Download or read book Ray based Stochastic Inversion of Pre stack Seismic Data for Improved Reservoir Characterisation written by Dennis Wilhelmus van der Burg and published by . This book was released on 2007 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Inversion of Pre stack Seismic Data to Improve Forecasts of Reservoir Production written by Omar Javier Varela Londoño and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Novel Stochastic Inversion Methods and Workflow for Reservoir Characterization and Monitoring written by Yang Xue and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reservoir models are generally constructed from seismic, well logs and other related datasets using inversion methods and geostatistics. It has already been recognized by the geoscientists that such a process is prone to non-uniqueness. Practical methods for estimation of uncertainty still remain elusive. In my dissertation, I propose two new methods to estimate uncertainty in reservoir models from seismic, well logs and well production data. The first part of my research is aimed at estimating reservoir impedance models and their uncertainties from seismic data and well logs. This constitutes an inverse problem, and we recognize that multiple models can fit the measurements. A deterministic inversion based on minimization of the error between the observation and forward modeling only provides one of the best-fit models, which is usually band-limited. A complete solution should include both models and their uncertainties, which requires drawing samples from the posterior distribution. A global optimization method called very fast simulated annealing (VFSA) is commonly used to approximate posterior distribution with fast convergence. Here I address some of the limitations of VFSA by developing a new stochastic inference method, named Greedy Annealed Importance Sampling (GAIS). GAIS combines VFSA with greedy importance sampling (GIS), which uses a greedy search in the important regions located by VFSA to attain fast convergence and provide unbiased estimation. I demonstrate the performance of GAIS on post- and pre-stack data from real fields to estimate impedance models. The results indicate that GAIS can estimate both the expectation value and the uncertainties more accurately than using VFSA alone. Furthermore, principal component analysis (PCA) as an efficient parameterization method is employed together with GAIS to improve lateral continuity by simultaneous inversion of all traces. The second part of my research involves estimation of reservoir permeability models and their uncertainties using quantitative joint inversion of dynamic measurements, including synthetic production data and time-lapse seismic related data. Impacts from different objective functions or different data sets on the model uncertainty and model predictability are investigated as well. The results demonstrate that joint inversion of production data and time-lapse seismic related data (water saturation maps here) reduces model uncertainty, improves model predictability and shows superior performance than inversion using one type of data alone.
Download or read book Unconventional Reservoir Parameter Estimation by Seismic Inversion and Machine Learning of the Bakken Formation North Dakota written by Jackson Ray Tomski and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research reported in this thesis focuses on the prediction of reservoir parameters and their uncertainties. The thesis comprises two studies. In the first part, I focus on quantitative and seismic interpretation problem, where I describe a workflow for estimation of porosity using the results from pre-stack seismic inversion. The second part focuses on the production problem, where I establish a relationship between completion parameters and production given a production dataset from the Bakken Formation. In the first study, I characterize the unconventional reservoir of the Bakken Formation, specifically within northwest North Dakota using 3D seismic and well log data. I employ seismic inversion followed by application of a Bayesian Neural Network to predict total porosity across the entire seismic volume given an estimated volume of P-impedance. The Bayesian Neural Network utilizes Markov Chain Monte Carlo via Langevin Dynamics in order to sample from the probability distribution and to estimate uncertainity. This method establishes a good correlation between estimated P-impedance from seismic inversion and total porosity from well data. By integrating these techniques, a better understanding of the parameters useful for reservoir characterization is possible given a degree of uncertainity thereby improving oil and gas exploration and risk assessment. In this second study, I make use of a production dataset of the Bakken Formation to identify production patterns in the field to establish a relationship between completion parameters and production. A random forest model is employed alongside the Bayesian Neural Network model to predict production given a set of predictive features found through a series of feature selection methods. I then aim to create various training and testing dataset scenarios through random sampling and clustering. I do this in order to reduce the sampling bias and ensure that the machine learning models are being trained and tested on data coming from similar geological regions with similar production rate values. With the integration of these techniques, a better understanding of the parameters useful for optimizing oil production is possible with a degree of uncertainity when using the Bayesian Neural Network
Download or read book Reservoir Characterization and History Matching with Uncertainty Quantification Using Ensemble based Data Assimilation with Data Re parameterization written by Mingliang Liu and published by . This book was released on 2021 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book A Practical Guide to Seismic Reservoir Characterization written by Timothy Tylor-Jones and published by Springer Nature. This book was released on 2023-01-11 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers in detail the entire workflow for quantitative seismic interpretation of subsurface modeling and characterization. It focusses on each step of the geo-modeling workflow starting from data preconditioning and wavelet extraction, which is the basis for the reservoir geophysics described and introduced in the following chapters. This book allows the reader to get a comprehensive insight of the most common and advanced workflows. It aims at graduate students related to energy (hydrocarbons), CO2 geological storage, and near surface characterization as well as professionals in these industries. The reader benefits from the strong and coherent theoretical background of the book, which is accompanied with real case examples.
Download or read book Bayesian Inversion Methods for Seismic Reservoir Characterization and Time lapse Studies written by Dario Grana and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses mathematical methodologies for seismic reservoir characterization and time-lapse studies. Generally the main goal of reservoir modeling is to provide 3-dimensional models of the main properties in the reservoir in order to perform fluid flow simulations. These properties generally include rock properties, such as porosity and lithology; fluid properties, such as water and hydrocarbon saturations; and dynamic properties, such as pressure and permeability. None of these properties can be directly measured in the subsurface, therefore reservoir properties must be estimated from other measurements. In petroleum geophysics we generally have two kinds of measured data: well log data and seismic data. Well log data contain high resolution information about elastic and petrophysical properties, but they can only sample few locations of the reservoir. On the other side, seismic data cover the whole reservoir but the resolution is lower than well log data. Electromagnetic data are sometimes acquired in addition to seismic data to improve the reservoir description but the resolution is still limited. In order to obtain suitable models of the reservoir, we have to combine these two sources of information, wells and seismic, and integrate physical relations (rock physics and seismic modeling) with mathematical methodologies (inverse theory and probability and statistics). In particular by using a Bayesian approach to seismic and rock physics inversion we aim to obtain reservoir models of rock and fluid properties and the associated uncertainty. Since the resolution and the quality of seismic data are generally not ideal, uncertainty quantification plays a key role in reservoir modeling. This thesis includes three innovative methodologies for seismic reservoir characterization: the first method is a Bayesian inversion methodology suitable for reservoirs in exploration phases with a limited number of wells, the second method is a Bayesian sampling methodology that can provide multiple reservoir models honoring the given seismic dataset, the third one is a stochastic inversion methodology that provides high-detailed models suitable for reservoirs with a large number of wells. The key innovation in all these methods is the use of new statistical tools to describe the multimodal behavior of rock and properties in the reservoir and the direct integration of the rock physics model. The main principle of these methodologies is then extended to time-lapse studies to invert time-lapse seismic data and improve the reservoir description in terms of changes in rock and dynamic properties. The novelty of this method is the simultaneous inversion of the pre-production base seismic survey and repeated monitor surveys. This dissertation contributes to both deterministic and statistical seismic-based reservoir characterization. Complementary, I investigated velocity-pressure transforms to determine analytical physical models to describe the pressure effect on elastic properties and integrate these models in time-lapse reservoir studies. Finally I also developed a statistical methodology to integrate rock physics models in formation evaluation analysis and log-facies classification. All the proposed probabilistic reservoir-characterization techniques can predict reservoir models with multiple properties (static and dynamic) and the associated uncertainty. Multiple models can then be derived to run multiple scenarios and the corresponding risk analysis. All the methodologies were tested using synthetic data and applied to real case datasets. In the future, these methodologies could be integrated with history matching techniques to develop statistical methodologies for seismic history matching and improve reservoir description and monitoring by simultaneously matching seismic data and production data.
Download or read book INTEGRATED APPROACH FOR THE PETROPHYSICAL INTERPRETATION OF POST AND PRE STACK 3 D SEISMIC DATA WELL LOG DATA CORE DATA GEOLOGICAL INFORMATION AND RESERVOIR PRODUCTION DATA VIA BAYESIAN STOCHASTIC INVERSION written by and published by . This book was released on 2004 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present report summarizes the work carried out between September 30, 2002 and August 30, 2003 under DOE research contract No. DE-FC26-00BC15305. During the third year of work for this project we focused primarily on improving the efficiency of inversion algorithms and on developing algorithms for direct estimation of petrophysical parameters. The full waveform inversion algorithm for elastic property estimation was tested rigorously on a personal computer cluster. For sixteen nodes on the cluster the parallel algorithm was found to be scalable with a near linear speedup. This enabled us to invert a 2D seismic line in less than five hours of CPU time. We were invited to write a paper on our results that was subsequently accepted for publication. We also carried out a rigorous study to examine the sensitivity and resolution of seismic data to petrophysical parameters. In other words, we developed a full waveform inversion algorithm that estimates petrophysical parameters such as porosity and saturation from pre-stack seismic waveform data. First we used a modified Biot-Gassmann equation to relate petrophysical parameters to elastic parameters. The transformation was validated with a suite of well logs acquired in the deepwater Gulf of Mexico. As a part of this study, we carried out a sensitivity analysis and found that the porosity is very well resolved while the fluid saturation remains insensitive to seismic wave amplitudes. Finally we conducted a joint inversion of pre-stack seismic waveform and production history data. To overcome the computational difficulties we used a simpler waveform modeling algorithm together with an efficient subspace approach. The algorithm was tested on a realistic synthetic data set. We observed that the use of pre-stack seismic data helps tremendously to improve horizontal resolution of porosity maps. Finally, we submitted four publications to refereed technical journals, two refereed extended abstracts to technical conferences, and delivered two oral presentation at a technical forum. All of these publications and presentations stemmed from work directly related to the goals of our DOE project.
Download or read book Comparison of Post stack Seismic Inversion Methods written by Ercan Arabaci and published by . This book was released on 2013 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Seismic Inversion Methods written by Brian H. Russell and published by SEG Books. This book was released on 1988 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the current techniques used in the inversion of seismic data is provided. Inversion is defined as mapping the physical structure and properties of the subsurface of the earth using measurements made on the surface, creating a model of the earth using seismic data as input.
Download or read book STOCHASTIC INVERSION INTEGRATING SEISMIC DATA LITHO FACIES PHYSICAL PROPERTIES AND MULTIPLE POINT GEOSTATISTICS FOR RESERVOIR CHARACTERIZATION written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : We proposed a novel seismic inversion approach that integrates the physical properties of litho-facies, and geophysical data, within the multiple-point geostatistical frameworks to reduce the uncertainty in predictions of litho-facies spatial arrangement away from wells or control points. The litho-facies groups (rock-type) in the well locations are defined and conditioned to the distribution of elastic properties, including P-wave velocity (Vp) and facies density (rho) in the well locations. A conceptual geological model (training image) is utilized within a wavelet-based multiple-point geostatistical simulation (WAVESIM) algorithm to generate litho-facies realizations. In our inversion algorithm, the forward model is created by implementing the bivariate Kernel density estimation technique of the litho-facies properties (Vp and rho) that are distributed in the well locations. The inversion approach is an iterative process, where a particular number of elastic properties (Vp and rho) for each WAVESIM realization are drawn, and then the forward model was utilized to create synthetic seismograms. For each generated set of the WAVESIM realizations, a series of synthetic seismograms are produced, and one realization is selected that provides the best-match synthetic seismogram compared to the input seismic data using crosscorrelation function. Our inversion technique was successfully applied to synthetic and field datasets. The results demonstrate the efficiency of our inversion approach to characterize highly heterogeneous reservoirs.
Download or read book Quantitative Seismic Interpretation written by Per Avseth and published by Cambridge University Press. This book was released on 2010-06-10 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Seismic Interpretation demonstrates how rock physics can be applied to predict reservoir parameters, such as lithologies and pore fluids, from seismically derived attributes. The authors provide an integrated methodology and practical tools for quantitative interpretation, uncertainty assessment, and characterization of subsurface reservoirs using well-log and seismic data. They illustrate the advantages of these new methodologies, while providing advice about limitations of the methods and traditional pitfalls. This book is aimed at graduate students, academics and industry professionals working in the areas of petroleum geoscience and exploration seismology. It will also interest environmental geophysicists seeking a quantitative subsurface characterization from shallow seismic data. The book includes problem sets and a case-study, for which seismic and well-log data, and MATLAB® codes are provided on a website (http://www.cambridge.org/9780521151351). These resources will allow readers to gain a hands-on understanding of the methodologies.
Download or read book Inversion of Prestack Seismic Data for Reservoir Characterization Offshore Andaman Sea Thailand written by Non Sanpairote and published by . This book was released on 2014 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are several methods for determining and predicting the fluid content of the petroleum reservoir rocks such as seismic inversion and Amplitude Versus Offset (AVO) analysis. This project presents one of seismic inversion methods called Simultaneous Inversion. The Simultaneous Inversion is applied to seismic data obtained from offshore Andaman Sea, Thailand, where has been identified as a gas sweet spot. This approach transforms seismic reflection data into elastic properties and quantitatively distinguishes these properties into two rock units--shale and sandstone. In addition, inversion results are compared with observations from previous AVO analysis study to confirm the distribution of the gas reservoir model. In conclusion, this project suggests that the seismic inversion is a more suitable method to apply to seismic data from offshore Andaman Sea.
Download or read book Seismic Reservoir Modeling written by Dario Grana and published by John Wiley & Sons. This book was released on 2021-05-04 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Seismic Amplitude written by Rob Simm and published by Cambridge University Press. This book was released on 2014-04-17 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces practical seismic analysis techniques and evaluation of interpretation confidence, for graduate students and industry professionals - independent of commercial software products.
Download or read book Stochastic Reservoir Characterization Constrained by Seismic Data written by Alfhild Lien Eide and published by . This book was released on 1999 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Inversion of Seismic Data by Implementing Image Quilting to Build a Litho facies Model for Reservoir Characterization of Delhi Field LA written by Mitra Azizian and published by . This book was released on 2018 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:
