Pre-stack Data Research Articles

Fracture prediction using prestack Q calculation and attenuation anisotropy

The analysis of fractured reservoirs is very important to hydrocarbon exploration. The quality factor Q is a parameter used to characterize the attenuation of seismic waves in subsurface media. Q not only reflects the inherent properties of the medium but also is used to make predictions regarding reservoir fractures. Compared with poststack seismic data, prestack seismic data contain detailed stratigraphic information of seismic attributes and data inversion in reservoirs. The extraction of absorption parameters from prestack data improves the accuracy of attenuation estimates. In this study, I present a new method for calculating Q based on the modified S transform (MST) using common midpoint (CMP) preprocessed gathers. First, I use the MST with adjustable time–frequency resolution to carry out a high-precision time–frequency analysis of prestack CMP gathers and derive the calculation formula for the improved S transform-based frequency spectrum ratio method. Then, I use the energy density ratio to calculate the slope of the frequency spectrum ratio instead of the conventional amplitude ratio. Thus, I establish the relation between the slope of the spectrum ratio and offset as well as eliminate the offset effect by multichannel linear fitting, obtaining accurate Q values from seismic prestack data. Finally, I use the proposed prestack Q extraction method to study the fractured reservoir in Qianjin burried hill and P-wave absorption and attenuation anisotropy with good results in the fracture characterization.

Interpolation and denoising of high-dimensional seismic data by learning a tight frame

Sparse transforms play an important role in seismic signal processing steps, such as prestack noise attenuation and data reconstruction. Analytic sparse transforms (so-called implicit dictionaries), such as the Fourier, Radon, and curvelet transforms, are often used to represent seismic data. There are situations, however, in which the complexity of the data requires adaptive sparse transform methods, whose basis functions are determined via learning methods. We studied an application of the data-driven tight frame (DDTF) method to noise suppression and interpolation of high-dimensional seismic data. Rather than choosing a model beforehand (for example, a family of lines, parabolas, or curvelets) to fit the data, the DDTF derives the model from the data itself in an optimum manner. The process of estimating the basis function from the data can be summarized as follows: First, the input data are divided into small blocks to form training sets. Then, the DDTF algorithm is applied on the training sets to estimate the dictionary. The DDTF is typically embodied as an explicit dictionary, and a sparsity-promoting algorithm is used to obtain an optimized tight frame representation of the observed data. The computational time and redundancy is controlled by the block overlap of the training set. Finally, the learned dictionary is used to represent the observed data and to estimate data at unobserved spatial positions. Our numerical results showed that the proposed methodology is capable of recovering n-dimensional prestack seismic data under different signal-to-noise ratio scenarios. We determined that subtle features tend to be better preserved with the DDTF method in comparison with standard Fourier and directional transform reconstruction methods.

True amplitude extraction technology and its application in pre-stack seismic data

Common-reflection-point (CRP) gather is a bridge that connects seismic data and petrophysical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are important means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then suppresses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio (SNR).

Geofluid Discrimination Incorporating Poroelasticity and Seismic Reflection Inversion

Geofluid discrimination plays an important role in the fields of hydrogeology, geothermics, and exploration geophysics. A geofluid discrimination approach incorporating linearized poroelasticity theory and pre-stack seismic reflection inversion with Bayesian inference is proposed in this study to identify the types of geofluid underground. Upon the review of the development of different geofluid indicators, the fluid modulus is defined as the geofluid indicator mainly affected by the fluid contained in reservoirs. A novel linearized P-wave reflectivity equation coupling the fluid modulus is derived to avoid the complicated nonlinear relationship between the fluid modulus and seismic data. Model examples illustrate the accuracy of the proposed linearized P-wave reflectivity equation comparing to the exact P-wave reflectivity equation even at moderate incident angle, which satisfies the requirements of the parameter estimations with P-wave pre-stack seismic data. Convoluting this linearized P-wave reflectivity equation with seismic wavelets as the forward solver, a pragmatic pre-stack Bayesian seismic inversion method is presented to estimate the fluid modulus directly. Cauchy and Gaussian probability distributions are utilized for prior information of the model parameters and the likelihood function, respectively, to enhance the inversion resolution. The preconditioned conjugate gradient method is coupled in the optimization of the objective function to weaken the strong degree of correlation among the four model parameters and enhance the stability of those parameter estimations simultaneously. The synthetic examples demonstrate the feasibility and stability of the proposed novel seismic coefficient equation and inversion approach. The real data set illustrates the efficiency and success of the proposed approach in differentiating the geofluid filled reservoirs.

5D seismic data completion and denoising using a novel class of tensor decompositions

We have developed a novel strategy for simultaneous interpolation and denoising of prestack seismic data. Most seismic surveys fail to cover all possible source-receiver combinations, leading to missing data especially in the midpoint-offset domain. This undersampling can complicate certain data processing steps such as amplitude-variation-with-offset analysis and migration. Data interpolation can mitigate the impact of missing traces. We considered the prestack data as a 5D multidimensional array or otherwise referred to as a 5D tensor. Using synthetic data sets, we first found that prestack data can be well approximated by a low-rank tensor under a recently proposed framework for tensor singular value decomposition (tSVD). Under this low-rank assumption, we proposed a complexity-penalized algorithm for the recovery of missing traces and data denoising. In this algorithm, the complexity regularization was controlled by tuning a single regularization parameter using a statistical test. We tested the performance of the proposed algorithm on synthetic and real data to show that missing data can be reliably recovered under heavy downsampling. In addition, we demonstrated that compressibility, i.e., approximation of the data by a low-rank tensor, of seismic data under tSVD depended on the velocity model complexity and shot and receiver spacing. We further found that compressibility correlated with the recovery of missing data because high compressibility implied good recovery and vice versa.

Prestack time-migration velocity analysis using remigration trajectories

We have developed a prestack time-migration tool for local improvement of the seismic migration-velocity model. The method is based on remigration trajectories that describe the position of an image point in the image domain for different source-receiver offsets as a function of the migration velocity. It determines kinematic migration parameters using local-slope information of migrated seismic reflection events. These parameters, in turn, are used to locally correct the velocity model. The main advantage of this technique is that it allows us to carry out a moveout correction not just at a fixed point in a zero-offset (poststack) time-migrated gather, but varying through all offsets of a common-image gather, taking into account the reflection-point displacement in the midpoint direction. In other words, it provides for time-migration velocity analysis from prestack data. We have tested the feasibility of the method on synthetic data from four simple models and the Sigsbee2B data. Our tests determined that the proposed tool increases the velocity-model resolution and provides a plausible time-migrated image. The quality of the initial model is not critical. The procedure is quite efficient. Significant effort was spent on manual event picking.

Velocity analysis using similarity-weighted semblance

Weighted semblance can be used for improving the performance of traditional semblance for specific data sets. We have developed a novel approach for prestack velocity analysis using weighted semblance. The novelty came from a different weighting criteria in which the local similarity between each trace and a reference trace is used. On the one hand, low similarity corresponded to a noise point or a point indicating incorrect moveout, which should be given a small weight. On the other hand, high similarity corresponded to a point indicating correct moveout that should be given a high weight. Our approach could also be effectively used for analyzing AVO anomalies with increased resolution compared with AB semblance. Synthetic and field common-midpoint gathers demonstrated higher resolution using the approach we developed. Applications of the proposed method on a prestack data set further confirmed that the stacked data using the similarity-weighted semblance could obtain better energy-focused events, which indicated a more precise velocity picking.

Mapping of moveout attributes using local slopes

Decomposing seismic data in local slopes is the basic idea behind velocity-independent imaging. Using accurate moveout approximations enables computing moveout attributes such as normal moveout velocity and nonhyperbolic parameters as functions of zero-offset travel time. Mapping of moveout attributes is performed from the pre-stack seismic data domain into the time-migrated image domain. The different moveout attributes have different accuracy for a given moveout approximation that depends on the corresponding order of travel-time derivative. The most accurate attribute is the zero-offset travel time, and the nonhyperbolic parameter has the worst accuracy, regardless of the moveout approximation. Typically, the mapping of moveout attributes is performed using a point-to-point procedure, whereas the generalized moveout approximation requires two point-to-point mappings. Testing the attribute mapping on the different models shows that the accuracy of mapped attributes is model dependent, whereas the generalized moveout approximation gives practically exact results.

Enhancing seismic reflections using empirical mode decomposition in the flattened domain

Due to different reasons, the seismic reflections are not continuous even when no faults or no discontinuities exist. We propose a novel approach for enhancing the amplitude of seismic reflections and making the seismic reflections continuous. We use plane-wave flattening technique to provide horizontal events for the following empirical mode decomposition (EMD) based smoothing in the flattened domain. The inverse plane-wave flattening can be used to obtain original curved events. The plane-wave flattening process requires a precise local slope estimation, which is provided by the plane-wave destruction (PWD) algorithm. The EMD based smoothing filter is a non-parametric and adaptive filter, thus can be conveniently used. Both pre-stack and post-stack field data examples show tremendous improvement for the data quality, which makes the following interpretation easier and more reliable.

확장 탄성 임피던스 역산을 이용한 저류층 물성 예측

확장 탄성 임피던스(extended elastic impedance, EEI)는 입사각에 따른 음향 임피던스(acoustic impedance, AI) 를 일반화한 탄성 임피던스(elastic impedance, EI)를 확장한 개념으로서 다양한 저류암 물성과 대비가 가능한 것으로 알려져 있다. 하지만 EEI 역산을 적용하여 예측한 저류암적 물성이 실제 물성을 얼마나 정확히 예측하는지를 검증한 사례는 거의 없다. 본 연구에서는 EEI 역산 기법을 이용하여 미국 와이오밍주 Teapot Dome 유전의 주요 저류층 중 하나인 Second Wall Creek 사암층의 P파속도(<TEX>$V_p$</TEX>), S파속도(<TEX>$V_s$</TEX>), P파속도-S파속도 비(<TEX>$V_p/V_s$</TEX>), 포아송비(Poisson's ratio)와 같은 속도 물성들을 유추하고 실제 물리검층 자료와 비교하여 EEI 역산 기법의 정확도를 검증했다. 사용된 자료는 Teapot Dome 유전 남부의 3차원 공심점 모음 자료(CDP gather)와 많은 시추공에서 선택된 4개의 시추공 자료이다. <TEX>$V_s$</TEX> 검층자료는 경험식을 통해 <TEX>$V_p$</TEX> 검층자료로부터 계산되었다. 4개의 속도 물성 EEI 예측 %에러는 한 시추공에서의 포아송비를 제외하면 약 5%를 넘지 않는다. 그러나 속도로부터 직접적으로 계산되지 않는 공극률, 감마선 검층값, 밀도와 같은 물성들은 시추공에서의 EEI 역산 분석 결과가 만족스럽지 못하여 전체 자료에 EEI 역산을 적용할 수 없었다. 따라서 속도 물성의 경우 EEI 역산을 적용할 수 있지만 속도로부터 직접 계산이 되지 않는 물성의 경우는 EEI 역산 적용에 신중해야 할 것으로 판단된다. Extended elastic impedance (EEI) is an extension of elastic impedance (EI) which is a generalization of acoustic impedance (AI) for nonzero angles of incidence and can be tuned to be proportional to reservoir properties. In this study, we evaluated EEI inversion by estimating the P-(<TEX>$V_p$</TEX>) and S-wave velocities (<TEX>$V_s$</TEX>), P-wave to S-wave velocity ratio (<TEX>$V_p/V_s$</TEX>), and Poisson's ratio of the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA. We also applied the EEI inversion technique to estimate porosity, gamma-ray values, and density of the Second Wall Creek Sand. Data used in the study include 3-D pre-stack seismic data from the southern part of the field and four wells, selected from a large well database. The <TEX>$V_s$</TEX> logs at the wells were constructed from the <TEX>$V_p$</TEX> logs using the empirical relationships. The percent prediction errors for the four velocity properties are less than about 5% except for Poisson's ratio at one well, supporting that the EEI inversion can be used in the prediction of rock properties. However, the results from the EEI inversion analysis of porosity, gamma-ray values, and density at the wells were unsatisfactory and thus these properties, which are not directly computed from velocities, may not be suitable for EEI inversion.

An efficient and effective common reflection surface stacking approach using local similarity and plane-wave flattening

We propose an efficient and effective approximate common reflection surface (CRS) stacking approach to obtain a clean zero-offset (ZO) seismic image. Since the basic purpose of CRS stacking is to first stack along offset direction and then smooth along midpoint direction, we propose to first use local similarity as the weight to stack pre-stack seismic data along offset dimension and then to use plane-wave flattened events as the integral surface along midpoint dimension for stacking. Compared with the classic CRS stacking approach, we only need to calculate the local slope and to scan the normal moveout (NMO) velocity in the proposed two-step approximate CRS approach, thus it is more efficient and applicable to real seismic data processing. Both synthetic and field data examples demonstrate a successful performance of the proposed approach.

Automated diffraction delineation using an apex-shifted Radon transform

Diffraction arrivals are important data that have increasingly been used to delineate the sources of diffractors and to explore subsurface discontinuities. In prestack data, diffractions are both zero- and non-zero offset hyperbolas while reflections are only zero-offset hyperbolas. An iterative algorithm using an apex-shifted Radon transform (ASRT) approach is presented in this paper that uses the diffraction hyperbolic trajectory similar to that of prestack time migration in order to locate diffractors and to estimate their corresponding background velocities. Because diffraction energy is generally weak in seismic data and particularly in prestack data, noise attenuation and edge enhancement methods are applied on the instantaneous phase of the seismic data instead of the amplitude data. This means that the phase data are input to the ASRT algorithm. The method is then tested on two synthetic datasets (a point-diffraction model with randomly distributed diffractors and the 2D BP/SEG salt model) and one real data example. Results show that this method can locate the diffractors reasonably well on the rough surfaces of the salt dome and the discontinuities associated with structures such as paleo-channels and faults. Our analysis of the estimated velocities suggests that they are generally valid for diffraction delineation; however, the accuracy of the estimation decreases as background velocity and depth increase.

Multidip tomography formulation for migration velocity analysis

We have developed a new approach for migration velocity analysis by ray-based reflection tomography, formulated according to more than a single dip direction. It is suggested to use a summation-free subsurface imaging system in the angle domain for generating multiparameter common image gathers through depth migration. Each gather associated with this system comprised dip-dependent opening-angle images contributed from the prestack data. Independent moveout information, coming from either a specular or nonspecular dip direction, was extracted inside these gathers to allow the entire scattered field to be involved in the velocity model optimization. By obeying the linear tomographic principle, a multidip tomography system was set to include imaging errors from specular and nonspecular directions. The updated velocity model was reconstructed by a least-squares inverse solution of the multidip tomographic equation system. Providing additional moveout from the migration’s dip, other than the specular one, was believed to be essential because the seismic data were misplaced in the image space while applying depth imaging by an erroneous velocity model. It might make the determination of a clear specular orientation, usually from the seismic image itself, misleading or ambiguous. The conversion of depth moveout into traveltime error along nonspecular rays was done according to an analytic mechanism, derived in the angle domain. The proposed analysis of migration errors by a multiple dip-angle orientation is demonstrated via the multidip tomography formulation by 2D synthetic and real data examples. It seems to be more efficient, as accurate and reliable as the conventional analysis, and to be better able to determine the ill-posed conditioning of the tomographic inversion.

A new technique for lithology and fluid content prediction from prestack data: An application to a carbonate reservoir

One of the leading challenges in hydrocarbon recovery is predicting rock types/fluid content distribution throughout the reservoir away from the boreholes because rock property determination is a major source of uncertainty in reservoir modeling studies. Spatial determination of the lateral and vertical heterogeneities has a direct impact on a reservoir model because it will affect the property distributions. An inappropriate determination of the facies distribution will lead to unrealistic reservoir behavior. Because these data can take different forms (lithologs, cuttings, and for seismic, poststack, and prestack attributes) and have different resolutions, the manual integration of all the information can be tedious and is sometimes impractical. We developed a new neural network-based methodology called democratic neural network association (DNNA). The DNNA method was trained using lithology logs from wells simultaneously with prestack seismic data. This technique, using a probabilistic approach, aims to find patterns in seismic that will predict lithology distribution and uncertainty.

Improving the quality of prestack inversion by prestack data conditioning

Prestack seismic inversion techniques provide valuable information of rock properties, lithology, and fluid content for reservoir characterization. The confidence of inverted results increases with increasing incident angle of seismic gathers. The most accurate result of simultaneous prestack inversion of P-wave seismic data is P-impedance. S-impedance estimation becomes reliable with incident angles approaching 30°, whereas density evaluation becomes reliable with incident angles approaching 45°. As the offset increases, we often encounter “hockey sticks” and severe stretch at large offsets. Hockey sticks and stretch not only lower the seismic resolution but also hinder long offset prestack seismic inversion analysis. The inverted results are also affected by the random noises present in the prestack gathers. We developed a three-step workflow to perform data conditioning prior to simultaneous prestack inversion. First, we mitigated the hockey sticks by using an automatic nonhyperbolic velocity analysis. Then, we minimized the stretch at the far offset by using an antistretch workflow. Last, we improved the signal-to-noise ratio by applying prestack structure-oriented filtering. We evaluated our workflow by applying it to a prestack seismic volume acquired over the Fort Worth Basin, Texas, USA. The results inverted from the conditioned prestack gathers have higher resolution and better correlation coefficients with well logs when compared to those inverted from conventional time-migrated gathers.

Application of a New Wavelet Threshold Method in Unconventional Oil and Gas Reservoir Seismic Data Denoising

Seismic data processing is an important aspect to improve the signal to noise ratio. The main work of this paper is to combine the characteristics of seismic data, using wavelet transform method, to eliminate and control such random noise, aiming to improve the signal to noise ratio and the technical methods used in large data systems, so that there can be better promotion and application. In recent years, prestack data denoising of all-digital three-dimensional seismic data is the key to data processing. Contrapose the characteristics of all-digital three-dimensional seismic data, and, on the basis of previous studies, a new threshold function is proposed. Comparing between conventional hard threshold and soft threshold, this function not only is easy to compute, but also has excellent mathematical properties and a clear physical meaning. The simulation results proved that this method can well remove the random noise. Using this threshold function in actual seismic processing of unconventional lithologic gas reservoir with low porosity, low permeability, low abundance, and strong heterogeneity, the results show that the denoising method can availably improve seismic processing effects and enhance the signal to noise ratio (SNR).

3D seismic data reconstruction based on complex-valued curvelet transform in frequency domain

Traditional seismic data sampling must follow the Nyquist Sampling Theorem. However, the field data acquisition may not meet the sampling criteria due to missing traces or limits in exploration cost, causing a prestack data reconstruction problem. Recently researchers have proposed many useful methods to regularize the seismic data. In this paper, a 3D seismic data reconstruction method based on the Projections Onto Convex Sets (POCS) algorithm and a complex-valued curvelet transform (CCT) has been introduced in the frequency domain. In order to improve reconstruction efficiency and reduce the computation time, the seismic data are transformed from the t–x–y domain to the f–x–y domain and the data reconstruction is processed for every frequency slice during the reconstruction process. The selection threshold parameter is important for reconstruction efficiency for each iteration, therefore an exponential square root decreased (ESRD) threshold is proposed. The experimental results show that the ESRD threshold can greatly reduce iterations and improve reconstruction efficiency compared to the other thresholds for the same reconstruction result. We also analyze the antinoise ability of the CCT-based POCS reconstruction method. The example studies on synthetic and real marine seismic data showed that our proposed method is more efficient and applicable.

Seismic facies analysis from pre-stack data using self-organizing maps

In facies analysis, seismic data are clustered in different groups. Each group represents subsurface points with similar physical properties. Different groups can be related to differences in lithology, physical properties of rocks and fluid changes in the rocks. The supervised and unsupervised data clustering are known as two types of clustering architecture. In supervised clustering, the number of clusters is predefined, while in unsupervised clustering, a collection of patterns partitions into groups without predefined clusters.In this study, the pre-stack data clustering is used for seismic facies analysis. In this way, a horizon was selected from pre-stack data, followed by sorting of data using offset. A trace associated with each CDP is constructed, for which the first and second samples are related to the first and second offsets, respectively. The created trace is called consolidated trace which is characteristic of subsurface points. These consolidated traces are clustered by using self-organizing maps (SOM).In proposed pre-stack seismic data clustering, points with similar physical properties are placed in one cluster. Seismic data associated with hydrocarbon reservoirs have very different characteristics that are easily recognized.The efficiency of the proposed method was tested on both synthetic and real seismic data. The results showed that the algorithm improves the data classification and the points of different properties are noticeable in final maps.

Diffraction imaging of the Eagle Ford shale

Diffraction imaging is a novel technology that uses diffractions to image very small subsurface elements. Diffraction imaging may: (1) improve prospect characterization and pre-drill assessment of the local geology; (2) improve production and recovery efficiency; (3) reduce field development cost; and (4) decrease environmental impact. Field development may be accomplished with fewer wells to optimally produce the reservoir using high-resolution images of small-scale fractures in shale or carbonate intervals. Standard approaches to obtain high-resolution information, such as coherency analysis and structure-oriented filters, derive attributes from stacked, migrated images. Diffraction imaging, in comparison, acts on the pre-stack data, and has the potential to focus super-resolution structural information. Diffraction images can be used as a complement to the structural images produced by conventional reflection imaging techniques, by emphasizing small-scale structural elements that are difficult to interpret on a conventional depth image. An efficient way to obtain diffraction images is to first separate the migration events according to the value of the specularity angle, in a similar way to offset gathers, and subsequent post-stack processing. The high-resolution potential is demonstrated by the diffraction images from the Kenedy 3D survey over the Eagle Ford shale, which show much more detail than conventional depth migration or coherence.

The Fractured Prediction for Narrow-Azimuth Seismic Data of Carbonate

It is vital importance to understand distribution and development of carbonate reservoir fractures for studying carbonate reservoir. Stimulate and acceptance conditions of study area in the desert are poor, and The seismic data is characterized by narrow azimuth. Limit of data has influence on fracture prediction of target layer. In this paper, the post-stack and pre-stack seismic data were used. At first , techniques of stress field numerical simulation was applied to predict the distribution of tectonic crack of the study area ; Secondly, because of the characteristics of narrow-azimuth seismic data, the paper applied technology of near and far offset attribute discrepancy of pre-stack data to predict the degree of development of cracks. Finally, effective fracture was predicted on the basis of development of the forecasted cracks.