Academic literature on the topic 'Stratigraphic Seismic reflection method'

Here, I provide an historical summary of seismic stratigraphy and suggest some potential avenues for future collaborative work between sedimentary geologists and geophysicists. Stratigraphic interpretations based on reflection geometry- or shape-based approaches have been used to reconstruct depositional histories and to make qualitative and (sometimes) quantitative predictions of rock physical properties since at least the mid-1970s. This is the seismic stratigraphy that is usually practiced by geology-focused interpreters. First applied to 2D seismic data, interest in seismic stratigraphy was reinvigorated by the development of seismic geomorphology on 3D volumes. This type of reflection geometry/shape-based interpretation strategy is a fairly mature science that includes seismic sequence analysis, seismic facies analysis, reflection character analysis, and seismic geomorphology. Rock property predictions based on seismic stratigraphic interpretations usually are qualitative, and reflection geometries commonly may permit more than one interpretation. Two geophysics-based approaches, practiced for nearly the same length of time as seismic stratigraphy, have yet to gain widespread adoption by geologic interpreters even though they have much potential application. The first is the use of seismic attributes for “feature detection,” i.e., helping interpreters to identify stratigraphic bodies that are not readily detected in conventional amplitude displays. The second involves rock property (lithology, porosity, etc.) predictions from various inversion methods or seismic attribute analyses. Stratigraphers can help quality check the results and learn about relationships between depositional features and lithologic properties of interest. Stratigraphers also can contribute to a better seismic analysis by helping to define the effects of “stratigraphy” (e.g., laminations, porosity, bedding) on rock properties and seismic responses. These and other seismic-related pursuits would benefit from enhanced collaboration between sedimentary geologists and geophysicists.

Abstract Seismic artifacts due to random and linear noises, low fold coverage, statics, and spatial aliasing are frequently affecting uncertainties in seismic interpretation. Several conventional methods, such as median filter, have been implemented to reduce random noises. However, this method can not be utilized for the area in which rich with stratigraphic features such as clinoforms and in the area with strong dips. We implemented layer-steered filter in order to attenuate random noises in this kind of situation. Layer-steered filter has ability to attenuate random noises but still respects to local dip events; therefore, the method provides better preservation of events and stratigraphics compared to other conventional methods such as median filter and dip-steered filter.

Seismic sequences are stratigraphic units of relatively conformable seismic reflections. These units are intervals of similar sedimentation conditions, governed by sediment supply and relative sea level, and they are key elements in understanding the evolution of sedimentary basins. Conventional seismic sequence analyses typically rely on human interpretation; consequently, they are time-consuming. We have developed a new data-driven method to identify first-order stratigraphic units based on the assumption that the seismic units honor a layer-cake earth model, with layers that can be discriminated by the differences in seismic reflection properties, such as amplitude, continuity, and density. To identify stratigraphic units in a seismic volume, we compute feature vectors that describe the distribution of amplitudes, texture, and two-way traveltime for small seismic subvolumes. Here, the seismic texture is described with a novel texture descriptor that quantifies a simplified 3D local binary pattern around each pixel in the seismic volume. The feature vectors are preprocessed and clustered using a hierarchical density-based cluster algorithm in which each cluster is assumed to represent one stratigraphic unit. Field examples from the Barents Sea and the North Sea demonstrate that the proposed data-driven method can identify major 3D stratigraphic units without the need for manual interpretation, labeling, or prior geologic knowledge.

The new rules of the game in hydrocarbon exploration demand an exact positioning of the seismic markers in order to define the geometry of the targets more than ever before. However, the degree of success will depend to a great extent on how accurately the amplitude of reflection coefficients can be estimated.These new requirements mean that all stages of traditional seismic processing have to be critically evaluated. It can be seen, in particular, when assessing existing deconvolution methods for seismic processing, that they are often ill-conditioned to problems posed by the targets of stratigraphic exploration or by reservoir seismic prospecting. The amplitude of the reflectivity function is often estimated inaccurately.The approach described in this paper abandons the usual hypothesis (white reflectivity spectra) made by deconvolution methods and employs as alternative information the lateral redundancies which are always present on a seismic section. Our method first estimates the location of high amplitude reflectors with good lateral continuity, by means of an elegant automatic picking program. Based on these locations, a generalized inversion can be used to yield the wavelet emitted by the source, and the amplitude of the main reflection coefficients simultaneously for each trace. All the reflection coefficients are then estimated using the amplitudes and the wavelets computed previously.The various stages of this method which is called Deconvolution-Inversion, developed by Total Compagnie Française des Pétroles, are illustrated in the paper by means of both synthetic and real examples. The ability of the method to preserve the amplitudes makes it a powerful tool for stratigraphic and reservoir seismic prospecting purposes.

Focal-time analysis is a straightforward data-driven method to obtain robust stratigraphic depth control for microseismicity or induced seismic events. The method eliminates the necessity to build an explicit, calibrated velocity model for hypocenter depth estimation, although it requires multicomponent 3D seismic data that are colocated with surface or near-surface microseismic observations. Event focal depths are initially expressed in terms of zero-offset focal time (two-way P-P reflection time) to facilitate registration and visualization with 3D seismic data. Application of the focal-time method requires (1) high-quality P- and S-wave time picks, which are extrapolated to zero offset and (2) registration of correlative P-P and P-S reflections to provide [Formula: see text] and [Formula: see text] time-depth control. We determine the utility of this method by applying it to a microseismic and induced-seismicity data set recorded with a shallow-borehole monitoring array in Alberta, Canada, combined with high-quality multicomponent surface seismic data. The calculated depth distribution of events is in good agreement with hypocenter locations obtained independently using a nonlinear global-search method. Our results reveal that individual event clusters have distinct depth distributions that can provide important clues about the mechanisms of fault activation.

A basis pursuit inversion of seismic reflection data for reflection coefficients is introduced as an alternative method of incorporating a priori information in the seismic inversion process. The inversion is accomplished by building a dictionary of functions representing reflectivity patterns and constituting the seismic trace as a superposition of these patterns. Basis pursuit decomposition finds a sparse number of reflection responses that sum to form the seismic trace. When the dictionary of functions is chosen to be a wedge-model of reflection coefficient pairs convolved with the seismic wavelet, the resulting reflectivity inversion is a sparse-layer inversion, rather than a sparse-spike inversion. Synthetic tests suggest that a sparse-layer inversion using basis pursuit can better resolve thin beds than a comparable sparse-spike inversion. Application to field data indicates that sparse-layer inversion results in the potentially improved detectability and resolution of some thin layers and reveals apparent stratigraphic features that are not readily seen on conventional seismic sections.

Multiple scattering in finely layered sediments is important for interpreting stratigraphic data, matching well-log data with seismic data, and seismic modeling. Two methods have been used to treat this problem in seismic applications: the O’Doherty-Anstey approximation and Backus averaging. The O’Doherty-Anstey approximation describes the stratigraphic-filtering effects, while Backus averaging defines the elastic properties for an effective medium from the stack of the layers. It is very important to know when the layered medium can be considered as an effective medium. In this paper, we only investigate vertical propagation. Therefore, no anisotropy effect is taken into consideration. Using the matrix-propagator method, we derive equations for transmission and reflection responses from the stack of horizontal layers. From the transmission response, we compute the phase velocity and compare the zero-frequency limit with the effective-medium velocity from Backus averaging. We also investigate how the transition from time-average medium to effective medium depends on contrast; i.e., strength of the reflection-coefficient series. Using numerical examples, we show that a transition zone exists between the effective medium (low-frequency limit) and the time-average medium (high-frequency limit), and that the width of this zone depends on the strength of the reflection-coefficient series.

The seismic profiles analysis of 4,533 km study area made it possible to study the sedimentary deposits in the Ivorian onshore basin. The method used consisted of manual plots of the seismic sections leading to the production of isochronos, iso-velocity, isobaths and isopac maps. As for the stratigraphic interpretation, it was used to develop a sedimentary model to extract information on the nature of sedimentary deposits and the mechanisms of their establishment based on the analysis of seismic facies. Examination of the different seismic profiles of the study area allowed the onshore sedimentary series to be subdivided into four main sequences which are: sequences I, II, III and IV. Thus, this analysis revealed two stages of sedimentary deposits linked to the behavior of the reflectors: 1. a syn-rift stage, characterized by significant fracturing in the sedimentation with faults and tilted blocks inthe Lower Cretaceous 2. a post-rift stage , corresponding to a less deformed sedimentation with parallel and continuous reflectors from the Upper Cretaceous to the present . These two phases allow us to understand the stratigraphic evolution of the onshore basin.

Although oil exploration has been performed in the Eastern Desert of Egypt for over a century, seismic reflection techniques have only been in use for less than a fourth of that time. In an effort to improve seismic imaging of geologic targets, many styles of acquisition and processing have been tested, accepted, or discarded. Over the last twenty‐four years, seismic data acquisition has evolved from low‐channel analog to high‐channel digital recordings. The most difficult exploration problems encountered in these efforts have been the low‐frequency and high‐energy ground roll and depth of penetration when imaging the oil producing Pre‐Miocene sandy reservoirs below the highly reflective salt and evaporites. Efforts have been focused on developing seismic processing procedures to enhance the seismic data quality of recently acquired seismic data and developing new acquisition methods to improve seismic data through acquisition and processing. In older acquisition, the new processing has improved the seismic quality (vertical and lateral resolution), but it still retains a low‐frequency character. In the newly acquired seismic data, however, there is improved reflection continuity, depth of penetration, and resolution. We attribute this result to the change from low‐fold (6–24 fold), long receiver and source patterns (50 to 222 m) to high fold (96 fold) short receiver and source group (25 m), and spectral balancing in the processing. The most recent acquisition and processing have greatly improved the quality of the shallow seismic reflections and the deeper reflections that have helped unravel the structural and stratigraphic style of the deeper portions of the basin.

Stratigraphic filtering (SF), or short-period multiples, is prominent in cyclically stratified sedimentation with large impedance contrasts that result in normal-incident reflection magnitudes greater than 0.5. Because SF attenuates and delays the propagating wavelet, similar to the effects of [Formula: see text] attenuation, the integrity of well ties is often jeopardized. A method is proposed to obtain better well ties in areas with severe SF. Starting with a well-log acoustic impedance curve, two-way transmitted wavefields and their equivalent inverse filters are generated at each time sample. Because a time-varying convolution of the transmitted wavefields with the primary-only reflectivity yields the multiple reflectivity, a time-varying deconvolution of the multiple synthetic with the inverse filters yields the primary-only reflectivity. In essence, when the multiple synthetic matches the near-angle stack at a well location, the near-angle stack is deconvolved in a time-varying fashion to match the primary-only synthetic, which then constitutes a correlation with the acoustic impedance yielding a good well tie. This new well-tie technique preserves the integrity of the lithologic interpretation because stretching and squeezing the time scale of the primary-only synthetic to force a seismic match are avoided. Our well-tie method is applied to the synthetic and field data from Cooper Basin, Australia, where more than 30 coal beds are observed within a 1000 ft (304 m) interval.