Relative Geologic Time Research Articles

Electrofacies-driven 3D-static reservoir modeling of the Late-Cenomanian AbuRoash’G lithounit (Abu-Gharadig Basin, Egypt): Sequence stratigraphic and geomodel constraints for a gas-bearing estuarine system

Estuarine-systems, developed upon transgressive-phases, feature high-quality reservoir-facies, e.g. tidal-bars, that are important stratigraphic-plays critical for hydrocarbon exploration-development. However, capturing their intricate architectural elements (heterogeneity and quality) is still challenging due to the complex stacking-nature and limited-examples. Moreover, defining reservoir-boundaries upon static-modeling of reservoirs cannot be efficient unless it is controlled by stratal-geometries and established depositional-models. To this end, in this study, we performed 3D-static geocellular reservoir-modeling process for the Late-Cenomanian AbuRoash“G” Member (Abu-Gharadig Basin, Egypt) with sequence-stratigraphic and geomodel, relative-geological-time (RGT) model and horizon-stacks, constraints. In this investigation, as an effective-workflow, not only facies-analysis, integrating seismic-stratigraphy and GR-log motifs, was applied for paleo-environment reconstruction, but also machine learning-based electrofacies were applied, through self-organized-maps (SOM), to accurately recognize complex facies-assemblages present. Object-based and pixel-based stochastic-simulation processes were applied upon geocellularly modeling rock and fluid properties, utilizing key-information scales of seismic and well-log data. The results show that three third-order depositional sequences dominate the succession, resting on the Late-Cretaceous unconformity, of which sequence-1 encloses the lowstand and transgressive systems-tracts of the fluvio-estuarine Bahariya and Abu Roash“G” units, respectively. The transgressive phase built AbuRoash“G” lithounit features an estuarine depositional-system encompassing four facies-associations, of which tidal-sand-bars represent significant gas-bearing reservoir-quality facies. The tidal-bar facies’ efficient reservoir quality calls for attention and testing in future development plans and investigation in similar settings. Furthermore, the facies-constrained workflow established in this study, for reservoir modeling, can worldwide help identify the ultimate reservoir-configuration, as long as the 3D-static modeling process is controlled by the stratal and geomodel restraints.

An artificial intelligence workflow for horizon volume generation from 3D seismic data

Horizon-based subsurface stratigraphic model building is a tedious process, especially in geologically complex areas where seismic data are contaminated with noise and thus are of weak and discontinuous reflectors. Seismic interpreters usually use stratal (proportional) slices to approximately inspect 3D seismic data along seismic reflectors yet to be interpreted. We introduce an artificial intelligence workflow consisting of three deep learning steps to provide a conditioned seismic image that is easier to interpret, a stratigraphic model that outlines major formations, and moreover a relative geologic time volume that allows us to automatically extract an infinite number of horizons along any seismic reflectors within a seismic cube. Depending on the availability of interpreters, the proposed workflow can either run fully unsupervised without human inputs or using sparse horizon interpretation as constraints to further improve the quality of extracted horizons, providing flexibility in both efficiency and quality. Starting from only seismic images and a few key horizons interpreted on very sparse seismic lines, we demonstrate the workflow to generate a stack of complete horizons covering the entire seismic volume from offshore Australia.

Stratal surfaces honoring seismic structures and interpreted geologic time surfaces

Seismic horizons play a significant role in reservoir model construction and sedimentary facies interpretation, providing crucial low-frequency constraints for seismic inversion. In basin and regional interpretations, the assumption that seismic reflections represent a stratigraphic surface with constant geologic time is significant for guiding seismic interpretation. This assumption may fail when applied to local reservoir scales due to common geologic time transgressions of a particular event in regular wavelet frequency. There will be inconsistencies between seismic events and stratigraphic surfaces. To address this issue and obtain relatively accurate stratal interpretations, we develop a hybrid horizon extraction method honoring seismic structures and time-stratigraphic frameworks, in which seismic reflection structures provide local details and interpreted geologic time surfaces offer critical constraints. First, we develop concepts and a workflow using a realistic outcrop model. We develop a new geology-guided structure tensor by fitting a gradient vector of seismic images and geologic time surfaces. We also consider existing geologic conditions, such as unconformities, and fuse them into our method to calculate accurate slopes and generate reliable relative geologic time images at a fine scale, followed by making slices. Further, we extend our method to 3D seismic data volumes. Our experiments, conducted using simulated and field data, show the superiority and accuracy of our hybrid method compared with the slope-based and stratal slicing methods. These results highlight the potential for applying our method to fine-scale subsurface modeling.

Seismic geomorphology of the deepwater sediment dispersals systems in the drift phase of the Mundaú sub-basin, equatorial margin, Brazil

The Ceará Basin, located in the Brazilian Equatorial Margin, represents an exploratory frontier situated in a complex structural framework. The basin observed a dynamic interplay of extensional, transtensional, and transpressional regimes, registered by several sedimentary intervals and unconformities poorly documented by wells, especially at the deeper portions. In this study, based on 3D seismic and well data from the Mundaú Sub-Basin, we interpreted the main seismic sequences of the drift phase to build a Relative Geological Time (RGT) model, based on a semi-automatic seismic traces correlation matched with the well top formations. A seismic attribute analysis from key seismic surfaces extracted from the RGT, highlights the main sediment dispersal routes revealing a significant change after the Mid-Eocene, once sedimentation overpassed a topographic barrier created by fault reactivations, in a transpressional regime, associated with the Romanche Fracture Zone. The integration of seismic and well data affords a deeper understanding of basin dynamics, revealing how sedimentary processes respond to shifts in tectonic stress in this portion of the basin.

Iterative multitask learning and inference from seismic images

SUMMARY Seismic interpretation aims to extract quantitative and interpretable attributes from a seismic image produced using some migration method to inform characteristics of a subsurface reservoir or target of interest. Current paradigms for computing seismic attributes mostly rely on single-task algorithms. We develop an iterative, multitask machine learning method to learn and infer multiple attributes from a seismic image. This method is composed of two stages: a multitask inference stage and a multimodal, multitask refinement stage. The basic mechanism of this method is that we train a multitask inference neural network to estimate a set of attributes, including a relative geological time volume, a denoised higher-resolution seismic image and multiple fault attributes (including probability, dip and strike), from a low-resolution, noisy seismic image; then we input the inferred attributes to a multitask refinement NN to enhance the raw inference results iteratively. The two multitask neural networks are trained separately based on synthetic seismic images and associated labels generated by geological modelling. Applications of this multitask learning and inference method to synthetic and field seismic images show that our method can improve the structural consistency among output seismic attributes compared with single-task neural networks, leading to more reliable automatic interpretation and subsurface characterization.

Automated multi‐well stratigraphic correlation and model building using relative geologic time

AbstractStratigraphic correlation of geophysical well logs is one of the most important—and most time‐consuming—tasks that applied geoscientists perform on a daily basis. Using the dynamic time warping (DTW) algorithm, automated correlation of two wells is a fairly simple task; DTW can also be used to correlate a large number of wells along a single path. However, errors accumulate along a path and loops cannot be closed. To create a three‐dimensionally consistent correlation framework, we use a Python implementation of the Wheeler and Hale (2014) approach, which is based on the idea of stretching and squeezing all logs into a chronostratigraphic diagram that has relative geologic time (RGT) on its y‐axis. The depth shifts needed for the RGT transformation are computed by translating the outputs of a large number of pairwise DTW correlations into a least‐squares optimization problem that is solved through the conjugate gradient method. The resulting chronostratigraphic diagram provides an overview of the overall stratigraphy and its variability; and the correlation framework is significantly different from what one could get from correlating based on lithologic similarity between two logs. To create geologically intuitive well‐log cross sections, we use a multi‐scale blocking method that relies on the continuous wavelet transform to identify stratigraphic units of a certain scale in one well and then propagate these boundaries to all the other wells. We demonstrate the usefulness of this approach on a data set with close to 700 wells from the Permian Basin, West Texas. Linear channel bodies in the deepwater Spraberry Formation are easily detected and clearly highlighted in maps and cross sections. The methodology is robust enough for mapping subtle stratigraphic details, previously considered feasible only through manual interpretation. More importantly, it can be used to quickly build three‐dimensional stratigraphic models for large segments of sedimentary basins where enough log data are available.

Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults.

ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation

Abstract. Deep learning has been widely used for various kinds of data-mining tasks but not much for seismic stratigraphic interpretation due to the lack of labeled training datasets. We present a workflow to automatically generate numerous synthetic training datasets and take the seismic clinoform delineation as an example to demonstrate the effectiveness of using the synthetic datasets for training. In this workflow, we first perform stochastic stratigraphic forward modeling to generate numerous stratigraphic models of clinoform layers and corresponding porosity properties by randomly but properly choosing initial topographies, sea level curves, and thermal subsidence curves. We then convert the simulated stratigraphic models into impedance models by using the velocity–porosity relationship. We further simulate synthetic seismic data by convolving reflectivity models (converted from impedance models) with Ricker wavelets (with various peak frequencies) and adding real noise extracted from field seismic data. In this way, we automatically generate a total of 3000 diverse synthetic seismic datasets and the corresponding stratigraphic labels such as relative geologic time models and facies of clinoforms, which are all made publicly available. We use these synthetic datasets to train a modified encoder–decoder deep neural network for clinoform delineation in seismic data. Within the network, we apply a preconditioning process of structure-oriented smoothing to the feature maps of the decoder neural layers, which is helpful to avoid generating holes or outliers in the final output of clinoform delineation. Multiple 2D and 3D synthetic and field examples demonstrate that the network, trained with only synthetic datasets, works well to delineate clinoforms in seismic data with high accuracy and efficiency. Our workflow can be easily extended for other seismic stratigraphic interpretation tasks such as sequence boundary identification, synchronous horizon extraction, and shoreline trajectory identification.

A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer

Horizon extraction and fault detection are essential in seismic interpretation and closely related to each other. Most existing methods tend to deal with these two tasks independently, and may not work well in interpreting seismic images with complex geologic structures. We propose a multi-task learning (MTL) network with two branches to extract all horizons and detect faults simultaneously by estimating a relative geologic time (RGT) map as well as computing a fault map. These two branches share training datasets, feature maps, and network parameters during the training. The RGT estimation branch, constructed with a transformer architecture, is more lightweight compared to previous CNN methods but provides a larger and structure-oriented receptive field to adaptively capture global structural information for estimating a globally optimal RGT map. The fault detection branch is a simple convolutional neural network (CNN) which merges feature maps shared by the transformer and the derivatives of the estimated RGT to compute a fault map. The fault detection branch provides boundary control for the RGT estimation branch while the later provides global constraints for the former to improve its robustness to noise. Note that our RGT estimation by globally fitting all structures in a seismic image is a volumetric horizon interpretation method with which we are able to obtain a whole volume of horizons, all at once, by simply extracting contours of the RGT map. In our method, we further enable convenient human interactions by integrating manually interpreted horizons (or horizon segments) into the network, which imposes expert knowledge on the network to estimate reasonable RGT results from seismic images with complex fault systems, unconformities, and poor data quality. Moreover, when using 3D horizons as constraints, we are able to decompose the computationaly expensive 3D RGT estimation from a seismic volume into independently parallel 2D estimations slice by slice and combine them to obtain a laterally consistent 3D result.

Toward accurate seismic flattening: Methods and applications

Seismic flattening maps a seismic volume from the original space in depth (or two-way traveltime) to the Wheeler domain in geologic time where all of the seismic reflections are horizontally aligned. It provides an efficient way to interpret a whole volume of horizons all at once by extracting the horizontal slices in the flattened space. Conventional slope-based flattening methods that can locally flatten the seismic reflections, however, often fail to flatten the reflections in a global sense and cannot accurately align the reflections across faults. We have developed an iterative method to improve the flattening by using the slopes and correlations of seismic traces. The local slopes, estimated for each image sample, can locally follow reflections but may fail to track the reflections over a long distance or correlate the reflections across faults. The seismic correlations, computed for randomly and sparsely extracted seismic traces, help to align the corresponding reflections over a long distance and across faults. We compute flattening shifts, one for each image sample, by fitting the seismic slopes and correlating in the least-squares sense. We further apply the shifts to relatively adjust the seismic samples in the vertical direction so that all of the reflections are horizontally aligned. We iteratively flatten the seismic volume by repeating the process multiple times (often less than five times). With the flattening shifts, we can compute a relative geologic time (RGT) volume that implicitly contains all the structure information of the seismic volume. An arbitrary number of horizons then can be extracted as isosurfaces of RGT values. Multiple field examples demonstrate that our method significantly improves the flattening, especially across faults and missing data zones, compared with conventional flattening methods. Our method is especially helpful to interpret subtle stratigraphic features (e.g., vertically thin channels) by providing an accurately flattened seismic volume.

Building subsurface models with horizon-guided interpolation and deep learning: Application to the Volve field data

Subsurface modeling plays an important role in hydrocarbon exploration but remains a challenging task that typically requires a full and reasonable integration of geophysical observations and geologic constraints. We have developed a workflow to fully use seismic amplitudes, well-log properties, and interpreted seismic structures to build geologically reasonable models. We take the Volve field data as an example and apply our workflow step by step as follows. First, we perform some preprocessing on the provided Volve seismic data, horizons, and well logs to remove anomalous values and adjust seismic-well ties in the depth domain. Second, we use a dynamic-programming-based method to infill gaps and refine the vertical positions of the provided horizons and efficiently pick more horizons. We further use the horizon surfaces to interpolate a relative geologic time (RGT) volume which can be considered as an implicit structural model representing seismic structural and stratigraphic features. Third, we integrate the provided well logs and the computed RGT volume to interpolate a subsurface model that conforms to well-log properties and seismic structural and stratigraphic features. Finally, we propose a multiscale neural network to predict a final subsurface model by using a combination of the seismic data and the interpolated model as inputs and using the well-log data as training labels. Inputting the interpolated model to the network helps to provide a low-frequency control for obtaining a more stable prediction. The results indicate that our workflow is able to produce geologically reasonable subsurface models with high lateral continuity and vertical resolution.

Deep learning for end-to-end subsurface modeling and interpretation: An example from the Groningen gas field

Subsurface interpretation and modeling are crucial to the success of reservoir exploration and production, which often involves integrating multiple types of subsurface data and accomplishing a series of subtasks consecutively and/or in parallel. Those tasks include data processing and conditioning, structural mapping, property modeling, and others. With the emergence of machine learning (ML), each component of the subsurface modeling and interpretation workflow now has elements of ML automation incorporated into individual steps. However, there have been few attempts to generate an integrated end-to-end workflow that incorporates ML at every stage. This paper presents an end-to-end solution for subsurface modeling and interpretation that is powered by multiple convolutional neural networks (CNNs). Its performance is demonstrated on the Groningen gas field. The workflow can be subdivided into four parts, with the initial phase focusing on data preconditioning, particularly log quality control and reconstruction. Phase two focuses on seismic property estimation (i.e., relative geologic time) and structural feature identification and extraction (i.e., fault and salt). Phase three combines seismic feature extraction, log data, derived seismic properties, and interpretations to generate a 3D rock property model containing modeled gamma ray, density, velocity, and porosity properties. The final component is to validate the estimated subsurface properties by CNN-based seismic image simulation. The results demonstrated in the Groningen field show high accuracy, strong lateral consistency, and a good match with the dominant subsurface features documented in this area, including the Zechstein salt, the complex fault system, and the Rotliegend reservoir model. We conclude that this end-to-end workflow can be readily applied to other fields to provide a one-click solution for efficient subsurface modeling and interpretation.

Geologic-time-based interpolation of borehole data for building high-resolution models: Methods and applications

Integrating borehole data into a uniformly sampled grid is an essential but challenging task for seismic data processing and reservoir characterization. We propose an efficient geologic-time-based interpolation method to build subsurface models where the structures are consistent with the seismic structures and the vertical resolution of model properties is as high as well-log records. Based on a seismic image, we first compute a relative geologic time (RGT) volume that provides an implicit map of all the geologic structures in the seismic image. We then construct an interpolated model from borehole data by following constant RGT values (each one corresponds to a same geologic layer), and thus obtain a high-resolution model honoring both seismic structures and well-log values. Such a model could provide a low-frequency control for a deep learning or conventional inversion method to estimate reservoir properties, or it can be used as a reliable initial background model to improve the performance of full-waveform inversion. We use both synthetic and field data examples to demonstrate the effectiveness of our method even when reservoir properties are only observed at sparsely scattered locations. In comparison to the existing approaches, our method can produce a more geologically consistent subsurface model which can be used as a better initial model for a deep learning method to estimate a refined rock-property model from seismic data.

Using relative geologic time to constrain convolutional neural network-based seismic interpretation and property estimation

Three-dimensional seismic interpretation and property estimation is essential for subsurface mapping and characterization, in which machine learning, particularly supervised convolutional neural network (CNN), has been extensively implemented for improved efficiency and accuracy in the past few years. However, in most seismic applications, the amount of available expert annotations often is limited, which raises the risk of overfitting a CNN, particularly when only seismic amplitudes are used for learning. In such a case, the trained CNN would have poor generalization capability, causing the interpretation and property results of obvious artifacts, limited lateral consistency, and thus restricted application to following interpretation/modeling procedures. Our study proposes addressing such an issue by using relative geologic time (RGT), which explicitly preserves the large-scale continuity of seismic patterns, to constrain a seismic interpretation and/or property estimation CNN. Such constrained learning is enforced twofold: (1) from the perspective of input, the RGT is used as an additional feature channel besides seismic amplitude, and more innovatively (2) the CNN has two output branches, with one for matching the target interpretation or properties and the other for reconstructing the RGT. In addition, multiplicative regularization is used to facilitate the simultaneous minimization of the target-matching loss and the RGT-reconstruction loss. The performance of such an RGT-constrained CNN is validated by two examples, facies identification in the Parihaka data set and property estimation in the F3 Netherlands data set. Compared with those purely from seismic amplitudes, the facies and property predictions using the proposed RGT constraint demonstrate significantly reduced artifacts and improved lateral consistency throughout a seismic survey.

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

AbstractExtracting horizons and detecting faults in a seismic image are basic steps for structural interpretation and important for many seismic processing schemes. A common ground of the two tasks is to analyze seismic structures and they are related to each other. However, previously proposed methods deal with the tasks independently, and challenge remains in each of them. We propose a volume‐to‐volume neural network to estimate a relative geologic time (RGT) volume from a seismic volume, and this RGT volume is further used to simultaneously interpret horizons and faults. The network uses U‐shaped framework with attention mechanism to systematically aggregate multi‐scale information and automatically highlight informative features, and achieves high prediction accuracy with affordable computational costs. To train the network, we build thousands of 3‐D noisy synthetic seismic volumes and corresponding RGT volumes with realistic and various structures. We introduce a loss function based on structure similarity to capture spatial dependencies among seismic samples for better optimizing the network, and use multiple reasonable assessments to evaluate the predicted results. Trained by using synthetic data, our network outperforms the conventional approaches in recognizing structural features in field data examples. Once obtaining an RGT volume, we can not only obtain seismic horizons by simply extracting RGT constant surfaces but also detect faults that are indicated by lateral RGT discontinuities. To be able to deal with large seismic volumes, we further propose a workflow to first estimate sub‐volumes of RGT and merge them to obtain a full RGT volume without boundary artifacts.

Deep learning for relative geologic time and seismic horizons

Constructing a relative geologic time (RGT) image from a seismic image is crucial for seismic structural and stratigraphic interpretation. In conventional methods, automatic RGT estimation from a seismic image is typically based on only local image features, which makes it challenging to cope with discontinuous structures (e.g., faults and unconformities). We have considered the estimation of 2D RGT images as a regression problem, where we design a deep convolutional neural network (CNN) to directly and automatically compute an RGT image from a 2D seismic image. This CNN consists of three parts: an encoder, a decoder, and a refinement module. We train this CNN by using 2080 pairs of synthetic input seismic images and target RGT images, and then we test it on 960 testing seismic images. Although trained with only synthetic images, the network can generate accurate results on real seismic images. Multiple field examples show that our CNN-based method is significantly superior to conventional methods, especially in dealing with complex structures such as crossing faults and complicatedly folded horizons, without the need of any manual picking.

Seismic horizon picking by integrating reflector dip and instantaneous phase attributes

Seismic horizons are the compulsory inputs for seismic stratigraphy analysis and 3D reservoir modeling. Manually interpreting horizons on thousands of vertical seismic slices of 3D seismic survey is a time-consuming task. Automatic horizon interpreting algorithms are usually based on the seismic reflector dip. However, the estimated seismic reflector dip is usually inaccurate near and across geologic features such as unconformities. We are determined to improve the quality of picked horizons using multiple seismic attributes. We assume that seismic horizons follow the reflector dip and that the same horizons should have similar instantaneous phase values. We first generate horizon patches using a reflector dip attribute, which is similar to current methods. We use seismic coherence attribute as the stop criteria for tracking the horizon within each patch. Considering the inaccuracy of reflector dip estimates at and near the discontinuous structures such as fault and unconformities, we use the seismic instantaneous phase attribute to improve the quality of the generated horizon patches. We generate horizons by merging the residual horizon patches and only outputting the best horizon in each iteration. Our method is capable of generating a horizon for each reflection within the 3D seismic survey, and the generated horizons strictly follow the seismic reflections over the whole seismic survey. Finally, each time sample of seismic traces is assigned a chronostratigraphic relative geologic time value according to the tracked horizons.

Full-volume 3D seismic interpretation methods: A new step towards high-resolution seismic stratigraphy

Following decades of technological innovation, geologists now have access to extensive 3D seismic surveys across sedimentary basins. Using these voluminous data sets to better understand subsurface complexity relies on developing seismic stratigraphic workflows that allow very high-resolution interpretation within a cost-effective timeframe. We have developed an innovative 3D seismic interpretation workflow that combines full-volume and semi-automated horizon tracking with high-resolution 3D seismic stratigraphic analysis. The workflow consists of converting data from seismic (two-way traveltime) to a relative geological time (RGT) volume, in which a relative geological age is assigned to each point of the volume. The generation of a horizon stack is used to extract an unlimited number of chronostratigraphic surfaces (i.e., seismic horizons). Integrated stratigraphic tools may be used to navigate throughout the 3D seismic data to pick seismic unconformities using standard seismic stratigraphic principles in combination with geometric attributes. Here, we applied this workflow to a high-quality 3D seismic data set located in the Northern Carnarvon Basin (North West Shelf, Australia) and provided an example of high-resolution seismic stratigraphic interpretation from an Early Cretaceous shelf-margin system (Lower Barrow Group). This approach is used to identify 73 seismic sequences (i.e., clinothems) bounded by 74 seismic unconformities. Each clinothem presents an average duration of approximately 63,000 years (fifth stratigraphic order), which represents an unprecedented scale of observation for a Cretaceous depositional system on seismic data. This level of interpretation has a variety of applications, including high-resolution paleogeographical reconstructions and quantitative analysis of subsurface data. This innovative workflow constitutes a new step in seismic stratigraphy because it enables interpreters to map seismic sequences in a true 3D environment by taking into account the full variability of depositional systems at high frequency through time and space.

Full-volume interpretation methods: Applications for quantitative seismic stratigraphy and geomorphology of the Lower Barrow Group, Northwest Australia

Following decades of technological innovation, geologists have now access to extensive 3D seismic datasets. How these data will help understanding the complexity of the subsurface relies on developing stratigraphic workflows that allow very high-resolution interpretation in a cost-effective timeframe. Here, the use of full-volume, semi-automatic horizon tracking tools allowed interpreting ultra-high resolution seismic sequences (^0,000 yrs duration) within a Cretaceous prograding shelf-margin (Lower Barrow Group; LBG) on the North West Shelf of Australia. Initially, semi-automated horizon tracking allowed mapping key regional unconformities defining 3rd order seismic sequences. In a second step, a very high resolution grid (nodes corresponding to seismic traces) was generated in each 3rd order sequence. An automatic propagation algorithm then linked the nodes based on their similarities, resulting in a very dense network of “proto”-seismic horizons. Volume interpolation resulted in the creation of a Relative Geological Time (RGT) model from which a very high number of chronostratigraphic surfaces were extracted. This allowed a full volume 3D mapping of every clinoform in each 3rd order sequence, from which quantitative data (clinoform height, slope, topset vs bottomset thickness) and seismic attributes (seismic geomorphology) were extracted. This analysis unveiled the high resolution changes in sediment supply and accommodation in time and space in the LBG, and provided new insights on the distribution of shallow and deep marine plays in the basin. This innovative workflow constitutes a new step in sequence stratigraphy as it allows interpreters to map sequences in a true 3D environment hence taking into account the full variability of depositional systems in time and space.

Robust and adaptive approaches for Relative Geologic Time Estimation

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the deposit orders and to deal efficiently with unconformities such as erosions, progradating systems, etc. To this end, approaches have been proposed formulating the estimation problem in a regularized convex optimization problem. However none of these fully address efficiently all issues. In this paper, we propose a new regularization term based on an asymmetric and adaptive weight function. The asymmetric behavior focuses on the ill-posed problem while the adaptive process is used to deal with unconformities by modulating the strength of the regularization if necessary. Moreover, to increase the robustness of the approach, we propose variants of the method in terms of ℓ1-norm instead of ℓ2-norm that corresponds to potentially too smooth solutions. For evaluating the relevance of our proposals, experimentations have been conducted on both synthetic and real seismic images.