest research in the fluid transport processes in unconventional reservoirs
This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications. The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.
Acquisition of downhole temperature measurements, in addition to production data, is routine in production systems. The temperature measurements, which are currently being used for pressure data correction, are cheap to acquire, accurate and have good resolutions. The answer to the question of how useful these temperature measurements can be, beyond the current utilization for pressure correction, was the goal of this research work. In the first part of this work, a mechanistic multiphysics and multiscale model for thermal transport process in a porous medium was developed, accounting for compressibility and viscous dissipation effects like Joule-Thomson and adiabatic expansion phenomena. To validate the model, a laboratory experiment was designed to allow for a controlled flow of air through a porous core, while measuring the temperature changes at different locations. The data acquired were used to verify the model and perform sensitivity studies, and the results showed functional dependencies of the model on useful reservoir parameters such as porosity, flow velocities and thermal properties of the rock and fluid; and these functional dependencies revealed the potential of temperature data as an additional source of constraining data in temporal and distributed reservoir parameter estimation. In addition, the temperature model was well suited for the application of a number of analytical tools that lead to the extraction of these useful reservoir characteristic information. In the second part, using multiresolution methods based on the second derivative of the Gaussian kernel, temperature measurements were combined with pressure data to improve the identification of transients in data as well as yield better behavioral filtering. Until now, only pressure measurements are used and this has shown to be unreliable. The approach developed here exploited the independence between the pressure and temperature measurements to constrain the estimation of the location of the breakpoints. The third segment of this research exploited the convective nature of thermal transport during flow to characterize near wellbore properties such as the extent of damage around a well (or extent of stimulation). The model lent itself to the application of the semianalytical Operator Splitting decomposition technique and as a result, the solution of the advection component could be separated and used to estimate near-wellbore structures such as damage or stimulation radius and permeability. As temperature measurements are an independent source of measurements, a joint inversion of production data and temporal temperature measurements, taken from multiwell production systems, showed a significant improvement in the reservoir state estimation problem, using state space estimation techniques like the Ensemble Kalman filter. This marked improvement was over the results from current approaches which match only production data. Results showed that introducing temperature improved the resolution of both permeability and porosity fields significantly. The last part of this research dealt with the estimation of flowrate, using only temperature measurements. The temperature model showed a strong functional dependence of temperature on flowrates at high Peclet number. By deconvolution, the advective flow kernel was separated from the diffusion part, and the complete flowrate history reconstructed from this kernel. Results showed that in synthetic and field cases, this extracted flowrate compared well with the true flowrate measurements. The philosophical significance of this work is that low-cost temperature measurements, which are measured routinely in producing wells, are a promising source of additional data for further constraining of reservoir characterization and optimization problems.
This book consists of 44 technical papers presented at the Ninth International Geostatistics Congress held in Oslo, Norway in June 2012. The papers have been reviewed by a panel of specialists in Geostatistics. The book is divided into four main sections: Theory; Petroleum; Mining; and Environment, Climate and Hydrology. The first section focuses on new ideas of general interest to many fields of applications. The next sections are more focused on the particular needs of the particular industry or activity. Geostatistics is vital to any industry dependent on natural resources. Methods from geostatistics are used for estimating reserves, quantifying economical risk and planning of future industrial operations. Geostatistics is also an important tool for mapping environmental hazard and integrating climate data.
This book is a compilation of selected papers from the 3rd International Petroleum and Petrochemical Technology Conference (IPPTC 2019). The work focuses on petroleum & petrochemical technologies and practical challenges in the field. It creates a platform to bridge the knowledge gap between China and the world. The conference not only provides a platform to exchanges experience but also promotes the development of scientific research in petroleum & petrochemical technologies. The book will benefit a broad readership, including industry experts, researchers, educators, senior engineers and managers.
This open-access textbook's significant contribution is the unified derivation of data-assimilation techniques from a common fundamental and optimal starting point, namely Bayes' theorem. Unique for this book is the "top-down" derivation of the assimilation methods. It starts from Bayes theorem and gradually introduces the assumptions and approximations needed to arrive at today's popular data-assimilation methods. This strategy is the opposite of most textbooks and reviews on data assimilation that typically take a bottom-up approach to derive a particular assimilation method. E.g., the derivation of the Kalman Filter from control theory and the derivation of the ensemble Kalman Filter as a low-rank approximation of the standard Kalman Filter. The bottom-up approach derives the assimilation methods from different mathematical principles, making it difficult to compare them. Thus, it is unclear which assumptions are made to derive an assimilation method and sometimes even which problem it aspires to solve. The book's top-down approach allows categorizing data-assimilation methods based on the approximations used. This approach enables the user to choose the most suitable method for a particular problem or application. Have you ever wondered about the difference between the ensemble 4DVar and the "ensemble randomized likelihood" (EnRML) methods? Do you know the differences between the ensemble smoother and the ensemble-Kalman smoother? Would you like to understand how a particle flow is related to a particle filter? In this book, we will provide clear answers to several such questions. The book provides the basis for an advanced course in data assimilation. It focuses on the unified derivation of the methods and illustrates their properties on multiple examples. It is suitable for graduate students, post-docs, scientists, and practitioners working in data assimilation.
This book is a guide to the use of inverse theory for estimation and conditional simulation of flow and transport parameters in porous media. It describes the theory and practice of estimating properties of underground petroleum reservoirs from measurements of flow in wells, and it explains how to characterize the uncertainty in such estimates. Early chapters present the reader with the necessary background in inverse theory, probability and spatial statistics. The book demonstrates how to calculate sensitivity coefficients and the linearized relationship between models and production data. It also shows how to develop iterative methods for generating estimates and conditional realizations. The text is written for researchers and graduates in petroleum engineering and groundwater hydrology and can be used as a textbook for advanced courses on inverse theory in petroleum engineering. It includes many worked examples to demonstrate the methodologies and a selection of exercises.
