scholarly journals Prestack Seismic Inversion via Nonconvex L1-2 Regularization

2021 ◽  
Vol 11 (24) ◽  
pp. 12015
Author(s):  
Wenliang Nie ◽  
Fei Xiang ◽  
Bo Li ◽  
Xiaotao Wen ◽  
Xiangfei Nie
Keyword(s):  
Objective Function ◽  
Regularization Method ◽  
Oil And Gas ◽  
A Priori ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Data Logging ◽  
Elastic Parameters ◽  
Geological Interpretation

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L1−2 regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L1−2 regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L1−2 regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs.

scholarly journals Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

2020 ◽  
Vol 25 (1) ◽  
pp. 89-100
Author(s):  
Lin Zhou ◽  
Jianping Liao ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Tianchun Yang ◽  
...  
Keyword(s):  
Wave Equations ◽  
Oil And Gas ◽  
Time Lapse ◽  
Seismic Inversion ◽  
Field Application ◽  
Inversion Method ◽  
S Wave ◽  
Elastic Parameters ◽  
Zoeppritz Equations ◽  
Approximate Formulas

Accurately inverting changes in the reservoir elastic parameters that are caused by oil and gas exploitation is of great importance in accurately describing reservoir dynamics and enhancing recovery. Previously numerous time-lapse seismic inversion methods based on the approximate formulas of exact Zoeppritz equations or wave equations have been used to estimate these changes. However the low accuracy of calculations using approximate formulas and the significant calculation effort for the wave equations seriously limits the field application of these methods. However, these limitations can be overcome by using exact Zoeppritz equations. Therefore, we study the time-lapse seismic difference inversion method using the exact Zoeppritz equations. Firstly, the forward equation of time-lapse seismic difference data is derived based on the exact Zoeppritz equations. Secondly, the objective function based on Bayesian inversion theory is constructed using this equation, with the changes in elastic parameters assumed to obey a Gaussian distribution. In order to capture the sharp time-lapse changes of elastic parameters and further enhance the resolution of the inversion results, the blockiness constraint, which follows the differentiable Laplace distribution, is added to the prior Gaussian background model. All examples of its application show that the proposed method can obtain stable and reasonable P- and S-wave velocities and density changes from the difference data. The accuracy of estimation is higher than for existing methods, which verifies the effectiveness and feasibility of the new method. It can provide high-quality seismic inversion results for dynamic detailed reservoir description and well location during development.

Source-independent extended waveform inversion based on space-time source extension: Frequency-domain implementation

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. R449-R461 ◽  
Author(s):  
Guanghui Huang ◽  
Rami Nammour ◽  
William W. Symes
Keyword(s):  
Source Function ◽  
Random Noise ◽  
A Priori ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Inversion Method ◽  
Target Velocity ◽  
Extended Source ◽  
Full Waveform ◽  
Time Variables

Source signature estimation from seismic data is a crucial ingredient for successful application of seismic migration and full-waveform inversion (FWI). If the starting velocity deviates from the target velocity, FWI method with on-the-fly source estimation may fail due to the cycle-skipping problem. We have developed a source-based extended waveform inversion method, by introducing additional parameters in the source function, to solve the FWI problem without the source signature as a priori. Specifically, we allow the point source function to be dependent on spatial and time variables. In this way, we can easily construct an extended source function to fit the recorded data by solving a source matching subproblem; hence, it is less prone to cycle skipping. A novel source focusing annihilator, defined as the distance function from the real source position, is used for penalizing the defocused energy in the extended source function. A close data fit avoiding the cycle-skipping problem effectively makes the new method less likely to suffer from local minima, which does not require extreme low-frequency signals in the data. Numerical experiments confirm that our method can mitigate cycle skipping in FWI and is robust against random noise.

Accuracy of wavelets, seismic inversion, and thin-bed resolution

2017 ◽  
Vol 5 (4) ◽  
pp. T523-T530
Author(s):  
Ehsan Zabihi Naeini ◽  
Mark Sams
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Frequency Content ◽  
Frequency Model ◽  
North West ◽  
The North ◽  
Well Data

Broadband reprocessed seismic data from the North West Shelf of Australia were inverted using wavelets estimated with a conventional approach. The inversion method applied was a facies-based inversion, in which the low-frequency model is a product of the inversion process itself, constrained by facies-dependent input trends, the resultant facies distribution, and the match to the seismic. The results identified the presence of a gas reservoir that had recently been confirmed through drilling. The reservoir is thin, with up to 15 ms of maximum thickness. The bandwidth of the seismic data is approximately 5–70 Hz, and the well data used to extract the wavelet used in the inversion are only 400 ms long. As such, there was little control on the lowest frequencies of the wavelet. Different wavelets were subsequently estimated using a variety of new techniques that attempt to address the limitations of short well-log segments and low-frequency seismic. The revised inversion showed greater gas-sand continuity and an extension of the reservoir at one flank. Noise-free synthetic examples indicate that thin-bed delineation can depend on the accuracy of the low-frequency content of the wavelets used for inversion. Underestimation of the low-frequency contents can result in missing thin beds, whereas underestimation of high frequencies can introduce false thin beds. Therefore, it is very important to correctly capture the full frequency content of the seismic data in terms of the amplitude and phase spectra of the estimated wavelets, which subsequently leads to a more accurate thin-bed reservoir characterization through inversion.

scholarly journals A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 299
Author(s):  
Zhihong Wang ◽  
Tiansheng Chen ◽  
Xun Hu ◽  
Lixin Wang ◽  
Yanshu Yin
Keyword(s):  
Seismic Inversion ◽  
Western China ◽  
Inversion Method ◽  
Seismic Record ◽  
Elastic Parameters ◽  
Probability Updating ◽  
Geostatistical Inversion ◽  
Local Probability ◽  
Seismic Records ◽  
The Given

In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development.

scholarly journals Seismic pre-stack AVA inversion scheme based on lithology constraints

2020 ◽  
Vol 17 (3) ◽  
pp. 411-428
Author(s):  
Shuang Xiao ◽  
Jing Ba ◽  
Qiang Guo ◽  
J M Carcione ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Model Updating ◽  
Optimal Solution ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Good Resolution ◽  
S Wave ◽  
Elastic Parameters ◽  
Multivariate Gaussian Distribution ◽  
Stability Performance ◽  
The Stability

Abstract Seismic pre-stack AVA inversion using the Zoeppritz equation and its approximations as a forward engine yields P- and S-wave velocities and density. Due to the presence of seismic noise and other factors, the solution to seismic inversion is generally ill-posed and it is necessary to add constraints to regularize the algorithm. Moreover, since pre-stack inversion is a nonlinear problem, linearized optimization algorithms may fall into false local minima. The simulated annealing (SA) algorithm, on the other hand, is capable of finding the global optimal solution regardless of the initial model. However, when applied to multi-parameter pre-stack inversion, standard SA suffers from instability. Thus, a nonlinear pre-stack inversion method is proposed based on lithology constraints. Specifically, correlations among the elastic parameters are introduced to establish constraints based on a Bayesian framework, with special intention of mitigating the ill-posedness of the inversion problem as well as addressing the lithological characteristics of the formations. In particular, to improve the stability, a multivariate Gaussian distribution of elastic parameters is incorporated into the model updating the SA algorithm. We apply the algorithm to synthetic and field seismic data, indicating that the proposed method has a good resolution and stability performance.

L1–2 minimization for P- and S-impedance inversion

2020 ◽  
Vol 8 (2) ◽  
pp. T379-T390
Author(s):  
Wenliang Nie ◽  
Xiaotao Wen ◽  
Jixin Yang ◽  
Jian He ◽  
Kai Lin ◽  
...  
Keyword(s):  
Objective Function ◽  
Reservoir Characterization ◽  
A Priori ◽  
Low Frequency ◽  
Superior Performance ◽  
Minimization Method ◽  
Block Boundary ◽  
Avo Inversion ◽  
Impedance Inversion ◽  
The Difference

Amplitude variation with offset (AVO) inversion has been widely used in reservoir characterization to predict lithology and fluids. However, some existing AVO inversion methods that use [Formula: see text] norm regularization may not obtain the block boundary of subsurface layers because the AVO inversion is a severely ill-posed problem. To obtain sparse and accurate solutions, we have introduced the [Formula: see text] minimization method as an alternative to [Formula: see text] norm regularization. We used [Formula: see text] minimization for simultaneous P- and S-impedance inversion from prestack seismic data. We first derived the forward problem with multiangles and set up the inversion objective function with constraints of a priori low-frequency information obtained from well-log data. Then, we introduced minimization of the difference of [Formula: see text] and [Formula: see text] norms, denoted as [Formula: see text] minimization, to solve this objective function. The nonconvex penalty function of the [Formula: see text] minimization method is decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem is solved by the alternating direction method of multipliers. Compared to [Formula: see text] norm regularization, the results indicate that [Formula: see text] minimization has superior performance over [Formula: see text] norm regularization in promoting blocky/sparse solutions. Tests on synthetic and field data indicate that our method can provide sparser and more accurate P- and S-impedance inversion results. The overall results confirm that our method has great potential in the detection and identification of fluids.

Inversion of low to medium frequency velocities and densities from AVO data using invertible neural networks

Geophysics ◽  
2022 ◽  
pp. 1-44
Author(s):  
Yuhang Sun ◽  
Yang Liu ◽  
Mi Zhang ◽  
Haoran Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Latent Variables ◽  
Low Frequency ◽  
Inversion Method ◽  
Elastic Parameters ◽  
Medium Frequency ◽  
Uniqueness Of Solutions ◽  
Avo Inversion ◽  
Training Samples

AVO (amplitude variation with offset) inversion and neural networks are widely used to invert elastic parameters. With more constraints from well log data, neural network-based inversion may estimate elastic parameters with greater precision and resolution than traditional AVO inversion, however, neural network approaches necessitate a massive number of reliable training samples. Furthermore, because the lack of low-frequency information in seismic gathers leads to multiple solutions of the inverse problem, both inversions rely heavily on proper low-frequency initial models. To mitigate the dependence of inversions on accurate training samples and initial models, we propose solving inverse problems with the recently developed invertible neural networks (INNs). Unlike conventional neural networks, which address the ambiguous inverse issues directly, INNs learn definite forward modeling and use additional latent variables to increase the uniqueness of solutions. Motivated by the newly developed neural networks, we propose an INN-based AVO inversion method, which can reliably invert low to medium frequency velocities and densities with randomly generated easy-to-access datasets rather than trustworthy training samples or well-prepared initial models. Tests on synthetic and field data show that our method is feasible, anti-noise capable, and practicable.

scholarly journals Assessment of updated low frequency model in delineating bypassed hydrocarbon reservoir: A 4D seismic study of ’X’ - field, offshore, Niger Delta, Nigeria

2020 ◽  
Vol 13 (36) ◽  
pp. 3738-3753 ◽  
Author(s):  
Aniefiok Sylvester Akpan ◽  
Keyword(s):  
Acoustic Impedance ◽  
Deterministic Model ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Fluid Replacement ◽  
Inversion Method ◽  
Frequency Model ◽  
Hydrocarbon Reservoir ◽  
Model Based ◽  
4D Seismic

Aim/objectives: The aim of this research is, to use Time lapse (4D) seismic and investigate the influence of low frequency update in deterministic model-based seismic inversion employed in delineating a prospect saturated with bypassed hydrocarbon accumulation. Method: The dataset employed in this study incorporates 4D seismic volumes with fifteen (15) years production, interval between 2001 baseline and 2016 monitor seismic vintages. The inversion was carried out using full bandwidth of the updated low frequency and bandpass filtered low frequency approaches. The seismic vintages (baseline and monitor) were simultaneously inverted into acoustic impedance volumes for the two approaches. The formation fluid and lithology were discriminated through fluid replacement modelling (FRM) based on the colour separation between brine and gas saturation scenarios. Findings: The two inversion methods employed reveal six (6) zones suspected to be saturated with bypassed hydrocarbons. The delineated bypassed zones are masked in the full bandwidth approach,depicting the effect of the updated low frequency model. Meanwhile, the bandpass filtered approach result presents a better delineated bypassed reservoir as the zones are more pronounced when compared with the full bandwidth approach. Porosity estimate reveals that the bandpass filtered approach is characterized with excellent porosity in the suspected bypassed zones. The results equally gave more reliable and full delineated bypassed zones. Originality and novelty: The dataset employed in this study were obtained from a producing hydrocarbon field which, interest is to maximize the production of oil/gas. The study will bridge the inherent gab observed in using model-based seismic inversion approach to analyse and interpret seismic data in order to delineate hydrocarbon prospects. The research reveals that,the model-based seismic inversion method is still very effective in delineating hydrocarbon prospect when the updated low frequency is bandpass filtered to remove the model effect which influences the inverted acoustic impedance results. Keywords: Porosity; frequency; bypassed; reservoir and impedance

scholarly journals Simultaneous Seismic Inversion for Reservoir Characterization at Poseidon Field, Browse Basin, Australia

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Suleman Mauritz Sihotang ◽  
Ida Herawati
Keyword(s):  
Oil And Gas ◽  
Reservoir Characterization ◽  
Research Area ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Well Log ◽  
Gas Field ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Oil And Gas Field

Seismic inversion method has been widely used to obtain reservoir property in an oil and gas field. In this research, one of inversion methods known as simultaneous inversion is used to analyze reservoir characterization at Poseidon Field, Browse Basin. Simultaneous inversion is applied to partial angle stack data and result in volume of Acoustic Impedance (AI), Shear Impedance (SI) and Lame parameter (LMR). The objective of this study is to determine distribution of sandstone lithology with gas saturated in Plover reservoir formation. Sensitivity analysis is done by cross-plotting elastic and Lame parameter from five well log data and analyzing lithology type and fluid saturation. Based on those cross-plots, lithological type can be identified from AI, λρ, µρ and λ/µ parameters. Meanwhile, the presence of gas can be discriminated using SI, λρ, and λ/µ parameters. Gas-saturated sandstone presence is characterized by Lambda-Rho value less than 50 GPa g cc-1 and Lambda over Mu value less than 0.8 GPa g cc-1. Maps of each parameter are generated at reservoir interval. Based on those maps, it can be concluded that gas sand spread out in the eastern and western areas of research area.

scholarly journals THE IMPORTANCE OF LOCALLY CONVERTED WAVES ON THE ELASTIC INVERSION OF COMPRESSIONAL DATA

2014 ◽  
Vol 32 (2) ◽  
pp. 323 ◽  
Author(s):  
Jorge Nicolás Hounie ◽  
Sérgio Adriano Moura Oliveira
Keyword(s):  
Elastic Properties ◽  
Seismic Waves ◽  
Oil And Gas ◽  
Seismic Inversion ◽  
Stratified Medium ◽  
Inversion Method ◽  
Elastic Media ◽  
Nonlinear Inversion ◽  
Converted Waves ◽  
Elastic Inversion

ABSTRACT. Seismic inversion is routinely used in oil and gas exploration to estimate the elastic properties of the subsurface. However, most of the inversion methodsused in the industry disregard an inherent phenomenon of wave propagation in elastic media: the conversion of compressional waves into shear waves and vice versa.In this paper we analyze the importance of the locally converted seismic waves in the results of compressional wave based elastic inversion. For this, the reflectivitymethod is used to model the seismic response of a layered elastic media and also as the base of a nonlinear inversion method. We show that the compressional wavesgenerated by local conversion can hardly be identified and eliminated by moveout filters once their transit time are very close to that of primary reflections. To assess theimpact of the locally converted waves, two versions of the inversion method were implemented: in the first one, all seismic events generated in a stratified medium weretaken into account, including the effects of transmission, internal multiples and converted waves. In the second version, the converted waves were ignored. A series ofsynthetic data were generated using full reflectivity modeling and submitted to the two versions of the inversion methods, what allow us to evaluate the error made whenthese waves are ignored. We conclude that this error is proportional to the degree of contrast in elastic properties between layers and is greatly affected by the presenceof thin layers.Keywords: waveform inversion, reflectivity, seismic. RESUMO. A inversão de dados sísmicos é utilizada rotineiramente na exploração de hidrocarbonetos e na caracterização de reservatórios, com o objetivo de estimaras propriedades elásticas do interior da terra. No entanto, a maioria dos algoritmos de inversão elástica desconsidera um fenômeno inerente à propagação de ondas emmeios elásticos: a conversão de ondas compressionais em cisalhantes e vice-versa. Neste artigo analisamos o impacto das ondas convertidas localmente nos resultadosde inversão elástica de dados compressionais. O método da refletividade é utilizado de duas maneiras na análise das ondas convertidas: como método de modelagem ecomo base para algoritmos de inversão. A modelagem mostra que as ondas convertidas localmente são de difícil identificação em um sismograma, confundindo-se comeventos de origem puramente compressional, o que torna inviável sua filtragem com os métodos de processamento baseados na diferença de tempo de trânsito entreeventos compressionais e convertidos. Para avaliar a influência das ondas convertidas nos resultados da inversão elástica de dados compressionais foram desenvolvidasduas estratégias de inversão baseadas no método da refletividade: na primeira, foram considerados todos os efeitos de propagação em meios elásticos estratificados,enquanto na segunda foram desconsideradas as conversões de modo que ocorrem entre as camadas. As estratégias foram testadas em dados sintéticos, e os resultadospermitiram avaliar a influência das ondas convertidas na inversão elástica de dados compressionais. De uma maneira geral, a inversão de forma de onda considerandotodos os efeitos de propagação apresentou resultados superiores aos da inversão que desconsideram as conversões de modo. Entretanto, em situações onde não ocorram camadas delgadas com grandes contrastes nas propriedades em relação às camadas adjacentes, as duas estratégias apresentaram resultados similares.Palavras-chave: inversão de forma de onda, refletividade, sísmica.

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