Vertical Resolution Of Seismic Data Research Articles

Multichannel seismic data attenuation compensation via curvelet‐based sparsity promotion

AbstractDue to subsurface viscosity and heterogeneity, the vertical resolution of observed seismic data is decreased after wave propagation, generating nonstationary seismic data with amplitude attenuation and phase distortion. Inverse Q filtering techniques are always used to enhance the vertical resolution of seismic data. However, the majority of inverse Q filtering methods treat attenuation compensation trace by trace, which may produce non‐robust compensation results with poor transverse continuity and amplify noise energy in noisy cases. Thus, we develop a novel sparsity‐promoting inversion‐based multichannel seismic data attenuation compensation approach by introducing a sparse constraint for curvelet coefficients of multichannel compensated data, which takes the transverse continuity of compensated data into account. Besides, the proposed method with a sparse constraint for curvelet coefficients has a better noise‐resistance property, which can attenuate the noise energy in noisy cases during attenuation compensation, improving compensation accuracy and robustness. To improve its computational efficiency, a fast iterative shrinkage–thresholding algorithm is adopted to solve the established lasso problem. Synthetic data examples with different noise levels and two post‐stack field data examples validate the effectiveness of the proposed multichannel method. Its compensation results have superior vertical resolution, transverse continuity and noise robustness in comparison to the conventional single‐channel compensation method using a damped least squares algorithm.

Machine learning-based vertical resolution enhancement considering the seismic attenuation

The vertical resolution of seismic data is important for the interpretation of geologic features at a fine scale. To improve the resolution of seismic data, spectral enhancement has progressed mainly due to one-dimensional (1D) convolutional models and deconvolution filters; however, recent studies have applied machine learning (ML) algorithms. To generate successful outcome using ML techniques, it is important to reflect the features of target data in the training stage of the ML model. One of main features of seismic field data is a non-stationary wavelet over time due to the change of frequency content as the wavelet passes through specific regions, such as a gas reservoir. However, since conventional methods are generally based on a 1D convolutional model, which assumes that the propagating wavelet is stationary, they show low performance for the parts where the frequency content of the wavelet is significantly changed. In this study, we developed a spectral enhancement algorithm using ML techniques, which can be applied to a seismic trace with different frequency contents over time. We showed that sparse spike inversion results of all target data, rather than just well log data, can be useful to obtain information on the reflectivity series for generating a training data set. To verify the performance of the new algorithm, we compared the spectrally enhanced results from the ML model trained by stationary wavelets and time-variant wavelets and confirmed that the latter wavelets provided better results. In addition, we proposed a quality control (QC) method for verifying the spectrally enhanced results. • We developed a spectral enhancement ML algorithm consider the seismic attenuation. • The ML model trained by time-variant wavelets yielded better results for the area with seismic attenuation. • The results obtained by the ML were verified the validity by our proposed QC method only using seismic data.

Reflection Coefficients Inversion Based on the Bidirectional Long Short-Term Memory Network

Improving the vertical resolution of seismic data to satisfy the demands for detailed characterization of reservoirs is an important part in seismic data processing. The sparse spike inversion(SSI) technique greatly enhances the seismic resolution by assuming that the reflection coefficients follow the sparse distributions. However, its characterization for the spatial structure of thin and thin interbed reservoirs is still limited. In this letter, we propose a novel approach for enhancing seismic resolution using a bidirectional long short-term memory(BiLSTM) neural network by extracting reflection coefficients directly from seismic data. By designing the network architectures, multiple seismic samples map to the specific reflection coefficient. Compared with the SSI method, the BiLSTM neural network provides higher resolution inversion results on model data, which demonstrates the effectiveness of the proposed approach for thin structure characterizations. The convergence speed of the proposed method is fast and the training process is stable. In addition, we conduct the high-resolution seismic data processing on the field data based on the transfer learning technology. High-resolution processing results illustrate the generalization ability and adaptability of the BiLSTM neural network.

Evaluating the thickness of thin-bed seams using seismic data on the example of the Tula-Bobrikovian sediments in the Republic of Tatarstan

Background.Determining the productive deposit thickness is of fundamental importance for assessing the reserves of oil and gas fields. 3D seismic data is used to assess the thickness of seams in the interwell space. However, owing to the limited vertical resolution of seismic data, estimating thicknesses of thin deposits (less than 20 m) is challenging.Aim.To evaluate different approaches to calculating the thickness of the productive deposits based on seismic data with the purpose of selecting the most optimal approach.Materials and methods.We compared the results obtained using different approaches to assessing the productive deposit thickness of the Tula-Bobrikovian age in the interwell space, including the convergence method (calculating the thickness for oilwells with no seismic data used), the use of seismic attributes to calculate the “seismic attribute — reservoir thickness” dependency (for attributes, dominant frequency and mono-frequency component at 60 Hz), estimation of the thickness from the seismic signal shape. Cokriging was used to calculate inferred power maps from seismic attribute data and to classify them by waveform. For each of the techniques, the crossvalidation method and calculating the root-mean-square deviation were used as quality criteria.Results.When assessing the accuracy of thickness map development, the root-mean-square deviation was 12.3 m according to convergence method, 10.2 m — to the dominant frequency attribute, 7.2 m — to the attribute of the monofrequency component at 60 Hz and 6.3 m — to the signal shape classification. The latter method yielded the best results, and the developed thickness map allowed paleo-cut to be traced.Conclusions.Applying the thickness estimation method based on the seismic signal shape allows the value of the root-mean-square deviation to be reduced by a factor of 2 compared to that of the widely adopted convergence method. This approach permits productive deposits thickness to be more accurately estimated and hydrocarbon reserves to be determined.

Non-Stationary Gabor Deconvolution Enhanced Full-Stack Penobscot Seismic Data is in Preparation for Public Download

The Penobscot dataset consisting of pre-stack seismic data and well-logs data is an exercising, open-to-public, oilfield data that is widely used by academics and researchers to test their workflow on reservoir evaluations, stratigraphic inferring, and reserve calculations. To make this data even more useful for such studies, we are enhancing the seismic data to an acceptable degree of resolution. Seismic resolution is primarily limited to wavelet overprints and interferences. Improving the vertical resolution of seismic data is challenged by the attenuation in wavelet propagation. A non-stationary deconvolution technique is needed to compensate for such anelastic effects. Gabor deconvolution is a widely accepted method to increase the resolution of seismic data as it utilizes information from the data seismic itself to minimize subjectivity in incorporating wavelet models. Our preliminary results show that the technique can widen up to twice the initial bandwidth. The resolution is thickened from 45.32 m to 22.15 m based on the tuning thickness analysis. The widening occurs on both sides at low and high frequencies without unnecessary energy increases and noise manifestations. The results show good agreement with the two available wells (L-30 and B-41). Our motivation is to provide higher resolution seismic data thus it can be used in workshops, software demonstrations, and academic discussion among practitioners and researchers. The preliminary enhanced seismic data is currently accessible by request, but it will soon be available to the public. In the future, we shall implement the same analysis to the full pre-stack seismic data so the benefits of Penobscot dataset will gain their optimum.

Application of frequency extension processing technology in logging constrained inversion

The resolution of seismic data affects the accuracy of sand body identification. Generally, the higher the resolution of seismic data, the higher the accuracy of sand body identification. Aiming at the problem of low seismic resolution in block X, this paper attempts to increase the effective signal bandwidth of the original seismic data by using the frequency extension method, maximize the resolution on the premise of ensuring that the seismic data are not distorted, and then use the waveform indication inversion technology to predict the reservoir after the frequency extension. Compared with the drilled wells, it is found that the combination of frequency extension and waveform indication inversion can not only improve the vertical resolution of seismic data, but also maintain the lateral continuity of seismic data, which conforms to the distribution characteristics of sand body and has higher prediction accuracy. The method of frequency extension + waveform indication inversion has a good application prospect, which provides a new idea for the next step of sand body characterization and well location deployment.

Siliciclastic depositional model within a subsurface coastal parasequence based on barriers and baffles – An application to the Donghe Sandstone Member in the Hudson Oilfield, Tarim Basin, NW China

Detailed analyses of a siliciclastic coastal reservoir highlights the need for an enhanced knowledge of their depositional model. At the scale of the bed sets, however, it is difficult to determine the depositional model, largely due to a limit in vertical resolution of seismic data and conventional wireline logs. Barriers and baffles, following either surfaces of bed sets or beds, act as isochronous boundaries, therefore, holding a key to reveal the sedimentary evolution of subsurface reservoirs at small scales. Calcareous and argillaceous barriers and baffles of the Donghe Sandstone Member in the Hudson Oilfield have obvious low conductivity responses in dipmeter data, therefore, they are interpreted and correlated by integration of conventional logs and dipmeter data. Barriers separate the Donghe Sandstone Member into three retrogradational bed sets (i.e., B1~B3) and two progradational bed sets (i.e., B4~B5), of which beds are constrained by baffles with larger dip angles than barriers in the current structure pattern as well as the period of deposition. The stacking patterns indicate relative sea-level rise within B1~B3 and fall within B4~B5. The relative sea-level changes control the baffle frequency, thickness and length of barriers and baffles. Vertically, the variation of frequency and maximum thickness of both argillaceous and calcareous baffles increase during the transgression yet reduce during regression. Horizontally, the length of barriers and baffles positively correlates with their maximum thickness along the paleoshoreline, as it does the direction perpendicular to the paleoshoreline. Therefore, a depositional model of early retrogradation followed by progradation is built for the Donghe Sandstone Member, a siliciclastic coastal parasequence with upper-shoreface and lower-shoreface facies, based on barriers and baffles. This depositional model is embodied with certain principles of reservoir architecture and behavior.

Estimation of reservoir porosity using analysis of seismic attributes in an Iranian oil field

The most commonly used data for reservoir description are well and seismic data. Well data such as logs typically provide sufficient vertical resolution but leave a large space between the wells. Three-dimensional seismic data, on the other hand, can provide more detailed reservoir characterization between wells. However, the vertical resolution of seismic data is poor compared with that of well data. Conventionally, seismic data have been used to delineate reservoir structure; however, seismic data can be used for reservoir characterization such as porosity. Therefore, we can combine these two types of data to obtain reservoir parameters such as porosity and saturation. It is available the desired parameter (such as porosity) of the number of wells in the reservoir and seismic cube. And we are looking for the parameter estimation in the whole reservoir. To do this, there are several methods including multiple linear regression, neural networks, and geostatistical methods. Therefore, by determining the reservoir properties and correctly estimating these parameters, optimization can be performed with fewer wells, and the costs of exploration and production are reduced. In this paper, we apply these methods on the available data for an oil field in southwest Iran to obtain the porosity in a total reservoir cube, and these methods are then compared with one another. The results clearly show the superiority of neural networks compared with the other methods in estimating the reservoir parameter. The results also show that although estimation accuracy is increased significantly with the use of the geostatistical approach, this method requires that a sufficient number of well logs, representing all the fields under investigation, be provided in order to improve the geological model obtained by the multi-attribute and neural network methods.

Combining CEEMD and recursive least square for the extraction of time-varying seismic wavelets

The vertical resolution of seismic data is greatly affected by time-varying seismic wavelets. To break through the shape restriction in amplitude estimation and the dependency upon the constant or piecewise stationary assumptions in phase estimation in the existing methods, we propose a time-varying wavelet extraction method combining CEEMD (Complementary Ensemble Empirical Mode Decomposition) and RLS (Recursive Least Square). According to the differences that the amplitude spectra of wavelets and reflection coefficient are smooth and oscillating respectively, the logarithm amplitude spectra of the seismogram at different times are decomposed into multi-layer components with different oscillation scales by CEEMD, and the amplitude spectra of time-varying wavelets can be estimated by filtering the oscillating components and reconstructing the smooth components. Hilbert transform is applied to the estimated amplitude spectra to build time-varying minimum-phase wavelets, and the system functions and pole-zeros in Z domain of minimum-phase wavelets can be estimated using RLS. Finally, time-varying mixed-phase wavelets are extracted by reconfiguring the pole-zeros and determining the optimal combinations under the constraint of local similarity. This method does not need to calculate the Q value. The numerical simulation and real seismic data processing results demonstrate that the proposed method can improve the accuracy of time-varying wavelet extraction compared to the conventional method.

A time-varying wavelet phase extraction method using the wavelet amplitude spectra

ABSTRACTThe vertical resolution of seismic data is greatly affected by time-varying seismic wavelets, and the phase extraction is the key topic for the accurate wavelet extraction. The traditional methods are limited to the piecewise stationary and constant-phase assumptions, so that the extracted results are not satisfactory. In this paper, we try to explore a new time-varying wavelet phase extraction method using the wavelet amplitude information. The propagation and attenuation law of seismic wavelets is analyzed, and the relationship between the amplitude and phase spectra of time-varying wavelets is obtained by deriving the wave equation for a viscoelastic medium. We find that accurately estimating the amplitude spectra at different time points and the initial phase spectrum is necessary to extract accurate time-varying wavelet phase spectra. Based on this conclusion, the wavelet amplitude spectra and initial phase spectrum are estimated based on time–frequency spectral modelling and higher-order cumulants, respectively, so that the phase spectra of time-varying wavelets are estimated by applying the obtained relationship. The numerical simulation and real seismic data processing results demonstrate that the proposed method can improve the accuracy of time-varying wavelet phase extraction compared to the traditional method.

Correction of seismic time-lapse data with different geometries

ABSTRACTIn the interpretation of time-lapse seismic data, we first assume that basal and monitor seismic data have been gathered and processed under equal condition. Basically having two sets of seismic data (basal and monitor data) does not confirm the possibility of 4D seismic data analysis and repeatability term commands that data should have equal data gathering and process condition. This is due to the low vertical resolution of seismic data which makes the changes in amplitude of seismic in basal and monitoring operations to occur finitely. Therefore, differences in amplitudes in two seismic surveys that have a source other than variations in reserval fluid saturation cause errors in 4D interpretations. In this paper, we represent a procedure to unify different geometries and also study post-stack processing methods for seismic time-lapse data and execute them on existing data at the studied field. In the, first step geometrical conception of basal and monitoring data should be equalized. This section has been implemented using a new methodology, developed in this paper and is called “similarity table”. Then, data will be analyzed with spectra point of view and by executing shaping filters and by using cross equalization approach, basal and monitor seismic information will be closer as far as possible. Finally, analyzes were performed to validate the obtained results and evaluate the quality of time -lapse seismic analyzes.

Seismic interpretation below tuning with multiattribute analysis

The tuning-bed thickness or vertical resolution of seismic data traditionally is based on the frequency content of the data and the associated wavelet. Seismic interpretation of thin beds routinely involves estimation of tuning thickness and the subsequent scaling of amplitude or inversion information below tuning. These traditional below-tuning-thickness estimation approaches have limitations and require assumptions that limit accuracy. The below-tuning effects are a result of the interference of wavelets, which are a function of the geology as it changes vertically and laterally. However, numerous instantaneous attributes exhibit effects at and below tuning, but these are seldom incorporated in thin-bed analyses. A seismic multiattribute approach employs self-organizing maps to identify natural clusters from combinations of attributes that exhibit below-tuning effects. These results may exhibit changes as thin as a single sample interval in thickness. Self-organizing maps employed in this fashion analyze associated seismic attributes on a sample-by-sample basis and identify the natural patterns or clusters produced by thin beds. This thin-bed analysis utilizing self-organizing maps has been corroborated with extensive well control to verify consistent results. Therefore, thin beds identified with this methodology enable more accurate mapping of facies below tuning and are not restricted by traditional frequency limitations.

Analysis on the Frequency Expansion Seismic Processing Technology in TA112 Field with Vibration

The single layer of the sand body in TA112 field is thin, changing, and complex. Therefore, to accurately predict sand body by using seismic data, the frequency band of it is narrowed with HFE algorithm, the pre-stack data of low dominant frequency is expanded in frequency. The dominant frequency is increased from 45 Hz to 75 Hz, and the bandwidth is expanded from 10 Hz~65 Hz to 10 Hz~125 Hz. Through a comprehensive interpretation on seismic and geological data, the vertical resolution of seismic data, the vertical fitness to sand body, the fitness to the plane distributed sand body, the fitness to the evolution law of sand body are verified and checked. As a result, it is known that pre-stack frequency expansion seismic data is of clear geological significance and can better reflect the changing law of sand groups, and also it is better than the data before frequency expansion and can meet the needs of the oil field exploration and development to a certain extent.

High-resolution reservoir characterization by seismic inversion with geological constraints

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface. Reservoirs in clinoform systems are difficult to characterize because they show two main types of complexity: complex sedimentology and poor seismic imaging. The former is due to complex internal architecture with many small sedimentary elements often at a sub-seismic scale. Poor seismic imaging occurs because the internal layers of the clinoform often do not differ much in their acoustic properties, in addition, they have thicknesses that are below the vertical resolution of seismic data, and therefore such features do not show up very well on seismic images. Obviously, the most unfavorable situation occurs when both conditions interact. The static model of a fluvio-deltaic (clinoform) reservoir is extremely important because it plays a critical role in the field development planning. There are many ways to build a static model but the most effective way is by integrating seismic and well data through the construction of an acoustic impedance model by inversion of seismic data within a sequence stratigraphic framework. There are several reasons to integrate well-log data into the inverse process in the reservoir characterization workflow, such as the integration of different sources of information in a common earth model, the estimation of the seismic distortion (also known as the wavelet filter), etc. In addition, the low vertical resolution of seismic data is an important motivation to integrate well-log information into the inverse process and thereby complement the relatively dense horizontal coverage of seismic data with high-resolution borehole data (Van Riel and Mesdag, 1988; Van Riel and Pendrel, 2000; Van Riel, 2000; Bosch et al., 2009). Another potential benefit of seismic inversion is the ability to incorporate structural and stratigraphic information of the reservoir in order to differentiate between similar mathematical solutions on the basis of their geological viability. We present two different inversion approaches for poststack, time migrated seismic data and apply them to a clinoform sequence in the North Sea. Both inversion methods are not fully 2D, but more than a series of independently processed 1D inversions. To stress the enforced continuity along the geological structure, we use the name pseudo 2D inversion. The methods use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is then propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and employs horizontal and vertical regularization parameters for lateral propagation. Both methods obtain an acoustic impedance image with a high level of detail. The first method has a low level of parameterization embedded in a geological framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geological settings of the reservoir, however there is no explicit geological significance and it is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data. The methods are especially considered to be useful when seismic data alone do not reveal the actual detailed reservoir architecture, which can be the case either because of their low vertical resolution or exceedingly thin layering.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, “zero-offset” seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial “ringiness”. In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral “blueness” is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

How Laterally Extensive Are Sequence Boundaries?

Sequence boundaries are the foundation on which modern sequence stratigraphic concepts have been erected. They are portrayed as discrete surfaces that are correlatable on the scales of basins. They comprise surfaces of stratal discontinuity (onlap, downlap, toplap, and erosional truncation) linked by uniquely defined correlative conformities. They are distinguished from local discontinuity surfaces by their property of regional extent. Onlap surfaces are absolutely central to the definition of type 1 and type 2 sequence boundaries and set the chronostratigraphic clock by which the correlative conformity for any given sequence boundary can be uniquely defined. This paper examines the conditions necessary for the development of onlap onto a discrete surface. Drawing extensively from Van Hinte's conception of condensed onlap sections and apparent seismic onlap, a simple geometric argument is presented that establishes a rigorous constraint on the development of a discrete onlap surface. The main conclusion is that a discrete onlap surface can form only if there is no net sediment accumulation beyond the lap-out position. Consideration of depositional processes in a range of environments suggests that this condition is rarely met and laterally extensive onlap onto a discrete surface is extremely rare. More commonly, onlap develops onto families of closelymore » related surfaces that may appear as a single surface on seismic data and may be mistakenly correlated over large areas. An extensive correlation of apparent onlap surfaces will cross time lines and should not be used to erect a chronostratigraphic framework. If it is assumed that sequence boundaries have an eustatic origin, there is a tacit expectation that they should be correlatable over large areas. It is concluded that the apparent areal extent of sequence boundaries owes more to the map-driven need to correlate over large areas and to the limited vertical resolution of seismic data than to stratigraphic reality.« less