Texture attributes for detection of salt

Within areas of salt tectonics, seismic imaging requires extensive updating of the velocity model. This includes defining the boundaries of salt structures which are often characterized by changes in texture of the seismic signal rather than reflectivity. The main characteristics of texture inside salt structures are identified. Three groups of texture attributes are studied: gray-level co-occurrence matrix (GLCM) attributes, frequency-based attributes, and dip and similarity attributes. Various combinations of the selected attributes are tested in a supervised Bayesian classification method. Experimental results show that the classification performance improves by combining at least two texture attribute groups. The classifier computes an estimate of the pixelwise probability of salt. It can then be applied to compute the probability of salt on different seismic sections. Classification results were found more robust based on timeslices. The result from classification, the salt probability image, is then input to a segmentation algorithm that produces a smooth border delimiting the extent of the salt. The segmented salt contours corresponded fairly well to the contours provided by an interpreter.     

Robust salt-dome detection using the ranking of texture-based attributes

The accurate interpretation and analysis of seismic data heavily depends on the robustness of the algorithms used. We focus on the robust detection of salt domes from seismic surveys. We discuss a novel feature-ranking classification model for saltdome detection for seismic images using an optimal set of texture attributes. The proposed algorithm overcomes the limitations of existing texture attribute-based techniques, which heavily depend on the relevance of the attributes to the geological nature of salt domes and the number of attributes used for accurate detection. The algorithm combines the attributes from the Gray-Level Co-occurrence Matrix (GLCM), the Gabor filters, and the eigenstructure of the covariance matrix with feature ranking using the information content. The top-ranked attributes are combined to form the optimal feature set, which ensures that the algorithm works well even in the absence of strong reflectors along the salt-dome boundaries. Contrary to existing salt-dome detection techniques, the proposed algorithm is robust and computationally efficient, and works with small-sized feature sets. I used the Netherlands F3 block to evaluate the performance of the proposed algorithm. The experimental results suggest that the proposed workflow based on information theory can detect salt domes with accuracy superior to existing salt-dome detection techniques.     

Estimation of pore‐pressure change in a compacting reservoir from time‐lapse seismic data

An approach is developed to estimate pore‐pressure changes in a compacting chalk reservoir directly from time‐lapse seismic attributes. It is applied to data from the south‐east flank of the Valhall field. The time‐lapse seismic signal of the reservoir in this area is complex, despite the fact that saturation changes do not have an influence. This complexity reflects a combination of pressure depletion, compaction and stress re‐distribution throughout the reservoir and into the surrounding rocks. A simple relation is found to link the time‐lapse amplitude and time‐shift attributes to variations in the key controlling parameter of initial porosity. This relation is sufficient for an accurate estimation of pore‐pressure change in the inter‐well space. Although the time‐lapse seismic estimates mostly agree with reservoir simulation, unexplained mismatches are apparent at a small number of locations with lower porosities (less than 38%). The areas of difference between the observations and predictions suggest possibilities for simulation model updating or a better understanding of the physics of the reservoir.     

Quantitative gas saturation estimation by frequency‐dependent amplitude‐versus‐offset analysis

Seismic amplitudes contain important information that can be related to fluid saturation. The amplitude‐versus‐offset analysis of seismic data based on Gassmann's theory and the approximation of the Zoeppritz equations has played a central role in reservoir characterization. However, this standard technique faces a long‐standing problem: its inability to distinguish between partial gas and “fizz‐water” with little gas saturation. In this paper, we studied seismic dispersion and attenuation in partially saturated poroelastic media by using frequency‐dependent rock physics model, through which the frequency‐dependent amplitude‐versus‐offset response is calculated as a function of porosity and water saturation. We propose a cross‐plotting of two attributes derived from the frequency‐dependent amplitude‐versus‐offset response to differentiate partial gas saturation and “fizz‐water” saturation. One of the attributes is a measure of “low frequency”, or Gassmann, of reflectivity, whereas the other is a measure of the “frequency dependence” of reflectivity. This is in contrast to standard amplitude‐versus‐offset attributes, where there is typically no such separation. A pragmatic frequency‐dependent amplitude‐versus‐offset inversion for rock and fluid properties is also established based on Bayesian theorem. A synthetic study is performed to explore the potential of the method to estimate gas saturation and porosity variations. An advantage of our work is that the method is in principle predictive, opening the way to further testing and calibration with field data. We believe that such work should guide and augment more theoretical studies of frequency‐dependent amplitude‐versus‐offset analysis.     

Analysis of time‐lapse travel‐time and amplitude changes to assess reservoir compartmentalization

Fluid depletion within a compacting reservoir can lead to significant stress and strain changes and potentially severe geomechanical issues, both inside and outside the reservoir. We extend previous research of time‐lapse seismic interpretation by incorporating synthetic near‐offset and full‐offset common‐midpoint reflection data using anisotropic ray tracing to investigate uncertainties in time‐lapse seismic observations. The time‐lapse seismic simulations use dynamic elasticity models built from hydro‐geomechanical simulation output and a stress‐dependent rock physics model. The reservoir model is a conceptual two‐fault graben reservoir, where we allow the fault fluid‐flow transmissibility to vary from high to low to simulate non‐compartmentalized and compartmentalized reservoirs, respectively. The results indicate time‐lapse seismic amplitude changes and travel‐time shifts can be used to qualitatively identify reservoir compartmentalization. Due to the high repeatability and good quality of the time‐lapse synthetic dataset, the estimated travel‐time shifts and amplitude changes for near‐offset data match the true model subsurface changes with minimal errors. A 1D velocity–strain relation was used to estimate the vertical velocity change for the reservoir bottom interface by applying zero‐offset time shifts from both the near‐offset and full‐offset measurements. For near‐offset data, the estimated P‐wave velocity changes were within 10% of the true value. However, for full‐offset data, time‐lapse attributes are quantitatively reliable using standard time‐lapse seismic methods when an updated velocity model is used rather than the baseline model.     

Seismic time‐lapse effects of solution salt mining – a feasibility study

This article addresses the question whether time‐lapse seismic reflection techniques can be used to follow and quantify the effects of solution salt mining. Specifically, the production of magnesium salts as mined in the north of the Netherlands is considered. The use of seismic time‐lapse techniques to follow such a production has not previously been investigated. For hydrocarbon production and CO₂ storage, time‐lapse seismics are used to look at reservoir changes mainly caused by pressure and saturation changes in large reservoirs, while for solution mining salt is produced from caverns with a limited lateral extent, with much smaller production volumes and a fluid (brine) replacing a solid (magnesium salt). In our approach we start from the present situation of the mine and then study three different production scenarios, representing salt production both in vertical and lateral directions of the mine. The present situation and future scenarios have been transformed into subsurface models that were input to an elastic finite‐difference scheme to create synthetic seismic data. These data have been analysed and processed up to migrated seismic images, such that time‐lapse analyses of intermediate and final results could be done. From the analyses, it is found that both vertical and lateral production is visible well above the detection threshold in difference data, both at pre‐imaging and post‐imaging stages. In quantitative terms, an additional production of the mine of 6 m causes time‐shifts in the order of 2 ms (pre‐imaging) and 4 ms (post‐imaging) and amplitude changes of above 20% in the imaged sections. A laterally oriented production causes even larger amplitude changes at the edge of the cavern due to replacement of solid magnesium salt with brine introducing a large seismic contrast. Overall, our pre‐imaging and post‐imaging time‐lapse analysis indicates that the effects of solution salt mining can be observed and quantified on seismic data. The effects seem large enough to be observable in real seismic data containing noise.     

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.     

A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels

Saltbodies are important subsurface structures that have significant implications for hydrocarbon accumulation and sealing in petroleum reservoirs, and accurate saltbody imaging and delineation is now greatly facilitated with the availability of three-dimensional seismic surveying. However, with the growing demand for larger survey coverage and higher imaging resolution, the size of seismic data is increasing dramatically. Correspondingly, manual saltbody interpretation fails to offer an efficient solution, particularly in exploration areas of complicated salt intrusion history. Recently, artificial intelligence is attracting great attention from geoscientists who desire to utilize the popular machine learning technologies for evolving the interpretational tools capable of mimicking an experienced interpreter's intelligence. This study first implements two popular machine learning tools, the multi-layer perceptron and the convolutional neural network, for delineating seismic saltbodies at sample and pattern levels, respectively, then compares their performance through applications to the synthetic SEAM seismic volume, and moreover tentatively investigates what contributes to the better convolutional neural network delineation. Specifically, the multi-layer perceptron scheme is capable of efficiently utilizing an interpreter's knowledge by selecting, pre-conditioning and integrating a set of seismic attributes that best highlight the target saltbodies, whereas the convolutional neural network scheme makes it possible for saltbody delineation directly from seismic amplitude and thus significantly reduces the dependency on attribute selection from interpreters. It is concluded that the better performance from the convolutional neural network scheme results from two factors. First, the convolutional neural network builds the mapping relationship between the seismic signals and the saltbodies using the original seismic amplitude instead of manually selected seismic attributes, so that the negative impact of using less representative attributes is virtually eliminated. Second and more importantly, the convolutional neural network defines, learns and identifies the saltbodies by utilizing local seismic reflection patterns, so that the seismic noises and processing artefacts of distinct patterns are effectively identified and excluded.     

Towards quantitative evaluation of gas injection using time‐lapse seismic data

Of particular concern in the monitoring of gas injection for the purposes of storage, disposal or improved oil recovery is the exact spatial distribution of the gas volumes in the subsurface. In principle this requirement is addressed by the use of 4D seismic data, although it is recognized that the seismic response still largely provides a qualitative estimate of moved subsurface fluids. Exact quantitative evaluation of fluid distributions and associated saturations remains a challenge to be solved. Here, an attempt has been made to produce mapped quantitative estimates of the gas volume injected into a clastic reservoir. Despite good results using three accurately repeated seismic surveys, time‐delay and amplitude attributes reveal fine‐scale differences though large‐scale agreement in the estimated fluid movement. These differences indicate disparities in the nature of the two attributes themselves, which can be explained by several possible causes. Of most impact are the effects of processing and migration, wave interference effects and noise from non‐repeatability of the seismic surveys. This subject highlights the need for a more careful consideration in 4D acquisition, amplitude processing and use of true amplitude preserving attributes in quantitative interpretation.     

基于频率域峰值属性的河道砂体定量预测及应用(英文)

河道砂体是陆相含油气盆地最重要的储集类型之一,其边界识别和厚度定量预测是储层预测的热点难题。本文在总结现有方法技术的基础上,提出一种利用频率域峰值属性进行河道砂体边界识别和厚度定量预测的新方法。对典型河道薄砂体地震反射进行了正演模拟,构造了一种新的地震属性——峰值频率-振幅比,研究表明:峰值频率属性对地层厚度变化敏感,振幅属性对地层岩性变化敏感,两者比值突出河道砂体的边界,同时,借助峰值频率与薄层厚度间存在的定量关系进行薄砂体厚度计算。实际数据应用表明,地震峰值频率属性可以较好的刻画河道的平面展布特征;峰值频率-振幅比属性可以提高对河道砂体边界的识别能力;利用频率域地震属性进行砂体边界识别及厚度定量预测是可行的。     

Misfit functionals in Laplace‐Fourier domain waveform inversion,with application to wide‐angle ocean bottom seismograph data

In seismic waveform inversion, non‐linearity and non‐uniqueness require appropriate strategies. We formulate four types of L2 normed misfit functionals for Laplace‐Fourier domain waveform inversion: i) subtraction of complex‐valued observed data from complex‐valued predicted data (the ‘conventional phase‐amplitude’ residual), ii) a ‘conventional phase‐only’ residual in which amplitude variations are normalized, iii) a ‘logarithmic phase‐amplitude’ residual and finally iv) a ‘logarithmic phase‐only’ residual in which the only imaginary part of the logarithmic residual is used. We evaluate these misfit functionals by using a wide‐angle field Ocean Bottom Seismograph (OBS) data set with a maximum offset of 55 km. The conventional phase‐amplitude approach is restricted in illumination and delineates only shallow velocity structures. In contrast, the other three misfit functionals retrieve detailed velocity structures with clear lithological boundaries down to the deeper part of the model. We also test the performance of additional phase‐amplitude inversions starting from the logarithmic phase‐only inversion result. The resulting velocity updates are prominent only in the high‐wavenumber components, sharpening the lithological boundaries. We argue that the discrepancies in the behaviours of the misfit functionals are primarily caused by the sensitivities of the model gradient to strong amplitude variations in the data. As the observed data amplitudes are dominated by the near‐offset traces, the conventional phase‐amplitude inversion primarily updates the shallow structures as a result. In contrast, the other three misfit functionals eliminate the strong dependence on amplitude variation naturally and enhance the depth of illumination. We further suggest that the phase‐only inversions are sufficient to obtain robust and reliable velocity structures and the amplitude information is of secondary importance in constraining subsurface velocity models.     

Bayesian inversion of time‐lapse seismic data for the estimation of static reservoir properties and dynamic property changes

Seismic conditioning of static reservoir model properties such as porosity and lithology has traditionally been faced as a solution of an inverse problem. Dynamic reservoir model properties have been constrained by time‐lapse seismic data. Here, we propose a methodology to jointly estimate rock properties (such as porosity) and dynamic property changes (such as pressure and saturation changes) from time‐lapse seismic data. The methodology is based on a full Bayesian approach to seismic inversion and can be divided into two steps. First we estimate the conditional probability of elastic properties and their relative changes; then we estimate the posterior probability of rock properties and dynamic property changes. We apply the proposed methodology to a synthetic reservoir study where we have created a synthetic seismic survey for a real dynamic reservoir model including pre‐production and production scenarios. The final result is a set of point‐wise probability distributions that allow us to predict the most probable reservoir models at each time step and to evaluate the associated uncertainty. Finally we also show an application to real field data from the Norwegian Sea, where we estimate changes in gas saturation and pressure from time‐lapse seismic amplitude differences. The inverted results show the hydrocarbon displacement at the times of two repeated seismic surveys.     

3‐D shallow‐water seismic survey evaluation and design using the focal‐beam method: a case study offshore Abu Dhabi

For 3‐D shallow‐water seismic surveys offshore Abu Dhabi, imaging the target reflectors requires high resolution. Characterization and monitoring of hydrocarbon reservoirs by seismic amplitude‐versus‐offset techniques demands high pre‐stack amplitude fidelity. In this region, however, it still was not clear how the survey parameters should be chosen to satisfy the required data quality. To answer this question, we applied the focal‐beam method to survey evaluation and design. This subsurface‐ and target‐oriented approach enables quantitative analysis of attributes such as the best achievable resolution and pre‐stack amplitude fidelity at a fixed grid point in the subsurface for a given acquisition geometry at the surface. This method offers an efficient way to optimize the acquisition geometry for maximum resolution and minimum amplitude‐versus‐offset imprint. We applied it to several acquisition geometries in order to understand the effects of survey parameters such as the four spatial sampling intervals and apertures of the template geometry. The results led to a good understanding of the relationship between the survey parameters and the resulting data quality and identification of the survey parameters for reflection imaging and amplitude‐versus‐offset applications.     

Foldback reflectors near methane hydrate bottom‐simulating reflectors: Indicators of gas distribution from 3D seismic images in the eastern Nankai Trough

Understanding of fluid behavior and gas distribution in the shallow subsurface are important considerations in gas hydrate formation and the global carbon cycle. Estimation of gas distribution based on reflection seismic surveys, however, is difficult because the boundary of a gas‐bearing zone is indistinct and not systematically defined. This study reports distinctive features related to gas‐hydrate distribution and possible fluid migration in high‐resolution 3D seismic‐reflection data from sediments of the eastern Nankai Trough. These features, here termed foldback reflectors (FBRs), descend in accordion shaped reflectors near the edges of bottom‐simulating reflectors (BSRs). FBRs generally correspond to lateral boundaries between two seismic facies, a ‘dimmed’ facies with relatively low amplitude and subdued high‐frequency components beneath the BSR and the contrasting facies around the BSR. The dimmed facies corresponds to areas of anomalously low velocity consistent with a small amount of free gas. FBR is mostly developed in well‐stratified formations in uplifted regions. Dip directions of the FBR appear to be restricted by orientation of the host formations. Edges of the FBR often correspond to high‐amplitude layers. Such occurrences of FBR suggest that regional uplift and layer‐parallel fluid migration are related to the formation of FBR as well as BSR.     

A texture‐based region growing algorithm for volume extraction in seismic data

We present a novel approach to automated volume extraction in seismic data and apply it to the detection of allochthonous salt bodies. Using a genetic algorithm, we determine the optimal size of volume elements that statistically, according to the U‐test, best characterize the contrast between the textures inside and outside of the salt bodies through a principal component analysis approach. This information was used to implement a seeded region growing algorithm to directly extract the bodies from the cube of seismic amplitudes. We present the resulting three‐dimensional bodies and compare our final results to those of an interpreter, showing encouraging results.     

Seismic preprocessing and amplitude cross-calibration for a time-lapse amplitude study on seismic data from the Oseberg reservoir

The cross‐calibration of different vintage data is an important prerequisite in attempting to determine the time‐lapse seismic effects induced by hydrocarbon production in a reservoir. This paper reports the preprocessing and cross‐calibration procedures adopted to modify the data of four seismic vintages (1982, 1989, 1992 and 1999) from the Oseberg field in the North Sea, for optimal conditions for a time‐lapse seismic amplitude analysis. The final results, in terms of time‐lapse variations, of acoustic impedance and of amplitude‐versus‐offset, are illustrated for selected data sets. The application of preprocessing to each individual vintage data set reduces the effects of the different acquisition and noise conditions, and leads to consistency in the amplitude response of the four vintages. This consistency facilitates the final amplitude cross‐calibration that is carried out using, as reference, the Cretaceous horizon reflections above the Brent reservoir. Such cross‐calibration can be considered as vintage‐consistent residual amplitude correction. Acoustic impedance sections, intercept and gradient amplitude‐versus‐offset attributes and coherent amplitude‐versus‐offset estimates are computed on the final cross‐calibrated data. The results, shown for three spatially coincident 2D lines selected from the 1982, 1989 and 1999 data sets, clearly indicate gas‐cap expansion resulting from oil production. Such expansion is manifested as a decrease in acoustic impedance and a modification of the amplitude‐versus‐offset trends in the apical part of the reservoir.     

Seismic facies analysis through musical attributes

Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.