Download Free A Data Estimation Based Approach For Quasi Continuous Seismic Reservoir Monitoring Book in PDF and EPUB Free Download. You can read online A Data Estimation Based Approach For Quasi Continuous Seismic Reservoir Monitoring and write the review.

Current strategies and logistics for seismic data acquisition impose restrictions on the calendar-time temporal resolution obtainable for a given time-lapse monitoring program. One factor that restricts the implementation of a quasi-continuous monitoring program using conventional strategies is the time it takes to acquire a complete survey. Here quasi-continuous monitoring describes the process of reservoir monitoring at short time intervals. This dissertation describes an approach that circumvents the restriction by requiring only a subset of a complete survey data each time an image of the reservoir is needed. Ideally, the time interval between survey subset acquisitions should be short so that changes in the reservoir properties are small. The accumulated data acquired are used to estimate the unavailable data at the monitor survey time, and the combined known and estimated data are used to produce an image of the subsurface for monitoring. Quasi-continuous seismic monitoring can be used to monitor geologic reservoirs during the injection phase of a carbon dioxide sequestration project. It can also be used to monitor reservoir changes between injector and producer wells during the secondary recovery phase in an oil field. The primary advantage of a quasi-continuous monitoring strategy over the conventional strategy is the high temporal resolution of the reservoir changes obtainable. Naturally, the spatial resolution of the image obtained using a subset of the data from a full survey will be worse than the spatial resolution of the image obtained using the complete data from a full survey. However, if the unavailable data are estimated perfectly, the spatial resolution is not lost. The choice of estimation algorithm and the size of the known data play an important role in the success of the approach presented in this dissertation.
Current strategies and logistics for seismic data acquisition impose restrictions on the calendar-time temporal resolution obtainable for a given time-lapse monitoring program. One factor that restricts the implementation of a quasi-continuous monitoring program using conventional strategies is the time it takes to acquire a complete survey. Here quasi-continuous monitoring describes the process of reservoir monitoring at short time intervals. This dissertation describes an approach that circumvents the restriction by requiring only a subset of a complete survey data each time an image of the reservoir is needed. Ideally, the time interval between survey subset acquisitions should be short so that changes in the reservoir properties are small. The accumulated data acquired are used to estimate the unavailable data at the monitor survey time, and the combined known and estimated data are used to produce an image of the subsurface for monitoring. Quasi-continuous seismic monitoring can be used to monitor geologic reservoirs during the injection phase of a carbon dioxide sequestration project. It can also be used to monitor reservoir changes between injector and producer wells during the secondary recovery phase in an oil field. The primary advantage of a quasi-continuous monitoring strategy over the conventional strategy is the high temporal resolution of the reservoir changes obtainable. Naturally, the spatial resolution of the image obtained using a subset of the data from a full survey will be worse than the spatial resolution of the image obtained using the complete data from a full survey. However, if the unavailable data are estimated perfectly, the spatial resolution is not lost. The choice of estimation algorithm and the size of the known data play an important role in the success of the approach presented in this dissertation.
Seismic reservoir characterization aims to build 3-dimensional models of rock and fluid properties, including elastic and petrophysical variables, to describe and monitor the state of the subsurface for hydrocarbon exploration and production and for CO2 sequestration. Rock physics modeling and seismic wave propagation theory provide a set of physical equations to predict the seismic response of subsurface rocks based on their elastic and petrophysical properties. However, the rock and fluid properties are generally unknown and surface geophysical measurements are often the only available data to constrain reservoir models far away from well control. Therefore, reservoir properties are generally estimated from geophysical data as a solution of an inverse problem, by combining rock physics and seismic models with inverse theory and geostatistical methods, in the context of the geological modeling of the subsurface. A probabilistic approach to the inverse problem provides the probability distribution of rock and fluid properties given the measured geophysical data and allows quantifying the uncertainty of the predicted results. The reservoir characterization problem includes both discrete properties, such as facies or rock types, and continuous properties, such as porosity, mineral volumes, fluid saturations, seismic velocities and density. Seismic Reservoir Modeling: Theory, Examples and Algorithms presents the main concepts and methods of seismic reservoir characterization. The book presents an overview of rock physics models that link the petrophysical properties to the elastic properties in porous rocks and a review of the most common geostatistical methods to interpolate and simulate multiple realizations of subsurface properties conditioned on a limited number of direct and indirect measurements based on spatial correlation models. The core of the book focuses on Bayesian inverse methods for the prediction of elastic petrophysical properties from seismic data using analytical and numerical statistical methods. The authors present basic and advanced methodologies of the current state of the art in seismic reservoir characterization and illustrate them through expository examples as well as real data applications to hydrocarbon reservoirs and CO2 sequestration studies.
This book introduces methodologies for subsurface imaging based upon asymptotic and trajectory-based methods for modeling fluid flow, transport and deformation. It describes trajectory-based imaging from its mathematical formulation, through the construction and solution of the imaging equations, to the assessment of the accuracy and resolution associated with the image. Unique in its approach, it provides a unified framework for the complete spectrum of physical phenomena from wave-like hyperbolic problems to diffusive parabolic problems and non-linear problems of mixed character. The practical aspects of imaging, particularly efficient and robust methods for updating high resolution geologic models using fluid flow, transport and geophysical data, are emphasized throughout the book. Complete with online software applications and examples that enable readers to gain hands-on experience, this volume is an invaluable resource for graduate-level courses, as well as for academic researchers and industry practitioners in the fields of geoscience, hydrology, and petroleum and environmental engineering.
Optimal Seismic Deconvolution: An Estimation-Based Approach presents an approach to the problem of seismic deconvolution. It is meant for two different audiences: practitioners of recursive estimation theory and geophysical signal processors. The book opens with a chapter on elements of minimum-variance estimation that are essential for all later developments. Included is a derivation of the Kaiman filter and discussions of prediction and smoothing. Separate chapters follow on minimum-variance deconvolution; maximum-likelihood and maximum a posteriori estimation methods; the philosophy of maxi.
Covers the application and impact of 4D monitoring for the oil and gas industry, along with requirements, modelling, and acquisition techniques to ensure good data acquisition and use in diagnosing reservoir production effects and updating reservoir simulation models; key lessons on measuring small production differences are highlighted.
Time Lapse Approach to Monitoring Oil, Gas, and CO2 Storage by Seismic Methods delivers a new technology to geoscientists, well logging experts, and reservoir engineers, giving them a new basis on which to influence decisions on oil and gas reservoir management. Named ACROSS (Accurately Controlled and Routinely Operated Signal System), this new evaluation method is presented to address more complex reservoirs, such as shale and heavy oil. The book also discusses prolonged production methods for enhanced oil recovery. The monitoring of storage zones for carbon capture are also included, all helping the petroleum and reservoir engineer to fully extend the life of a field and locate untapped pockets of additional oil and gas resources. Rounded out with case studies from locations such as Japan, Saudi Arabia, and Canada, this book will help readers, scientists, and engineers alike to better manage the life of their oil and gas resources and reservoirs. - Benefits both geoscientists and reservoir engineers to optimize complex reservoirs such as shale and heavy oil - Explains a more accurate and cost efficient reservoir monitoring technology called ACROSS (Accurately Controlled and Routinely Operated Signal System) - Illustrates real-world application through multiple case studies from around the world
Joint integration of time-lapse seismic, time-lapse electromagnetic, and production data can provide a powerful means of characterizing and monitoring reservoirs. Those data contain complementary information about the changes in the reservoir during operation, and, thus, their proper integration can lead to more reliable forecasts and optimal decisions in reservoir management. This dissertation focuses on developing workflows to jointly integrate time-lapse seismic, electromagnetic, and production data because this task is significantly time-consuming and very challenging. The first part of the thesis addresses a quick and efficient method, which can provide a tool for locating the changes in the reservoir and assessing the uncertainty associated with the estimation quantitatively. The developed workflow, termed statistical integration workflow, utilizes well logs to link reservoir properties with seismic and electromagnetic data by building the joint probability distribution. A new upscaling method from well logs to the scales of seismic and electromagnetic measurements is established using multiple-point geostatistical simulation. The statistical integration workflow is applied to facies classification and the detection of depleted regions. Stochastic optimization is also investigated in this dissertation. As the joint optimization of time-lapse seismic, electromagnetic, and production data requires a huge amount of computational time, we formulate a new algorithm, the probabilistic particle swarm optimization (Pro-PSO). This algorithm is designed to alleviate the time-consuming job by parallel computations of multiple candidate models and the improvement of models based on information sharing. More importantly, any probabilistic priors, such as geological information, can be incorporated into the algorithm. Applications are investigated for a synthetic example of seismic inversion and flow history matching of a Gaussian porosity field, parameterized by its spatial principal components. The result validates the effectiveness of Pro-PSO as compared with conventional PSO. Another version of Pro-PSO for discrete parameters, called Pro-DPSO, is also developed where particles (candidate models) move in the probability mass function space instead of the parameter space. Then, Pro-DPSO is hybridized with a multiple-point geostatistical algorithm, the single normal equation algorithm (SNESIM) to preserve non-Gaussian geological features. This hybridized algorithm (Pro-DPSO-SNESIM) is evaluated on a synthetic example of seismic inversion and compared with a Markov chain Monte Carlo (MCMC) optimization method. The algorithms Pro-DPSO and Pro-DPSO-SNESIM provide not only optimized models but also optimized probability mass functions (pmf) of parameters. Therefore, it also presents the variations of realizations sampled from the optimized pmfs. Lastly, we introduce the specialization workflow of Pro-PSO algorithms for the joint integration of time-lapse seismic, time-lapse electromagnetic, and production data. In this workflow, the particles of Pro-PSO are divided into several groups, and each group is specialized in the evaluation of a particular type of data misfit. Dividing up the objective function components among different groups of particles allows the algorithm to take advantage of situations where different forward simulators for each type of data require very different computational times per iteration. The optimization is implemented by sharing multiple best models from each type of data misfit, not by sharing a single best model based on the sum (or other combinations) of all the misfits. The "divide-and-conquer" workflow is evaluated on two synthetic cases of joint integration showing that it is much more efficient than an equivalent conventional workflow minimizing an integrated objective function.
Reservoir characterization and history matching are essential steps in various subsurface applications, such as petroleum exploration and production and geological carbon sequestration, aiming to estimate the rock and fluid properties of the subsurface from geophysical measurements and borehole data. Mathematically, both tasks can be formulated as inverse problems, which attempt to find optimal earth models that are consistent with the true measurements. The objective of this dissertation is to develop a stochastic inversion method to improve the accuracy of predicted reservoir properties as well as quantification of the associated uncertainty by assimilating both the surface geophysical observations and the production data from borehole using Ensemble Smoother with Multiple Data Assimilation. To avoid the common phenomenon of ensemble collapse in which the model uncertainty would be underestimated, we propose to re-parameterize the high-dimensional geophysics data with data order reduction methods, for example, singular value decomposition and deep convolutional autoencoder, and then perform the models updating efficiently in the low-dimensional data space. We first apply the method to seismic and rock physics inversion for the joint estimation of elastic and petrophysical properties from the pre-stack seismic data. In the production or monitoring stage, we extend the proposed method to seismic history matching for the prediction of porosity and permeability models by integrating both the time-lapse seismic and production data. The proposed method is tested on synthetic examples and successfully applied in petroleum exploration and production and carbon dioxide sequestration.