Seismic Facies Interpretation Research Articles

Revisiting the rocks discovers turbidites and a new exploration play in the Penola Trough, Otway Basin, South Australia

As part of an integrated geological, geochemical, and geophysical study, conventionally cored intervals from the lower-most stratigraphic section of the onshore South Australian part of the Penola Trough, Otway Basin have been reviewed and reinterpreted. Three conventionally cored wells; Bungaloo-1, Jolly-1ST1 and Sawpit-2, were described using a quick-look approach to determine gross lithological and sedimentological information. Seven lithology types were observed: clay-siltstone with horizontal lamination, diamictite (claystone with horizontal lamination and dropstones), clay-siltstone with massive-horizontal lamination, silt-sandstone with horizontal lamination, sandstone with ripple lamination, sandstone with parallel lamination and sandstone with massive bedding ± rip-up clasts. The first two lithologies are interpreted as representing deposition in a cold climate, profundal-lacustrine depositional environment. The first, containing interpreted varves is the most distal and the second, containing dropstones indicates relatively colder freeze/thaw conditions. The last five lithologies are interpreted as lacustrine turbidite depositional environments, specifically Bouma sequences Te, Td, Tc, Tb and Ta, respectively. Seismic facies interpretation has identified canyon-like and mound features and interpreted them as slope-feeder channels and fan/s respectively, at and away from well control. This rock-based turbidite interpretation provides an alternative to the current alluvial channel/crevasse splay/floodplain lake model for reservoir deposition and as such presents a new exploration play. Further the reservoirs’ immediate lateral association with gas/oil-prone source rocks and seals in the clay-rich lithologies potentially provide for stratigraphic trapping in addition to the previously targeted fault-controlled structural traps.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation, there has been continuous development in the field of seismic facies interpretation using convolutional neural networks. These intelligent and automated methods significantly reduce manual labor, particularly in the laborious task of manually labeling seismic facies. However, the extensive demand for training data imposes limitations on their wider application. To overcome this challenge, we adopt the UNet architecture as the foundational network structure for seismic facies classification, which has demonstrated effective segmentation results even with small-sample training data. Additionally, we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range. The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries. Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results, as evidenced by various evaluation metrics for image segmentation. Obviously, the classification accuracy reaches an impressive 96%. Furthermore, the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method, which accurately defines the range of different seismic facies. This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Active learning with deep autoencoders for seismic facies interpretation

Machine-learning (ML)-assisted seismic interpretation tasks require large quantities of labeled data annotated by expert interpreters, which is a costly and time-consuming process. Whereas existing works to minimize dependence on labeled data assume the data annotation process to already be completed, active learning — a field of ML — works by selecting the most important training samples for the interpreter to annotate in real time simultaneously with the training of the interpretation model itself, resulting in high levels of performance with fewer labeled data samples than otherwise possible. Whereas there exists significant literature on active learning for classification tasks with respect to natural images, there exists very little to no work for dense prediction tasks in geophysics such as interpretation. We have developed a unique and first-of-its-kind active learning framework for seismic facies interpretation using the manifold learning properties of deep autoencoders. By jointly learning representations for supervised and unsupervised tasks and then ranking unlabeled samples by their nearness to the data manifold, we can identify the most relevant training samples to be labeled by the interpreter in each training round. On the popular F3 data set, we obtain close to a 10% point difference in terms of the interpretation accuracy between the proposed method and the baseline with only three fully annotated seismic sections.

Possible geological interpretation of the volcanic seismic facies based on volcanostratigraphy elements: A case analysis of the Yingcheng Formation in the Changling Fault Depression, Songliao Basin, NE China

Volcanic rocks in faulted depressions are one of the most interested areas for hydrocarbon exploration. In a frontier basin exploration, integrated interpretation of the seismic facies of volcanic rocks is a key success. The key information of boundary system and deposited unit in the volcanostratigraphy did not attract sufficient attention during the process of seismic stratigraphic interpretation of volcanic rocks. In this paper, the volcanic rocks of the Yingcheng Formation in the Changling Fault Depression of the Songliao Basin are taken as an example. According to the maximum average slope, the volcanic seismic facies were divided into three types: mounded seismic facies unit (MSFU), tabular seismic facies unit (TSFU), and mounded-tabular seismic facies unit (M-TSFU). MSFU consists of lava dome and simple lava flows, which has fewer reservoir layers, medium to poor reservoir quality with limited extension. The TSFU comprises more complex component of simple lava flow and base surges, and contains more layers, with good-medium reservoir quality and a large reservoir extension. The M-TSFU has numerous reservoir layers with good reservoir quality and a significant reservoir extension, as well as a higher proportion of braided lava flows and pyroclastic flows. Going from the most favorable reservoir targets to the least favorable, the following units were deposited in the Changling Fault Depression: pyroclastic flow, braided lava flow, base surge, simple lava flow, lava dome, and reworked volcanic clastic flow. The study suggested that mounded-tabular facies units are potential favorable exploration targets in the Songliao Basin. This paper provides a technical route for seismic facies interpretation of volcanic rocks and the analysis of favorable volcanic exploration targets in faulted volcanic basins.

Seismic Facies Visualization Analysis Method of SOM Corrected by Uniform Manifold Approximation and Projection

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As a common seismic facies visualization analysis method, SOM projects the waveform or seismic attribute vectors into a two-dimensional topological plane in a nonlinear way, which can effectively and efficiently discover the topological structure of a dataset. SOM does not need to set the number of classes in prior, and has friendly visualization characteristics and excellent generalization, which are conducive to seismic facies interpretation using unlabeled data. However, due to the competitive learning used in SOM and the imbalance of data distribution in real world, the samples from majority classes are expanded on the topological plane and the minority classes are compressed. As a result, the plane cannot accurately describe the global structure of data distribution. To improve the visualization precision by modifying the topological relationship of the prototype vectors of SOM, we utilize UMAP (Uniform Manifold Approximation and Projection), a novel manifold learning technique for dimension reduction, to correct the prototype vectors generated by SOM. By combing the advantages of SOM and UMAP in the representation of data topological structure, the global relationship between seismic data samples can be properly established, and the internal relative spatial structure of majority class samples can be retained as much as possible, resulting in a more reliable classification. Meanwhile, the framework maintains the advantages of SOM in visualization. In the modeling tests and real data experiments, we have demonstrated the effectiveness and rationality of UMAP-SOM on the spatial structure representation of three-dimensional seismic data</i> .

A novel workflow for geothermal exploration: 3D seismic interpretation of biohermal buildups (Upper Jurassic, Molasse Basin, Germany)

The Upper Jurassic carbonates are the prime target for deep geothermal exploration in the Molasse basin, South Germany. The carbonates have a thickness of over 500m (1640 ft) and consist of two major facies: (1) bedded marly limestone and (2) massive limestone and dolostone. The massive limestone facies is composed of sponge-microbial biohermal buildups It is considered the main geothermal reservoir facies. Only this facies type may be (1) karstified, (2) dolomitized, and/or (3) faulted and fractured, and therefore can yield very high flow rates of >100 l/sec = 26 gps. The main data source used in this study is the 3D seismic survey of the Freiham geothermal field in the western part of Munich/Germany. Blended in were cutting logs to describe the lithology from 2 wells and borehole image logs from the two geothermal wells. Lithologies derived from these wells were upscaled in support of the seismic interpretation. The study presents an integrated workflow of 3D seismic attribute analysis to analyze the distribution and quantification of reservoir facies (massive limestone) versus non-reservoir facies (bedded marly limestone) per time slice. The attribute “sum of magnitude” is mapped for 9-time slices based on the vertical resolution of the Freiham 3D cube. The seismic facies interpretation is compared with upscaled borehole image facies associations of two geothermal wells. BHI log data is calibrated with an interpretation of the depositional environment based on cutting analysis Reservoir geometries were derived from an outcrop analog study to better understand the 3D seismic facies interpretation and to construct the conceptual depositional model of the Upper Jurassic carbonates. This technique is commonly used in hydrocarbon exploration but is not yet adapted to geothermal projects, which are often based on little data, smaller company sizes, tight budgets, and limited access to specialized geomodelling software and experience. The approach of using 3D seismic attribute analysis presented in this study provides a quantitative subsurface model of geothermal reservoir facies in the Freiham geothermal field. It is quick and straightforward and can easily be applied in the exploration workflow for similar fields and reservoirs.

Spark map reduce based framework for seismic facies classification

Seismic facies analysis provides an efficient way to identify the structure and geology of reservoir units. In the past two decades, seismic attributes have been widely used to highlight the geological features of interest. The combined benefits of multiple seismic attributes have been explored to estimate seismic events accurately. However, with the increase in the number of seismic attributes and size of seismic data, the traditional methods of seismic facies interpretation become very difficult and time-consuming. To address this issue, several computer-assisted facies classification algorithms including k-Means, self-organizing map and generative topographic map were introduced to capture the spatial distribution of seismic facies. The automated facies classification methods accurately quantify the characteristics of geological facies. However, these methods are computationally very time-expensive. To resolve this problem, we provide Spark Map-Reduce based framework for unsupervised classification of seismic facies. The study aims to reduce the computational time complexity of various existing unsupervised facies classification techniques. Further, to demonstrate the applicability, the proposed parallel frameworks are applied to the seismic dataset of Dutch Sector (North Sea) and the classification results are compared in terms of run-time complexity and accuracy.

Unsupervised seismic facies classification using deep convolutional autoencoder

With the increased size and complexity of seismic surveys, manual labeling of seismic facies has become a significant challenge. Application of automatic methods for seismic facies interpretation could significantly reduce the manual labor and subjectivity of a particular interpreter present in conventional methods. A recently emerged group of seismic interpretation techniques is based on deep neural networks. These approaches are data-driven and require large labeled data sets for network training. We have developed a deep convolutional autoencoder for unsupervised seismic facies classification, which does not require manually labeled examples. The facies maps are generated by clustering the deep-feature vectors obtained from the input data. Our method yields accurate results on real data and provides them instantaneously, which allows an interpreter to identify the dominant seismic features. The proposed approach opens possibilities to analyze geologic patterns in real time without human intervention.

Research on sedimentary facies of Guantao Formation in Shanjiasi area of Dongying depression, China

Alluvial fan and Fluvial facies sand bodies are important reservoirs for oil and gas resources. In this study, the sedimentary characteristics and evolutionary patterns of Alluvial fan and fluvial deposits in the Neogene Guantao Formation of the Dongying Sag, Bohai Bay Basin were analyzed using cores, well logs and three-dimensional seismic data, and the quantitative information of the sand bodies in different deposits were characterized with dense well-net constraints. Through the calibration between the core and wireline logging data, the developmental characteristics of the sand bodies in the Alluvial fan and fluvial deposits were clearly defined base on sedimentary microfacies and seismic facies interpretation. Using the RMS amplitude attribute and reconstructed Wave Impedance Inversion, the sand bodies of Guantao Formation in this area were carefully described and studied, and the effective reservoir range was predicted on the basis of fully understanding the oil test data. Results: The top and bottom interface of Guantao Formation are characterized by strong continuous reflection on seismic section, exist within the guantao on seismic section three continuous or intermittent peaks, from bottom to top respectively represent the interface between Ng4 and Ng3 sand group, the interface between Ng3 and Ng2 sand group,the interface between Ng2 and Ng1 of the Guantao Formation, and Ng4 sand group of the Guantao Formation is the key layer; The boundary Growth Fault and Binxian high jointly controlled the deposition of Guantao Formation. The sedimentary thickness of the strata decreased on the north and west sides of the sag, and increased from the core to the edge of the Binxian high; The sedimentary facies of Guantao Formation in the study area can be divided into three types: alluvial fan facies, braided river facies and meandering river facies, and various corresponding subfacies and microfacies can be subdivided into different types. Ng4 sand group of the Guantao Formation is identified as alluvial fan-braided river model; Based on the analysis of the distribution and physical properties of alluvial and fluvial facies sand bodies in the study area, it is considered that the effective reservoir of Ng4 sand group of the Guantao Formation is mainly developed in the nose structure around Binxian high, and is semicircular in distribution.

Evidence for deeply buried, oil-prone source rocks in the Baiyun depression, Pearl River Mouth Basin, northern South China Sea

Carbon isotope analyses of fluid inclusion gases trapped in quartz grains in nine sandstone core samples from eight wells in the Baiyun depression and the Panyu lower uplift were performed to determine the source and thermal maturity of the paleo-natural gases. Two-dimensional thermal and hydrocarbon generation history modeling of the source rocks coupled with the carbon isotopic results of the fluid inclusion gases were used to understand the natural gas accumulation process and locate the active gas source rocks in the Baiyun depression. The fluid inclusion gases were generated from oil-prone Wenchang Formation source rocks of high thermal maturity (1.7%–3.1% vitrinite reflectance [Ro]), with most Ro values between 2.1% and 2.7% Ro. The mixing of Wenchang Formation–sourced gas of high thermal maturity with Enping Formation–sourced oil-associated gas of relatively low thermal maturity resulted in relatively lighter carbon isotopes of the reservoired gases compared to the fluid inclusion gases. Gas mixing could have occurred during the main Enping Formation–sourced oil charge period based on the hydrocarbon generation history modeling results. This study confirms the presence of oil-prone semideep-water and deep-water lacustrine facies source rocks in the Wenchang Formation of the Baiyun depression, which are widely developed with great thicknesses based on the seismic facies interpretations. However, successful oil and gas exploration in the deep-water area should consider the distribution of active gas source rocks in the Wenchang Formation because the depth of the base of the active source rock greatly decreases from shallow-water to deep-water areas, which increases exploration risk in deep-water and ultradeep-water areas.

Automatic seismic facies interpretation using supervised deep learning

Seismic facies interpretation supports subsurface geologic environment analyses and reservoir predictions. Traditional interpretation methods require much manual work, and they heavily depend on the experience and expertise of the interpreters. We have developed advanced algorithms from supervised deep learning to perform automatic seismic facies interpretations. In deep learning, conventional convolutional neural networks (CNN) and encoder-decoder architectures are widely used for image classification and segmentation problems, respectively. Based on these two architectures, we build a 3D conventional CNN and a conventional encoder-decoder, and then we apply an enhanced encoder-decoder (DeepLabv3+) that integrates superior structures to our research. To train the networks, we develop an effective scheme to automatically and diversely augment data by using well-labeled 2D seismic sections in which facies are divided into nine classes. We perform experiments on the Netherlands F3 data set by training diverse samples and tuning parameters, implement the trained networks on the whole data volume, and then quantitatively evaluate the results. The testing results of the encoder-decoders are more accurate and efficient than those of the conventional CNN, as well as being more consistent with the geologic background. The mean intersection-over-union values for the encoder-decoders are 87.8% (conventional) and 92.4% (enhanced), respectively, whereas for the conventional CNN, the value is 67.8%. Besides, the prediction of one seismic section takes less than 1.0 s for the encoder-decoders, whereas it takes 4.0 min for the conventional CNN.

Seismic facies, stratigraphy, geomorphology, and seismic modelling of a Lower Cretaceous carbonate platform

AbstractGlobally, Lower Cretaceous carbonate platforms form important hydrocarbon reservoirs. An exceptional seismic dataset allowed us to examine controls on the stratigraphy, seismic geomorphology, facies architecture and along-strike variability of a Lower Cretaceous platform. Within the platform succession, a partitioning of facies is developed between Transgressive, Highstand and Forced Regressive seismic sequences. Spectacular seismic geomorphological images of the platform using spectral decomposition techniques reveal spatial organization within both depositional and diagenetic (i.e. palaeokarst) patterns. The study was undertaken in order to optimize a well location. Predrill seismic facies interpretations were interpreted with the aid of offset well data and drill cores. Core data encompass a range of slope, platform margin and platform interior facies with inferred palaeoenvironments that are matched to the seismic geomorphology of the platform. 2D seismic forward modelling was integrated in our workflow to investigate the extent of potential sealing lithologies and the reservoir potential of the individual seismic sequences. Nearby wells were used to calibrate a modified rock physics model for the different lithofacies. Multiple seismic model realizations were generated to assess reservoir quality in alternative well locations and the continuity and quality of sealing strata. Results of the predrill forward-modelled poro-perm prediction are presented, showing a good fit with the final well observations after drilling.

A river-dominated to tide-dominated delta transition: A depositional system case study in the Orinoco heavy oil belt, Eastern Venezuelan Basin

The Orinoco heavy oil belt (OHOB), which has the most significant heavy oil reserve in the world, has shown various sedimentary patterns and different evolution processes within its different blocks. We selected two blocks, Junin in the west and Carabobo in the east, to analyze the depositional environment distinctions due to different transgression processes. Based on the interpretation of 3D seismic data and electrical well-logging facies and analysis of depositional phenomena using cores, we clarified diverse depositional associations and constructed a sedimentary evolution model of the depositional system in the study area. We suggest that the depositional system transitioned from a braided river delta to a tide-dominated delta in the Early Miocene and that two factors mainly controlled the transition: paleo-flow and paleo-geomorphology, which are related to tectonic uplift and transgression processes. Transgression increased gradually through tectonic activity, though transgression rates varied area to area, resulting in different depositional environment transitions and sand-body distribution patterns. In the Carabobo block (east), a gentle slope developed, indicating a fast transgression rate. The intense interaction between river and tide flow led to a large inter-distributary channel area with high-sinuosity- or tidal-channels (relative to the Junin block). In the Junin block (west), tectonic activity developed a large accommodation area providing space for the sediment produced, slowing the transgression rate, and contributing to a mainly fluvial delta environment. Due to the gradual intensive transgression, channel stack patterns transitioned from braided-to meandering-channel in the river dominated delta or tidal-channel in the tide-dominated delta.

Fatehgarh lacustrine turbidite potential, Barmer Basin, India

Global discovered resources of oil and gas in giant stratigraphic and structural-stratigraphic combination traps have increased by nearly 50% in the last 17 years. Among the biggest contributors are the large discoveries in deepwater turbidite systems in passive margins and rift basins. The current study area is located in the Barmer Basin in northwestern India. Barmer Basin is a prolific petroliferous basin with major oil discoveries in structural plays including Mangala, Bhagyam, and Aishwariya fields. The principal reservoirs in the structural highs are high-quality fluvial sandstones of the Paleocene Fatehgarh Formation. Lacustrine turbidite plays have been discovered in the overlying Paleocene Barmer Hill Formation, albeit with moderate to poor reservoir quality. The potential exists, however, for finding off-structure lacustrine deepwater turbidite plays in the Paleocene Fatehgarh with reservoir quality comparable to the high-quality fluvial facies encountered updip in the structural plays. An integrated approach was adopted to identify stratigraphic entrapments across the basin to chase high-quality Fatehgarh reservoirs. Gross depositional environment maps integrating new geoscientific data were created, followed by well-calibrated seismic geomorphology and seismic facies interpretations to identify the distal lacustrine deepwater turbidite system fed by the updip fluvial Fatehgarh systems. Worldwide, the critical risk elements associated with such plays are reservoir presence, quality, and lateral seal. Geophysical tools like unsupervised seismic waveform classification, spectral decomposition, and seismic inversion were applied to the available seismic data, and the results were integrated with the regional geology and well facies information to derisk the critical risk segments.

Morphology, architecture, and evolutionary processes of the Zhongjian Canyon between two carbonate platforms, South China Sea

Two isolated Neogene carbonate platforms (Xisha and Guangle carbonate platforms) have developed in the rifted uplifts since the Early Miocene. A large-scale submarine canyon system, the Zhongjian Canyon (ZJC), has developed in the tectonic depression between the two platforms since the Middle Miocene. High-resolution bathymetry data and 2D and 3D seismic data reveal the existence of the ZJC on the present seafloor, as well as in Neogene intervals. It exhibits typical characteristics of deepwater canyons that cut the surrounding rocks and indicate strong erosional features. The ZJC resulted from northwest–southeast strike-slip fault activities during synrift and postrift stages, and it periodically grew during the development of carbonate platforms since the Middle Miocene. We identified four cycles of parallel to subparallel high amplitude and dim reflectors in seismic data, which we interpreted as alternating canyon fill, based on the interpretation of seismic facies. Thus, the sedimentary evolution of the ZJC can be divided into four typical stages, which were in the Middle Miocene, Late Miocene, Early Pliocene, and Pleistocene. Considering the tectonic background of the carbonate platforms, as well as the on-going igneous activities, the sediment filling the canyon could be derived from a mixture of carbonate clasts, igneous clasts, mud, and silt. The laminar high-amplitude reflectors and dim-reflector package represented a fining-upward sedimentary cycle. The coarse-grained sediment in canyon fillings could be turbidites, carbonate debrites, and even igneous clasts. In contrast, the fine-grained sediment is likely to be dominated by pelagic to hemipelagic mud, and silt. This case study describes a deepwater canyon under a carbonate-dominated sedimentary environment and has significant implications for improving our knowledge of periplatform slope depositional processes. Furthermore, the insight gained into periplatform slope depositional processes can be applied globally.

Robust time-frequency analysis of seismic data using general linear chirplet transform

Time-frequency analysis (TFA) has been widely used in seismic processing and interpretation. A good time-frequency representation can preferably characterize geologic spatial distribution and detect hydrocarbon reservoir anomalies. This paper applies a robust seismic TFA method based on the general linear chirplet transform (GLCT). The GLCT method is an extended form of LCT, which is a unifying framework encompassing the short time Fourier transform (STFT) and the continuous wavelet transform (CWT) using the chirplet atom as the kernel function instead of the sinusoidal wave or wavelets. By rotating the chirplet atom at each time-frequency point, GLCT method could adaptively choose the best atom to fit the local time-frequency feature of seismic signals. The algorithm follows such a simple logic and produces a broadband time-frequency spectrum free of cross-term interference, resulting in good performance characterizing the instantaneous spectral variations. Synthetic data analysis demonstrates that the GLCT method is able to reach a higher energy concentration in the time-frequency plane than conventional methods. Robustness analysis indicates that GLCT produces more stable results that outperform not only STFT, CWT, but also high-resolution methods such as the synchrosqueezing transform and complete ensemble empirical mode decomposition in the case of noisy data. The application to field data illustrates that the isofrequency attributes extracted by GLCT through spectral decomposition could effectively image subtle stratigraphic structures of the subsurface paleotopography and highlight the frequency anomalies associated with hydrocarbons. Sometimes, these anomalies might be otherwise inundated in the background noise. Our method can be a validation tool for seismic facies interpretation improvement and direct hydrocarbon indication in practice.

Integrated Study of Lithofacies Identification—A Case Study in X Field, Sabah, Malaysia

Understanding subsurface geology is essential for oil and gas exploration. Seismic facies interpretation is very useful in investigating this concept. The interpretation of the depositional setting of the X Field is achieved by integrating the seismic facies characteristics on 3D seismic data and well log data. Both the seismic and well log data are widely used in hydrocarbon exploration to map the subsurface, as they complement each other. Well logs yield the vertical resolution of the subsurface geology at the drilled well, whereas seismic data reveal the lateral continuity. The objective of this paper is to demonstrate the integration of 3D seismic data and well log data for lithofacies identification. Interpretation and analysis of lithofacies is carried out through the integration of the characteristics of seismic reflections with well information (logs). Horizons are interpreted based on the variation in seismic reflections on the seismic section, which is caused by the change in geology within seismic sequences. Well logs give detailed information at the points where the wells were drilled. Interpolating between these points and extrapolating away from the points into undrilled areas can be helpful in providing a better geological knowledge of an area. The result of this integrated study depicts the lithofacies in the area. This integrated study will provide a better insight with higher degree of reliability to the facies distribution and depositional setting of the X Field. The geological and geophysical aspects of the field will be documented.

Late Pleistocene Bryant Canyon turbidite system: Implications for Gulf of Mexico minibasin petroleum systems

The western ancestral Mississippi shelf-margin delta fed the Bryant Canyon turbidite system in the intraslope basin province of the northwestern Gulf of Mexico (GOM) during the penultimate glacial lowstand of sea level (130–160 year BP). The Bryant Canyon links a chain of 15 fill-and-spill minibasins on the continental slope. On the upper and lower continental slopes, minibasins are narrow (1–3 km), elongate (3–6 km), and follow salt ridges. On the middle slope, minibasins are larger (8–15 km) semicircular basins. Three main depositional facies are recognized based on seismic-facies interpretation: (1) ponded turbidites (T), (2) mass-transport deposits (MTDs), and (3) bypass channelized turbidites (C). Thick, intrabasinal, muddy MTD wedges sourced from high-relief internal walls of the minibasins alternate with and frequently cap externally derived deposits. Tabular extrabasinal MTD deposits originated from shelf-margin delta or canyon-wall failures upslope. The T and MTD facies deposits each make up approximately 40% of basin fill, and the C facies deposits comprise approximately 20%. The T facies deposits form perched lobes at canyon inlets into basins and ponded units on distal sides of basins. Channels in the C facies are similar in width (500–2000 m) and relief (20–100 ms) to channels in productive GOM subsurface minibasins. Syntectonic activity of salt diapirs typically began midway through filling of Bryant Canyon minibasins and then preferentially uplifted northern portions of basin deposits. Local salt-tectonic activity caused greater basin relief and thicker capping MTDs than in subsurface minibasins to the west (e.g., Brazos-Trinity Basin IV) or east (e.g., Mississippi Canyon). Bryant Canyon minibasins provide excellent modern analogs for subsurface Miocene to Pleistocene GOM chains of minibasins because of similar scales and depositional facies. The youngest Bryant T facies deposits and their overlying incised, thick, channel deposits contain the most sand-prone facies and suggest the best potential for petroleum reservoirs in subsurface minibasins.

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.

Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation

Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.