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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Rock Physics-Based Carbonate Reservoir Pore Type Evaluation by Combining Geological, Petrophysical and Seismic Data

Dou, Qifeng 2011 May 1900 (has links)
Pore type variations account for complex velocity-porosity relationship and intensive permeability heterogeneity and consequently low oil and gas recovery in carbonate reservoir. However, it is a challenge for geologist and geophysicist to quantitatively estimate the influences of pore type complexity on velocity variation at a given porosity and porosity-permeability relationship. A new rock physics-based integrated approach in this study was proposed to quantitatively characterize the diversity of pore types and its influences on wave propagation in carbonate reservoir. Based on above knowledge, permeability prediction accuracy from petrophysical data can be improved compared to conventional approach. Two carbonate reservoirs with different reservoir features, one is a shallow carbonate reservoir with average high porosity (>10%) and another one is a supper-deep carbonate reservoir with average low porosity (<5%), are used to test the proposed approach. Paleokarst is a major event to complicate carbonate reservoir pore structure. Because of limited data and lack of appropriate study methods, it is a difficulty to characterize subsurface paleokarst 3D distribution and estimate its influences on reservoir heterogeneity. A method by integrated seismic characterization is applied to delineate a complex subsurface paleokarst system in the Upper San Andres Formation, Permian basin, West Texas. Meanwhile, the complex paleokarst system is explained by using a carbonate platform hydrological model, similar to modern marine hydrological environments within carbonate islands. How to evaluate carbonate reservoir permeability heterogeneity from 3D seismic data has been a dream for reservoir geoscientists, which is a key factor to optimize reservoir development strategy and enhance reservoir recovery. A two-step seismic inversions approach by integrating angle-stack seismic data and rock physics model is proposed to characterize pore-types complexity and further to identify the relative high permeability gas-bearing zones in low porosity reservoir (< 5%) using ChangXing super-deep carbonate reservoir as an example. Compared to the conventional permeability calculation method by best-fit function between porosity and permeability, the results in this study demonstrate that gas zones and non-gas zones in low porosity reservoir can be differentiated by using above integrated permeability characterization method.
12

Multiscale Seismic Inversion in the Data and Image Domains

Zhang, Sanzong 12 1900 (has links)
I present a general methodology for inverting seismic data in either the data or image domains. It partially overcomes one of the most serious problems with current waveform inversion methods, which is the tendency to converge to models far from the actual one. The key idea is to develop a multiscale misfit function that is composed of both a simplified version of the data and one associated with the complex part of the data. Misfit functions based on simple data are characterized by many fewer local minima so that a gradient optimization method can make quick progress in getting to the general vicinity of the actual model. Once we are near the actual model, we then use the gradient based on the more complex data. Below, we describe two implementations of this multiscale strategy: wave equation traveltime inversion in the data domain and generalized differential semblance optimization in the image domain. • Wave Equation Traveltime Inversion in the Data Domain (WT): The main difficulty with iterative waveform inversion is that it tends to get stuck in local minima associated with the waveform misfit function. To mitigate this problem and avoid the need to fit amplitudes in the data, we present a waveequation method that inverts the traveltimes of reflection events, and so is less prone to the local minima problem. Instead of a waveform misfit function, the penalty function is a crosscorrelation of the downgoing direct wave and the upgoing reflection wave at the trial image point. The time lag which maximizes the crosscorrelation amplitude represents the reflection-traveltime residual that is back-projected along the reflection wavepath to update the velocity. Shot- and angle-domain crosscorrelation functions are introduced to estimate the reflection-traveltime residual by semblance analysis and scanning. In theory, only the traveltime information is inverted and there is no need to precisely fit the amplitudes or assume a high-frequency approximation. Results with both synthetic data and field records reveal both the benefits and limitations of WT. • Generalized Differental Semblance Optimization in the Image Domain (GDSO): We now extend the multiscale physics approach to differential semblance optimization (DSO) in the image domain. That is, we identify the space-lag offset H(x, z, h) in the subsurface-offset domain as an implicit function of velocity. It describes the smoothly varying moveout H(x, z, h) of the migration image m(x, z, h) in the subsurface-offset domain, which is analogous to the smoothly varying traveltime residual ∆τ(x) of a reflection event in a shot gather. The velocity model is found that minimizes the objective function ∑x,z,h H(x, z, h)2m(x, z, h)2, where coherent noise is eliminated everywhere except along the picked curve H(x, z, h). This method is denoted as generalized DSO (GDSO) and mitigates the coherent noise problem with DSO. Numerical examples are presented that empirically demonstrate its effectiveness in providing more accurate velocity models compared to conventional DSO.
13

Enhanced Detection of Seismic Time-Lapse Changes with 4D Joint Seismic Inversion and Segmentation

Romero, Juan Daniel 04 1900 (has links)
Seismic inversion is the leading method to map and quantify changes in time-lapse (4D) seismic datasets, with applications ranging from monitoring hydrocarbon-producing fields to geological CO2 storage. However, the process of inverting seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. This comes with additional challenges for 4D applications, given the inaccuracies in the repeatability of time-lapse acquisition surveys. Consequently, adding prior information to the inversion process in the form of properly crafted regularization terms is essential to obtain geologically meaningful subsurface models and 4D effects. In this thesis, I propose a joint inversion-segmentation algorithm for 4D seismic inversion, which integrates total variation and segmentation priors as a way to counteract the missing frequencies and noise present in 4D seismic data. I validate the algorithm with synthetic and field seismic datasets and benchmark it against state-of-the-art 4D inversion techniques. The proposed algorithm shows three main advantages: 1. it produces high-resolution baseline and monitor acoustic impedance models, 2. by leveraging similarities between multiple seismic datasets, the proposed algorithm mitigates the non-repeatable noise and better highlights the real seismic time-lapse changes, and 3. it simultaneously provides a volumetric classification of the acoustic impedance 4D difference model based on user-defined classes, i.e., percentages of seismic time-lapse changes. Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations.
14

3D Post-stack Seismic Inversion using Global Optimization Techniques: Gulf of Mexico Example

Adedeji, Elijah A 10 August 2016 (has links)
Seismic inversion using a global optimization algorithm is a non-linear, model-driven process. It yields an optimal solution of the cost function – reflectivity/acoustic impedance, when prior information is sparse. The inversion result offers detailed interpretations of thin layers, internal stratigraphy, and lateral continuity and connectivity of sand bodies. This study compared two stable and robust global optimization techniques, Simulated Annealing (SA) and Basis Pursuit Inversion (BPI) as applied to post-stack seismic data from the Gulf of Mexico. Both methods use different routines and constraints to search for the minimum error energy function. Estimation of inversion parameters in SA is rigorous and more reliable because it depends on prior knowledge of subsurface geology. The BPI algorithm is a more robust deterministic process. It was developed as an alternative method to incorporating a priori information. Results for the Gulf of Mexico show that BPI gives a better stratigraphic and structural actualization due to its capacity to delineate layers thinner than the tuning thickness. The SA algorithm generates both absolute and relative impedances, which provide both qualitative and quantitative characterization of thin-bed reservoirs.
15

[en] DETERMINISTIC ACOUSTIC SEISMIC INVERSION USING ARTIFICIAL NEURAL NETWORKS / [pt] INVERSÃO SÍSMICA ACÚSTICA DETERMINÍSTICA UTILIZANDO REDES NEURAIS ARTIFICIAIS

MARCELO GOMES DE SOUZA 02 August 2018 (has links)
[pt] A inversão sísmica é o processo de transformar dados de Sísmica de Reflexão em valores quantitativos de propriedades petroelásticas das rochas. Esses valores, por sua vez, podem ser correlacionados com outras propriedades ajudando os geocientistas a fazer uma melhor interpretação que resulta numa boa caracterização de um reservatório de petróleo. Existem vários algoritmos tradicionais para Inversão Sísmica. Neste trabalho revisitamos a Inversão Colorida (Impedância Relativa), a Inversão Recursiva, a Inversão Limitada em Banda e a Inversão Baseada em Modelos. Todos esses quatro algoritmos são baseados em processamento digital de sinais e otimização. O presente trabalho busca reproduzir os resultados desses algoritmos através de uma metodologia simples e eficiente baseada em Redes Neurais e na pseudo-impedância. Este trabalho apresenta uma implementação dos algoritmos propostos na metodologia e testa sua validade num dado sísmico público que tem uma inversão feita pelos métodos tradicionais. / [en] Seismic inversion is the process of transforming Reflection Seismic data into quantitative values of petroleum rock properties. These values, in turn, can be correlated with other properties helping geoscientists to make a better interpretation that results in a good characterization of an oil reservoir.There are several traditional algorithms for Seismic Inversion. In this work we revise Color Inversion (Relative Impedance), Recursive Inversion, Bandwidth Inversion and Model-Based Inversion. All four of these algorithms are based on digital signal processing and optimization. The present work seeks to reproduce the results of these algorithms through a simple and efficient methodology based on Neural Networks and pseudo-impedance. This work presents an implementation of the algorithms proposed in the methodology and tests its validity in a public seismic data that has an inversion made by the traditional methods.
16

Pre-Stack Seismic Inversion and Amplitude Variation with Offset (AVO) Attributes as Hydrocarbon Indicators in Carbonate Rocks: A Case Study from the Illinois Basin

Murchek, Jacob T. 11 May 2021 (has links)
No description available.
17

[en] DEEP PHYSICS-DRIVEN STOCHASTIC SEISMIC INVERSION / [pt] INVERSÃO SÍSMICA ESTOCÁSTICA COM APRENDIZADO PROFUNDO ORIENTADO À FÍSICA

PAULA YAMADA BURKLE 28 August 2023 (has links)
[pt] A inversão sísmica é uma etapa essencial na modelagem e caracterização de reservatórios que permite a estimativa de propriedades da subsuperfície a partir dos dados de reflexão sísmica. os métodos convencionais usualmente possuem um alto custo computacional ou apresentam problemas relativos à não-linearidade e à forte ambiguidade do modelo de inversão sísmica. Recentemente, com a generalizaçãodo aprendizado de máquina na geofísica, novos métodos de inversão sísmica surgiram baseados nas técnicas de aprendizado profundo. Entretanto, a aplicação prática desses métodos é limitada devido a ausência de uma abordagem probabilística capaz de lidar com as incertezas inerentes ao problema da inversão sísmica e/ou a necessidade de dados de treinamento completos e representativos. Para superar essas limitações, um novo método é proposto para inverter dados de reflexão sísmica diretamente para modelos da subsuperfície de alta resolução. O método proposto explora a capacidade das redes neurais convolucionais em extrair representações significativas e complexas de dados espacialmente estruturados, combinada à simulação estocástica geoestatística. Em abordagem auto-supervisionada, modelos físicos são incorporados no sistema de inversão com o objetivo de potencializar o uso das medições indiretas e imprecisas, mas amplamente distribuídas do método sísmico. As realizações geradas com simulação geoestatística fornecem informações adicionais com maior resolução espacial do que a originalmente encontrada nos dados sísmicos. Quando utilizadas como entrada do sistema de inversão, elas permitem a geração de múltiplos modelos alternativos da subsuperfície. Em resumo, o método proposto é capaz de: (1) quantificar as incertezas das previsões, (2) modelar a relação complexa e não-linear entre os dados observados e o modelo da subsuperfície, (3) estender a largura de banda sísmica nas extremidades baixa e alta do espectro de parâmetros de frequência, e (4) diminuir a necessidade de dados de treinamento anotados. A metodologia proposta é inicialmente descrita no domínio acústico para inverter modelos de impedância acústica a partir de dados sísmicos pós-empilhados. Em seguida, a metodologia é generalizada para o domínio elástico para inverter a partir de dados sísmicos pré-empilhados modelos de velocidade da onda P, da velocidade da onda S e de densidade. Em seguida, a metodologia proposta é estendida para a inversão sísmica petrofísica em um fluxo de trabalho simultâneo. O método foi validado em um caso sintético e aplicado com sucesso a um caso tridimensional de um reservatório brasileiro real. Os modelos invertidos são comparados àqueles obtidos a partir de uma inversão sísmica geoestatística iterativa. A metodologia proposta permite obter modelos similares, mas tem a vantagem de gerar soluções alternativas em maior número, permitindo explorar de forma mais efetiva o espaço de parâmetros do modelo quando comparada à inversão sísmica geoestatística iterativa. / [en] Seismic inversion allows the prediction of subsurface properties from seismic reflection data and is a key step in reservoir modeling and characterization. Traditional seismic inversion methods usually come with a high computational cost or suffer from issues concerning the non-linearity and the strong non-uniqueness of the seismic inversion model. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of them lack a probabilistic approach to deal with the uncertainties inherent in the seismic inversion problems and/or rely on complete and representative training data, which is often scarcely available. To overcome these limitations, we introduce a novel seismic inversion method that explores the ability of deep convolutional neural networks to extract meaningful and complex representations from spatially structured data, combined with geostatistical stochastic simulation to efficiently invert seismicn reflection data directly for high-resolution subsurface models. Our method incorporates physics constraints, sparse direct measurements, and leverages the use of imprecise but widely distributed indirect measurements as represented by the seismic data. The geostatistical realizations provide additional information with higher spatial resolution than the original seismic data. When used as input to our inversion system, they allow the generation of multiple possible outcomes for the uncertain model. Our approach is fully unsupervised, as it does not depend on ground truth input-output pairs. In summary, the proposed method is able to: (1) provide uncertainty assessment of the predictions, (2) model the complex non-linear relationship between observed data and model, (3) extend the seismic bandwidth at both low and high ends of the frequency parameters spectrum, and (4) lessen the need for large, annotated training data. The proposed methodology is first described in the acoustic domain to invert acoustic impedance models from full-stack seismic data. Next, it is generalized for the elastic domain to invert P-wave velocity, S-wave velocity and density models from pre-stack seismic data. Finally, we show that the proposed methodology can be further extended to perform petrophysical seismic inversion in a simultaneous workflow. The method was tested on a synthetic case and successfully applied to a real three-dimensional case from a Brazilian reservoir. The inverted models are compared to those obtained from a full iterative geostatistical seismic inversion. The proposed methodology allows retrieving similar models but has the advantage of generating alternative solutions in greater numbers, providing a larger exploration of the model parameter space in less time than the geostatistical seismic inversion.
18

Full-waveform inversion in three-dimensional PML-truncated elastic media : theory, computations, and field experiments

Fathi, Arash 03 September 2015 (has links)
We are concerned with the high-fidelity subsurface imaging of the soil, which commonly arises in geotechnical site characterization and geophysical explorations. Specifically, we attempt to image the spatial distribution of the Lame parameters in semi-infinite, three-dimensional, arbitrarily heterogeneous formations, using surficial measurements of the soil's response to probing elastic waves. We use the complete waveforms of the medium's response to drive the inverse problem. Specifically, we use a partial-differential-equation (PDE)-constrained optimization approach, directly in the time-domain, to minimize the misfit between the observed response of the medium at select measurement locations, and a computed response corresponding to a trial distribution of the Lame parameters. We discuss strategies that lend algorithmic robustness to the proposed inversion schemes. To limit the computational domain to the size of interest, we employ perfectly-matched-layers (PMLs). The PML is a buffer zone that surrounds the domain of interest, and enforces the decay of outgoing waves. In order to resolve the forward problem, we present a hybrid finite element approach, where a displacement-stress formulation for the PML is coupled to a standard displacement-only formulation for the interior domain, thus leading to a computationally cost-efficient scheme. We discuss several time-integration schemes, including an explicit Runge-Kutta scheme, which is well-suited for large-scale problems on parallel computers. We report numerical results demonstrating stability and efficacy of the forward wave solver, and also provide examples attesting to the successful reconstruction of the two Lame parameters for both smooth and sharp profiles, using synthetic records. We also report the details of two field experiments, whose records we subsequently used to drive the developed inversion algorithms in order to characterize the sites where the field experiments took place. We contrast the full-waveform-based inverted site profile against a profile obtained using the Spectral-Analysis-of-Surface-Waves (SASW) method, in an attempt to compare our methodology against a widely used concurrent inversion approach. We also compare the inverted profiles, at select locations, with the results of independently performed, invasive, Cone Penetrometer Tests (CPTs). Overall, whether exercised by synthetic or by physical data, the full-waveform inversion method we discuss herein appears quite promising for the robust subsurface imaging of near-surface deposits in support of geotechnical site characterization investigations.
19

Feasibility of rock characterization for mineral exploration using seismic data

Harrison, Christopher Bernard January 2009 (has links)
The use of seismic methods in hard rock environments in Western Australia for mineral exploration is a new and burgeoning technology. Traditionally, mineral exploration has relied upon potential field methods and surface prospecting to reveal shallow targets for economic exploitation. These methods have been and will continue to be effective but lack lateral and depth resolution needed to image deeper mineral deposits for targeted mining. With global need for minerals, and gold in particular, increasing in demand, and with shallower targets harder to find, new methods to uncover deeper mineral reserves are needed. Seismic reflection imaging, hard rock borehole data analysis, seismic inversion and seismic attribute analysis all give the spatial and volumetric exploration techniques the mineral industry can use to reveal high value deeper mineral targets. / In 2002, two high resolution seismic lines, the East Victory and Intrepid, were acquired along with sonic logging, to assess the feasibility of seismic imaging and rock characterisation at the St. Ives gold camp in Western Australia. An innovative research project was undertaken combining seismic processing, rock characterization, reflection calibration, seismic inversion and seismic attribute analysis to show that volumetric predictions of rock type and gold-content may be viable in hard rock environments. Accurate seismic imaging and reflection identification proved to be challenging but achievable task in the all-out hard rock environment of the Yilgarn craton. Accurate results were confounded by crocked seismic line acquisition, low signal-to-noise ratio, regolith distortions, small elastic property variations in the rock, and a limited volume of sonic logging. Each of these challenges, however, did have a systematic solution which allowed for accurate results to be achieved. / Seismic imaging was successfully completed on both the East Victory and Intrepid data sets revealing complex structures in the Earth as shallow as 100 metres to as deep as 3000 metres. The successful imaging required homogenization of the regolith to eliminate regolith travel-time distortions and accurate constant velocity analysis for reflection focusing using migration. Verification of the high amplitude reflections within each image was achieved through integration of surface geological and underground mine data as well as calibration with log derived synthetic seismograms. The most accurate imaging results were ultimately achieved on the East Victory line which had good signal-to-noise ratio and close-to-straight data acquisition direction compared to the more crooked Intrepid seismic line. / The sonic logs from both the East Victory and Intrepid seismic lines were comprehensively analysed by re-sampling and separating the data based on rock type, structure type, alteration type, and Au assay. Cross plotting of the log data revealed statistically accurate separation between harder and softer rocks, as well as sheared and un-sheared rock, were possible based solely on compressional-wave, shear-wave, density, acoustic and elastic impedance. These results were used successfully to derive empirical relationships between seismic attributes and geology. Calibrations of the logs and seismic data provided proof that reflections, especially high-amplitude reflections, correlated well with certain rock properties as expected from the sonic data, including high gold content sheared zones. The correlation value, however, varied with signal-to-noise ratio and crookedness of the seismic line. Subsequent numerical modelling confirmed that separating soft from hard rocks can be based on both general reflectivity pattern and impedance contrasts. / Indeed impedance inversions on the calibrated seismic and sonic data produced reliable volumetric separations between harder rocks (basalt and dolerite) and softer rock (intermediate intrusive, mafic, and volcaniclastic). Acoustic impedance inversions produced the most statistically valid volumetric predictions with the simultaneous use of acoustic and elastic inversions producing stable separation of softer and harder rocks zones. Similarly, Lambda-Mu-Rho inversions showed good separations between softer and harder rock zones. With high gold content rock associated more with “softer” hard rocks and sheared zones, these volumetric inversion provide valuable information for targeted mining. The geostatistical method applied to attribute analysis, however, was highly ambiguous due to low correlations and thus produced overly generalized predictions. Overall reliability of the seismic inversion results were based on quality and quantity of sonic data leaving the East Victory data set, again with superior results as compared to the Intrepid data set. / In general, detailed processing and analysis of the 2D seismic data and the study of the relationship between the recorded wave-field and rock properties measured from borehole logs, core samples and open cut mining, revealed that positive correlations can be developed between the two. The results of rigorous research show that rock characterization using seismic methodology will greatly benefit the mineral industry.
20

[en] INVERSION OF PARAMETERS IN SEISMIC DATA BY GENETIC ALGORITHMS / [pt] INVERSÃO DE PARÂMETROS EM DADOS SÍSMICOS POR ALGORITMOS GENÉTICOS

SHELLY CRISTIANE DAVILA MEDEIROS 05 July 2006 (has links)
[pt] Esta dissertação investiga o uso de Algoritmos Genéticos aplicados em dados sísmicos com o objetivo de obter parâmetros físicos e atributos sísmicos que auxiliem na caracterização das rochas de um subsolo terrestre. Os dados sísmicos têm sido extensamente empregados no setor de exploração de petróleo. As aplicações envolvendo sísmica não se restringem na busca por novas reservas de petróleo, mas também são usadas para projetar novos poços e melhorar a produção dos reservatórios de petróleo. O levantamento de dados sísmicos permite analisar extensas áreas da subsuperfície com custo praticável em relação a outras técnicas. Entretanto, a interpretação desses dados com o objetivo de obter informações relevantes e acuradas não é uma tarefa simples. Para isto, várias técnicas de inversão sísmica vêm sendo desenvolvidas. Este trabalho consistiu em avaliar uma alternativa que emprega Algoritmos Genéticos para inverter parâmetros a partir de dados sísmicos. Existem 3 etapas principais neste trabalho. Primeiramente, foram estudados o tema da exploração sísmica e a técnica de Algoritmos Genéticos. Na segunda etapa foi definido um modelo, usando Algoritmos Genéticos, que busca, neste caso, minimizar uma medida de erro, para obtenção dos parâmetros objetivos. Finalmente, foi implementado um sistema a partir do modelo proposto e realizados os estudos de casos com dados sísmicos sintéticos para avaliar o seu desempenho. O modelo baseado em Algoritmos Genéticos foi avaliado submetendo-se seus resultados a um especialista e comparando-os com os da busca aleatória. Os resultados obtidos se mostraram consistentemente satisfatórios e sempre superiores aos da busca exaustiva. / [en] This dissertation investigates the use of Genetic Algorithms applied to seismic data with the objective of obtaining physical parameters and seismic attributes that would facilitate the characterization of rocks in terrestrial subsoil. The seismic data has been extensively utilized in the field of petroleum exploration. The applications involving seismic are not restrained to the search for new petroleum reserves, but are also used to project new wells and to improve the production of existing petroleum reservoirs. The survey of seismic data allows the analysis of extended areas of the subsurface at an affordable price relative to other techniques. However, the interpretation of the data with the objective of obtaining relevant and accurate information is not an easy task. For that, several seismic inversion techniques are being developed. This work consists in evaluating an alternative that uses Genetic Algorithms to invert parameters from seismic data. There are 3 main stages in this work. Initially, the theme of seismic exploration and the technique of Genetic Algorithms have been studied. On the second stage a model has been defined, using Genetic Algorithms, which aims, in this case, to minimize an error measurement, obtaining objective parameters. Finally, a system from the proposed model has been implanted and the study of cases with synthetic seismic data has been executed to evaluate its performance. The process of optimizing has been compared to the process of random search and the results obtained by the model have always been superior.

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