Seismic Facies Analysis Research Articles

Seismic Facies Analysis for Lithofacies Prediction, Okam Field of Niger Delta, Nigeria

Seismic facies analysis constrained with well log information have been used to predict lithofacies distribution across the Okam Field of Niger Delta. Density and gamma ray logs were cross-plotted and the seismic section was subdivided vertically into different seismic facies. The delineated lithologies, from well logs were correlated with seismic facies signatures using lines of intersection across the wells. Gamma ray and resistivity logs were used to identify the interfaces between the lithofacies and correlated across the field. Structural interpretation was carried out. Time slices were generated and examined at different intervals within the identified reservoirs. Stratigraphic related attribute and envelope were extracted on these time slices. Three seismic facies type (facies A, B and C) were identified based on reflection amplitude, reflection patterns and continuity. Seismic facies A represents the undisturbed sediments in the Uppermost Benin Formation (parallel continuous, moderate to high reflections). Seismic facies B corresponds to the sand-shale pairs of the Agbada Formation which is diagnostic of moderate to high amplitude subparallel-parallel discontinuous reflections. The chaotic seismic facies signature (facies C) beneath this represents the overpressured mobile deformed Akata shales. The time slices show lateral variation in amplitude from high amplitude continuous reflections to low amplitude chaotic reflections from the southwest to northeast direction of the field. The extracted stratigraphic related attribute and envelope show lateral changes in amplitude from the northeast direction depicting increase in shaliness towards the northeast direction. The lateral variation in lithofacies across the field suggest varied and spatial changes in key reservoir parameters such as porosity and permeability that suggest changes in reservoir qualities across the field.

Integration of Seismic Facies and Seismic Sequence analysis for Depositional Environment Reconstruction of nandy Field, Niger Delta

An integrated seismic sequence stratigraphy and facies analysis has been carried out with a view to understanding the depositional environment and stratal stacking pattern of Nandy Field of Niger Delta. Well logs and biostratigraphic information were used to identify the lithologies, stratal stacking patterns, stratigraphic surfaces, system tracts and reservoir potentials of the field. Seismic sequence stratigraphy and seismic reflection patterns were used to identify the seismic facies. Facies-related attributes were employed to identify the continuity and amplitude of seismic events. Three seismic facies packages and three reservoir sands were identified. The environments of deposition of the area consist of marginal marine to continental depositional settings predominantly made of shoreface deposits with few channel sands. The depositional model of the area shows that the deposition went down the axis of fluvial depositional system, which transports sediments through channels down to the shelf into the base of the slope down the basin. The seismic reflection patterns in this field are parallel to divergent reflections, high-continuity and high-amplitude facies, low-amplitude facies, low-continuity and variable-amplitude reflections, chaotic reflections and wavy reflection patterns. The seismic stratigraphic analysis of this field revealed the presence of low stand system tract (LST), high stand system tract (HST) and transgressive system tract (TST). Sands 1 and 2 were delineated within high stand systems tract (HST) while sand 3 was within low stand systems tract (LST). The results of this study show that the Nandy Field was of high hydrocarbon potentials and highlight the importance of using seismic reflection parameters (reflection configuration, amplitude, frequency and continuity) in the division and classification of seismic facies.

Improved Sand characterization of Mafe Field of Niger Delta by integrated well logs information and 3D seismic data

Well log rock physics and seismic facies analysis was carried out with a view to enhancing reservoir sand characterization of Mafe Field of Niger Delta . Lithofacies were identified using suites of well logs and correlated across the block. Rock properties were estimated from wireline logs using empirical methods. Vp-porosity crossplot was used to characterize the delineated sandstone reservoirs by comparing observed clusters and trends with various rock physics models. Seismic attribute analysis was employed to detect lateral changes in lithology across the field. Reservoir A is a relatively clean sand, with low average volume of shale of 0.4, average thickness of 55m, good average porosity of 0.26 and average water saturation of 0.45. Reservoir B is also a relatively clean sand with low average volume of shale of 0.35, average thickness of 85m, high average porosity of 0.27 and average water saturation of 0.54. Reservoir C has an estimated volume of shale of 0.21 average total porosity of 0.23, and an average thickness of 70m with average water saturation of 0.65. Reservoir A conforms to the friable sand model while Vp-porosity crossplot cluster trend for both reservoir B and C show trend and properties imitating the contact cement model. The time slices extracted at different time intervals from the envelope and instantaneous frequency cubes show lateral variation in lithofacies across the delineated sandstones. Instantaneous frequency decreases from southwest to northeast which corresponds to decrease in shalines. Reservoir quality information can be predicted or even derived from the estimated petrophysical properties since these parameters such as porosity and volume of shale are sometimes closely associated with rock properties such as sorting, lithofacies and grain maturity.

Unsupervised seismic facies analysis via deep convolutional autoencoders

One of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate facies maps. Therefore, it is promising to use prestack seismic data for the seismic facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data-driven offset-temporal feature extraction approach using the deep convolutional autoencoder (DCAE). As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic facies analysis can be accomplished through the use of conventional classification or clustering techniques (e.g., K-means or self-organizing maps). Using a physical model and field prestack seismic surveys, we comprehensively determine the effectiveness of our scheme. Our results indicate that DCAE provides a much higher resolution than the conventional methods and offers the potential to significantly highlight stratigraphic and depositional information.

Adaptive phase k-means algorithm for waveform classification

Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

Crustal configuration in the northern Levant basin based on seismic interpretation and numerical modeling

Abstract The interpretation of five 2D PSTM seismic reflection sections (14 s TWT) covering the northern Levant Basin revealed a total of 10 horizons, among which, one is interpreted as an interface that may represent the Moho. The interpretation of seismic packages and their bounding surfaces as well as the seismic facies analysis were constrained by published 2D seismic interpretations of the northern Lebanese offshore. A total of nine seismic packages are identified in the basin with ages varying from the Mid Jurassic to the Quaternary. The filling of the basin is made up of thick Cenozoic and Mesozoic strata deposited above rifted Triassic – Early Jurassic interval. The sediments are deposited in deep water mixed-settings resulting from high-stand systems (various types of carbonate platforms) and low-stand systems (siliciclastic and carbonate deep-water turbidite complexes). Carbonate and siliciclastic systems are sealed by 1–1.5 km of evaporites, and underlie Plio-Quaternary hemipelagic and pelagic sediments intercalated by turbiditic sheets. The time horizons were converted into depth using two methods; the first one is based on stacking velocities and the second one on velocities resulting from refraction data. 2D crustal modeling was achieved by integrating free-air gravity anomaly, geoid heights and topography data on the five interpreted PSTM seismic lines. The models representing five sections across the northern Levant Basin, show a progressively attenuated crystalline crust in an EW direction (away from the basin's eastern margin). The crystalline crust is best interpreted as a strongly thinned continental crust under the Levant Basin, represented by two distinct components, an upper and a lower continental crust. The Moho appears to be situated between 20 and 23 km in the central and southern Lebanese offshore. Estimated surface heatflow in the basin is around 40 mW/m2, which is lower than reported values for the onshore and the margin. These differences in heatflow values between the offshore, the margin and the onshore have an important impact on hydrocarbon maturation and assessment of potential petroleum systems.

Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights

With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition-driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and the geologic target of interest. However, such an approach may overlook unsuspected or subtle features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of candidate attributes that best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on their response from the unsupervised learning algorithm and the interpreter’s knowledge. In other words, we expect the weights to represent “which attribute is ‘favored’ by an interpreter as input for unsupervised learning” from an interpretation perspective and “which attribute is ‘favored’ by the learning algorithm” from a data-driven perspective. Therefore, we claim the weights are user guided and data adaptive, as the derivation of weight for each input attribute is embedded into the learning algorithm, providing a specific measurement tailored to the selected learning algorithm, while still taking the interpreter’s knowledge into account. We develop our workflow using Barnett Shale surveys and an unsupervised self-organizing map seismic facies analysis algorithm. We found that the proposed weighting-based attribute selection method better differentiates features of interest than using equally weighted input attributes. Furthermore, the weight values provide insights into dependency among input attributes.

Supervised seismic facies analysis based on image segmentation

Seismic facies analysis aims to characterize the seismic expression of a reservoir by placing seismic attributes into classes that are presumably related to geologic heterogeneities. Supervised seismic facies analysis can be used to classify the seismic expression of reservoirs in oil field development using well control as the training samples. Manual seismic facies analysis is often subjective, and it is usually time-consuming. Pattern recognition techniques such as support vector machine and neural networks have been used to automate seismic facies analysis. However, they often do not produce satisfactory spatial continuity in their results. The spurious spatial discontinuities are usually caused by the so-called “over-fitting” problem inherent in these methods. To avoid this issue, we have developed a supervised seismic facies analysis method based on image segmentation that promotes spatial continuity of the classified seismic facies. The seed region growing technique borrowed from image segmentation is used to segment attribute maps outward from points of well control. In addition, we calculate the seismic facies probability distribution at every grid point in the seismic image. We use this probability distribution as a weighting coefficient to allow regions to grow anisotropically. As demonstrated by the results on a field data set, this approach improves spatial continuity and indicates promising results.

SeisART software: seismic facies analysis by contributing interpreter and computer

In this paper, an interpreter computer interactive software, named SeisART, is introduced which is employed for seismic facies analysis. Seismic facies analysis is considered as a technique for mapping geological changes using seismic data. In recent years, the number of seismic attributes and the size of seismic data have been increased. Therefore, the interpretation of seismic facies has become a more elusive issue. In a seismic facies analysis procedure, there are three main steps: (i) selecting appropriate attributes as the feature extraction task, (ii) defining the proper number of clusters, and (iii) utilizing an appropriate pattern recognition method. Interpreter plays a remarkable role in performing these steps, based on his/her knowledge, and also available tools and geological information. SeisART as a package-deal software includes numerous methods of feature extraction, validation, and pattern recognition. In a user-friendly environment, the interpreter is able to employ these utilities along with well-designed visualization tools for choosing the best methods to obtain and compare numerous results. Besides, owing to the uncertainty of seismic data, SeisART employs the Fuzzy system to gain more confidence with the seismic facies interpretation.

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Occurrence of submarine canyons, sediment waves and mass movements along the northern continental slope of the South China Sea

In this study, we reveal a series of newly discovered submarine canyons, sediment waves, and mass movements on a flat and smooth seafloor using high-resolution, multi-beam bathymetry and shallow seismic surveys along the northern slope of the South China Sea. We also describe their geomorphology and seismic stratigraphy characteristics in detail. These canyons display U-shaped cross sections and are roughly elongated in the NNW–SSE direction; they are typically 8–25 km long, 1.2–7 km wide, and form incisions up to 175 m into Pliocene–Quaternary slope deposits at water depths of 400–1000 m. Slide complexes and the sediment wave field are oriented in the NE–SW direction and cover areas of approximately 1790 and 926 \(\hbox {km}^{2}\), respectively. Debris/turbidity flows are present within these canyons and along their lower slopes. Detailed analysis of seismic facies indicates the presence of six seismic facies, in which Cenozoic strata located above the acoustic basement in the study area can be roughly subdivided into three sequences (1–3), which are separated by regional unconformities (Tg, T4, and T3). By combining these data with the regional geological setting and the results of previous studies, we are able to determine the genetic mechanisms used to create these canyons, sediment wave field, and mass movements. For example, frontally confined slide complexes could have been influenced by high sedimentation rates and high pore pressures. A series of very large subaqueous sediment waves, which record wavelengths of 1.4–2 km and wave heights of 30–50 m, were likely produced by interactions between internal solitary waves and along-slope bottom (contour) currents. Canyons were likely initially created by landslides and then widened laterally by the processes of downcutting, headward erosion, and active bottom currents and debris/turbidity flows on canyon floors. We therefore propose a three-dimensional model to describe the development of these mass movements, the sediment wave field, and canyons. The four stages of this model include a stable stage, followed by the failure of the slope, and subsequent formations of the sediment wave field and canyons.

Modeling the facies of reservoir using seismic data with missing attributes by dissimilarity based classification

Using seismic attributes as features for classification in feature space, in various aims such as seismic facies analysis, is conventional for the purpose of seismic interpretation. But sometimes seismic data may have no attributes or it is hard to define a small and relevant set of attributes in some applications. Therefore, employing techniques that perform facies modeling without using attributes is necessary. In this paper we present a new method for facies modeling of seismic data with missing attributes that called dissimilarity based classification. In this method, classification is based on dissimilarities and facies modeling will be done in dissimilarity space. In this space dissimilarities consider as new features instead of real features. A support vector machine as a powerful classifier was employed in both feature space (feature-based) and dissimilarity space (feature-less) for facies analysis. The proposed feature-less and feature-based classification is applied on a real seismic data from an Iranian oil field. Facies modeling using seismic attributes provide better results, but the feature-less classification outcome is also satisfactory and the facies correlation is acceptable. Indeed, the power of attributes to discriminate different facies causes to that facies analysis using attributes provide more reliable results comparing to featureless facies analysis.

Biostratigraphy and seismic data analysis to detect the sequence stratigraphic depositional environment of the Miocene succession: Gulf of Suez (Egypt)

This work comprises a study of the sequence stratigraphy, seismic-facies analysis, biostratigraphy and depositional environments of the northern part of the Gulf of Suez, Egypt, using a set of 24 3D seismic profiles, composite logs and sonic logs from ten wells. The syn-rift formations in the studied ten wells are described lithologically and interpreted based on investigating two seismic profiles. Biostratigraphically, the Miocene fossils are identified to correlate the five planktonic foraminiferal biozones in the examined boreholes (RB-A1, RB-B1, RB-B3, EE85-2 and RB-C1). The sequence stratigraphic analysis suggests that the Miocene succession can be subdivided into two major third order depositional sequences (S1 and S2) separated by the three major sequence boundaries (DSB1, DSB2 and DSB3).

The occurrence and transformation of lacustrine sediment gravity flow related to depositional variation and paleoclimate in the Lower Cretaceous Prosopis Formation of the Bongor Basin, Chad

Bed variability of sediment-gravity-flow deposits is quite prevalent in deep-marine settings, but it has not been well investigated in lacustrine settings. The depositional characteristics of various event beds are characterized in the North Slope Belt of the Bongor Basin (Chad), using detailed sedimentological, petrographic, geochemical as well as palynological analysis. Four bed types including classical turbidite bed, debrite bed, hybrid event bed, and hyperpycnite bed were distinguished based on their interpreted depositional processes. Variable mud contents of debrite beds and classic turbidite beds show distinct genetic characteristics in four core wells, whilst the high mud content of cohesive debrite interval and the low mud content of turbidite interval in hybrid event bed demonstrate the existence of flow transformation. Generally, several trace element and rare earth element proxy parameters show that these gravity-flow deposits of BS1-1 and D-3 cores are formed in more distal depositional settings than them of BN8 and BNE3 cores, which is also well consistent with sedimentological understandings achieved by seismic facies analysis. Although palynological results show a general hot arid climate during the deposition of the Prosopis Formation, but the climate-sensitive Sr/Cu ratio demonstrates that most sampled turbidite beds are postulated to be formed within a short humid pulse. The multi-approach analysis has illustrated that two potential forming mechanisms (delta-front-failure and flood-related origin) can be derived in different cored wells of such a small lacustrine rift basin. Differentiated flow transformation plays a significant role in the depositional characteristics and heterogenetic distribution of these event deposits.

Characteristics of Meso-Cenozoic Igneous Complexes in the South Yellow Sea Basin, Lower Yangtze Craton of Eastern China and the Tectonic Setting

The South Yellow Sea Basin is partially surrounded by the East Asian continental Meso-Cenozoic widespread igneous rocks belt. Magnetic anomaly and multi-channel seismic data both reveal the prevalent occurrence of igneous rocks. We preliminarily defined the coupling relation between magnetic anomalies and igneous rock bodies. Some igneous complexes were also recognized by using multi-channel seismic and drilling data. We identified various intrusive and extrusive igneous rock bodies, such as stocks, sills, dikes, laccoliths and volcanic edifice relics through seismic facies analysis. We also forecasted the distribution characteristics of igneous complexes. More than fifty hypabyssal intrusions and volcanic relics were delineated based on the interpretation of magnetic anomaly and dense intersecting multi-channel seismic data. It is an important supplement to regional geology and basin evolution research. Spatial matching relations between igneous rock belts and fractures document that extensional N-E and N-NE-trending deep fractures may be effective pathways for magma intrusion. These fractures formed under the influence of regional extension during the Meso-Cenozoic after the Indosinian movement. Isotopic ages and crosscutting relations between igneous rock bodies and the surrounding bedded sedimentary strata both indicate that igneous activities might have initiated during the Late Jurassic, peaked in the Early Cretaceous, gradually weakened in the Late Cretaceous, and continued until the Miocene. Combined with previous studies, it is considered that the Meso-Cenozoic igneous activities, especially the intensive igneous activity of the Early Cretaceous, are closely associated with the subduction of the Paleo-Pacific Plate.

Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification

Pattern recognition-based seismic facies analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps (SOMs) as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute facies analysis begins by correlating a suite of candidate attributes to a desired output, keeping those that correlate best for subsequent analysis. The analysis then takes place in attribute space rather than ([Formula: see text], [Formula: see text], and [Formula: see text]) space, removing spatial trends often observed by interpreters. We add a stratigraphy layering component to a SOM model that attempts to preserve the intersample relation along the vertical axis. Specifically, we use a mode decomposition algorithm to capture the sedimentary cycle pattern as an “attribute.” If we correlate this attribute to the training data, it will favor SOM facies maps that follow stratigraphy. We apply this workflow to a Barnett Shale data set and find that the constrained SOM facies map shows layers that are easily overlooked on traditional unconstrained SOM facies map.

Seismic facies analysis based on speech recognition feature parameters

Seismic facies analysis plays an important role in seismic stratigraphy. Seismic attributes have been widely applied to seismic facies analysis. One of the most important steps is to optimize the most sensitive attributes with regard to reservoir characteristics. Using different attribute combinations in multidimensional analyses will yield different solutions. Acoustic waves and seismic waves propagating in an elastic medium follow the same law of physics. The generation process of a speech signal based on the acoustic model is similar to the seismic data of the convolution model. We have developed the mel-frequency cepstrum coefficients (MFCCs), which have been successfully applied in speech recognition, as feature parameters for seismic facies analysis. Information about the wavelet and reflection coefficients is well-separated in these cepstrum-domain parameters. Specifically, information about the wavelet mainly appears in the low-domain part, and information about the reflection coefficients mainly appeared in the high-domain part. In the forward model, the seismic MFCCs are used as feature vectors for synthetic data with a noise level of zero and 5%. The Bayesian network is used to classify the traces. Then, classification accuracy rates versus different orders of the MFCCs are obtained. The forwarding results indicate that high accuracy rates are achieved when the order exceeds 10. For the real field data, the seismic data are decomposed into a set of MFCC parameters. The different information is unfolded in the parameter maps, enabling the interpreter to capture the geologic features of the target interval. The geologic features presented in the three instantaneous attributes and coherence can also be found in the MFCC parameter maps. The classification results are in accordance with the paleogeomorphy of the target interval as well as the known wells. The results from the synthetic data and real field data demonstrate the information description abilities of the seismic MFCC parameters. Therefore, using the speech feature parameters to extract information may be helpful for processing and interpreting seismic data.

Seismic facies analysis through musical attributes

ABSTRACTSeismic 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.

Multi-waveform classification for seismic facies analysis

Seismic facies analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic facies, which does not consider the stratigraphy continuity, and the final facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic facies analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic facies analysis.

Seismic expression of depositional elements associated with a strongly progradational shelf margin: northern Santos Basin, southeastern Brazil

ABSTRACT: Seismic facies analysis and seismic geomorphology are important tools for the analysis of depositional elements in subsurface. This paper aimed to investigate the character and genesis of depositional elements and erosive features associated with an Eocene progradational shelf margin in northern Santos Basin. Identified seismic facies are interpreted as shelf-margin deltas/shoreface deposits, tangential (oblique) clinoforms, sigmoidal clinoforms, topset reflectors, mass-transport deposits and turbidites. These facies are grouped into four associations representing periods of relatively constant environmental conditions. Association 1 is composed of shelf-margin deltas/shoreface deposits, tangential clinoforms and extensive sand-rich turbidites disposed as submarine channels and frontal splays. A progressive increase in clinoform angle within this association has been identified, culminating in high-relief sigmoidal clinoforms with less voluminous turbidites of facies association 2. Association 3 is composed by subparallel to divergent topset reflectors, interpreted as continental to shelfal deposits placed during base-level rises. These are always truncated basinward by slump scars, formed as a consequence of sediment overload at the shelf margin during aggradations. Association 4 is composed of sigmoidal clinoforms, mass-transport deposits and turbidites. Early clinoforms are steeper as a consequence of the topography of the slump scars. Subsequently, dip angles become progressively gentler as the system approach to the equilibrium profile. The steep physiography was favorable for canyon incision, which played an important role in turbidite deposition. Mass-transport deposits, formed subsequent to slope collapse, are composed of mud-rich diamictites, and show strong internal deformation.