Supervised Seismic Facies Analysis via ACGAN

Seismic facies analysis makes use of different seismic parameters in order to get other than structural information. A review is given of possibilities and usefulness of seismic facies analysis in oil exploration.A seismic facies unit can be defined as a sedimentary unit which is different from adjacent units in its seismic characteristics. Parameters that should be taken into consideration in the seismic facies analysis are as follows: reflection amplitude, dominant reflection frequency, reflection polarity, interval velocity, reflection continuity, reflection configuration, abundance of reflections, geometry of seismic facies unit, and relationship with other units.Interpretation of seismic facies data may be either direct or indirect. The purpose of the direct interpretation is to find out geological causes responsible for the seismic signature of a seismic facies unit. So, the direct interpretation may be aimed at predicting lithology, fluid content, porosity, relative age, overpressured shales, type of stratification, geometry of the geological body corresponding to the seismic facies unit and its geological setting. The indirect interpretation is intended to reach some conclusions on depositional processes and environments, sediment transport direction, and some aspects of geological evolution (transgression, regression, subsidence, uplift, erosion).The results of the seismic facies analysis may be shown on seismic facies cross‐sections and seismic facies maps. Depending on the available seismic data and geological conditions in the area under consideration, the seismic facies maps may be of different types such as general seismic facies maps showing distribution of different seismic facies units, sand‐shale ratio maps, direction of cross‐bedding and paleo‐transport maps etc.Several kinds of seismic facies units and their geological interpretation are discussed as examples of seismic facies analysis.

Seismic facies analysis is a powerful, yet largely subjective, methodology for hydrocarbon exploration and development. The term “seismic facies” means different things to different people. For this study, however, seismic facies is defined as a seismic description of depositional facies confined in a seismically thin bed at a normal seismic sampling rate (e.g., 4 ms), making this study's seismic facies analysis different from classical seismic facies analysis of much thicker depositional sequences. As a subjective interpretation of seismic attributes, seismic facies analysis is often laborious and inconsistent, especially if multiple mapping units are involved. How to automate the process via computer pattern recognition so that consistency could be improved and the 3D cycle time reduced has been a hot topic in recent years.

Summary Conventional seismic facies analysis methods are difficult to adapt to the seismic facies analysis that focuses on characterizing the stratigraphic texture. We propose a method for seismic facies analysis via unsupervised dictionary learning for stratigraphic texture enhancement. The method first learns a complete dictionary containing seismic data features through the dictionary learning method. Then clustering method and DBI index are used to cluster the atoms in the dictionary and automatically determine the number of categories to divide multiple sub-dictionaries. Finally, the sub-dictionary is used to perform sparse reconstruction on the seismic data. According to the reconstruction error, it is determined which sub-dictionary the reconstructed seismic signal belongs to. In the process of sparse reconstruction, the amplitude strength of the seismic signal is converted into the coefficient magnitude of the sparse reconstruction. The sparse reconstruction error only reflects the difference in the waveform structure between the reconstructed data and the sub-dictionary, which can alleviate the influence of amplitude strength related to physical property changes, and achieve the purpose of fine identification of geological bodies related to strata structures. Synthetic data and field data tests have confirmed that the proposed method can more effectively characterize the lateral distribution of river channels.

Deep learning (DL) applications of seismic reservoir characterization often require the generation of synthetic data to augment available sparse labeled data. An approach for generating synthetic training data consists of specifying probability distributions modeling prior geologic uncertainty on reservoir properties and forward modeling the seismic data. A prior falsification approach is critical to establish the consistency of the synthetic training data distribution with real seismic data. With the help of a real case study of facies classification with convolutional neural networks (CNNs) from an offshore deltaic reservoir, we have highlighted several practical nuances associated with training DL models on synthetic seismic data. We highlight the issue of overfitting of CNNs to the synthetic training data distribution and propose regularization strategies to address it. We demonstrate the efficacy of our proposed strategies by training the CNN on synthetic data and making robust predictions with real 3D partial stack seismic data.

Machine learning-based automatic seismic facies analysis has increased significantly over the past few decades. The key is to select the most representative features (such as the commonly used poststack amplitude data or the induced seismic attributes) as the input of the different machine learning algorithms. As an advanced branch of machine learning, deep learning can be used to extract the discriminatively deep features from seismic data similar to those used in image classification. In this study, we havedeveloped an unsupervised seismic facies analysis method by using a recurrent autoencoder model. First, we have constructed and trained an autoencoder architecture combined with long short-term memory-based recurrent operation. Its main aim is to learn the deep discriminative features by taking the windowed poststack seismic data as the input time series data. This type of unsupervised learning takes advantage of the no labeling requirement of seismic data. In addition, the recurrent operation is beneficial in delineating the time-sequential characteristics of seismic data. Second, we have taken the learned features as the input of simple K-means clustering and analyzed the corresponding seismic facies. In other words, the clustering is executed in the learned feature space (learned feature-based clustering). Real data results have demonstrated that our method reveals more details than the original amplitude-based K-means clustering, depending on the cluster calibration. In particular, according to the known natural gamma-ray logs and lithological descriptions of five wells, different amounts of sandstone and mudstone deposit are more accurately discriminated, which is substantially informative in reservoir prediction and hydrocarbon exploration. Furthermore, the estimated average silhouette scores have quantitatively shown the effectiveness of our method.

Seismic facies analysis plays a key role in stratigraphy and has developed rapidly in the context of deep learning methods. However, seismic facies analysis cannot only be regarded as an image segmentation or classification task because this would exclude prior knowledge of geophysicists on seismic interpretation. Seismic parameters summarized by geophysicists were included in the seismic facies analysis model. Seismic parameters were used to calibrate the different seismic facies units, and well logging data and sequence stratigraphy were integrated to perform geological interpretation and to establish a training dataset. A multi-head seismic parameter classification (MSPC) model was then established to divide the seismic facies units. A seismic facies geological interpretation (SFGI) model was constructed for the geological interpretation of seismic facies units, and an end-to-end seismic facies analysis model was designed based on the MSPC model and the SFGI model. The proposed model effectively improves the accuracy of the seismic interpretation, solves the intra-facies noise problem, and is easy to train and use.

Seismic interpretation is an essential factor in the success of field development projects because it allows geologists and engineers to better understand subsurface formations and identify potential resources; however, noisy seismic data, especially in regions with complex geological structures, frequently hinder this process. Such noise emerging from equipment limitations, environmental interference, and inherent geological complexities can obscure essential geological features, making accurate subsurface imaging challenging and reducing the effectiveness of traditional interpretation methods. This paper presents a machine learning (ML) method to enhance the conditioning of 2D and 3D post-stack seismic data by targeting noise and migration artifacts. These issues are often responsible for compromising the quality of seismic data sets. The method this paper presents leverages convolutional neural networks (CNNs) to identify and correct spatial inconsistencies within the data. With minimal preprocessing, requiring only the conversion of data into a 3D NumPy array format, this approach simplifies the workflow while improving data clarity and accuracy. Early statistical analysis of outliers and data normalization provides the ML model with stable inputs, allowing it to iteratively train on raw data and infer the original signal patterns in an unsupervised manner without the need for labeled data sets. The model outputs a conditioned version of the seismic data, significantly reducing noise while preserving critical geological features. The results demonstrate that ML seismic conditioning improves fault imaging, horizon continuity in areas with low signal-to-noise ratios (SNR), and localized smoothing without compromising resolution. Furthermore, the method presented in this paper retains signal fidelity and relative amplitude strengths, ensuring that geological interpretations remain accurate and reliable. By automating the traditional manual task of data conditioning, the proposed solution optimizes interpreter workflows, allowing them to focus on higher-level geological analysis and decision-making. This approach ultimately increases the accuracy of subsurface interpretations and resource exploration, improving the overall quality and efficiency of seismic interpretation projects. In addition, for 2D seismic, traditional methods of denoising rely on expert judgement using tools such as f-x deconvolution. 2D CNNs offer an efficient alternative, especially when training data can be synthesized and leveraged in supervised learning. This method can help the CNN identify and remove noise while preserving pertinent subsurface structures. This paper presents a 2D CNN architecture containing a model trained with synthetic seismic data to improve SNR of field seismic data; hence, interpretation.

The collection of a range of different seismic data types has greatly improved our understanding of the crustal architecture of Australia's Archaean Yilgarn Craton over the last few years. These seismic data include broadband seismic studies, seismic receiver functions, wide-angle recordings and mine-scale to deep seismic reflection transects. Each data set provides information on the three-dimensional (3D) tectonic model of the Yilgarn Craton from the craton scale through to the mine scale. This paper demonstrates that the integration and rationalisation of these different seismic data sets into a multi-scale 3D geological/seismic model, that can be visualised at once in a single software package, and incorporating all available data sets, significantly enhances this understanding. This enhanced understanding occurred because the integrated 3D model allowed easy and accurate comparison of one result against another, and facilitated the integrated questioning and interrogation across scales and seismic method. As a result, there are feedback questions regarding understanding of the individual seismic data sets themselves, as well as the Yilgarn Craton as a whole. The methodology used, including all the data sets in the model range, had to allow for the wide range of data sets, frequencies and seismic modes. At the craton scale, P-wave, S-wave and surface wave variations constrained the 3D lithospheric velocity model, revealing noticeable large-scale velocity variations within and across the craton. An interesting feature of the data, easily identified in 3D, is the presence of a fast S-wave velocity anomaly (> 4.8 km s − 1 ) within the upper mantle. This velocity anomaly dips east and has a series of step-down offsets that coincide approximately with province and terrane boundaries of the Yilgarn Craton. One-dimensional receiver function profiles show variations in their crustal velocity across the craton. These crustal velocity variations are consistent with the larger-scale geological subdivision of the craton, and provide characteristic profiles for provinces and terranes. The receiver function results and the deep seismic reflection data both agree on the depth to the Moho, and both indicate an increase in Moho depth to the east. The 2D seismic refraction results in the south-west of the craton provide crustal thickness information, an indication of middle and lower crustal compositions, and information regarding the broad-scale architectural framework. At the province- and terrane-scale, the deep seismic reflection data and the mine-scale seismic data provide geometric constraints on crustal architecture, in particular the orientation of the region's fault systems as well as variations in the thickness of the granite–greenstone succession. Integration of the results from wide-angle seismic refraction data coincident with the deep seismic reflection data provided additional constraints on likely upper crustal lithologies. The integrated 3D seismic model implies the dominant geodynamic process involved the development of an orogenic belt that developed with a series of contractional (folding and thrusting) events, separated by equally important extensional events. The seismic reflection data in particular suggests that extensional movement on many shear zones was more common than previously thought. The seismic reflection data suggest that the dominant mineral systems involved deeply sourced fluid flowing up crustal-penetrating shear zones. These deeply sourced fluids were further focussed into sites located above fault-breached domal regions in the upper crust.

Seismic facies analysis is increasingly being used in the interpretation of high-resolution seismic reflection data. Existing depositional systems models, based primarily on seismic data, have been constructed on the basis that acoustic character can be directly correlated with relatively consistent lithologies and geotechnical properties. However, borehole and shallow core data from the northern UK continental margin indicate significant inconsistencies in the use of this technique as a predictive tool. Changes in acoustic texture can occur laterally and vertically, in both lithologically homogeneous and heterogeneous sequences. The successful application of this technique can only be achieved by the integration of seismic data with other subsurface information, with interpretation based on sound geological concepts and models.

Interpreting three-dimensional (3-D) seismic data is of great value in high-grading prospects in frontier exploration areas in the Gulf of Mexico. This technique is now routinely used to reduce the exploration and production risks associated with turbidite reservoirs. A subregional 3-D seismic data set was used in this study to describe the Pliocene- Pleistocene turbidite systems in three intraslope minibasins in the north-central Green Canyon protraction area of the Gulf of Mexico. The stratal geometries and lithology of the different turbidite elements were interpreted based on the integrated analysis of well-log data, seismic facies observed on the vertical seismic profiles, and amplitude distribution recognized on series of horizon slices and amplitude extraction maps (horizontal seismic facies). The lithologic interpretation, however, was limited by the lack of seismic velocity data; the wells were tied to the seismic data through synthetic seismograms created by integrating the sonic and density logs. Three turbidite elements were mapped in the selected upper Pliocene-lower Pleistocene interval of the study area: basin-floor fan (amalgamated depositional lobes), erosional and depositional channels, and overbank sediments. Lobe-shaped high-amplitude areas (onlapping high-amplitude continuous reflections on seismic profiles) indicate laterally continuous, sheetlike deposition, interpreted as turbidite lobes. Elongated sinuous high-amplitude areas (high-amplitude discontinuous reflections) correspond to depositional channel systems. Sinuous low-amplitude areas in generally high-amplitude areas (e.g., sequence boundaries) reflect the erosional channels. The depositional channels are interpreted to be filled with sands; the shale-filled erosional channels are interpreted as conduits for sediments bypassing farther down slope. The detailed 3-D seismic interpretation allowed description of the characteristics of the channel systems, analysis of their evolution through 2 m.y., and determination of the controls on the variations in turbidite deposition. Widespread low- to moderate-amplitude areas without dominant amplitude patterns are interpreted as overbank deposits. The turbidite elements identified in this study, including basin-floor fans (depositional lobes) and channel systems, form the reservoirs in the fields of the study area and in many other fields and discoveries of the northern Gulf of Mexico. The geometries of the sand-rich turbidite elements mapped in this study are considered to be good analogs for these producing fields and some exploration targets. The results of this study also can be used as analogs for the description of reservoir architecture in the subsalt trend of the Gulf of Mexico and in other deep- water exploration areas.

An integrated sedimentological workflow was applied to the methane-hydrate exploration project in the eastern Nankai Trough area, central Japan, as a useful tool to investigate the distributions and actual volume of methane hydrates. Since previous research revealed that most of the methane hydrates in the eastern Nankai Trough area occur in matrix pores of submarine-fan turbidite sandstones, the facies distribution of turbidite sandstones may be one of the important keys to evaluate the occurrence of methane hydrates. Accordingly, this study conducted a) core facies analysis, b) log facies analysis mainly using FMI, c) depositional sequence division, d) seismic facies analysis on the 2D/3D seismic sections, and e) seismic geomorphological analysis on the 3D seismic survey data to map submarine-fan turbidite facies distributions for 17 depositional sequence horizons in the targeted Pleistocene interval. The obtained facies maps reveal that submarine-fan depositional styles changed throughout Pleistocene from a braided channel type, through small radial fan, trough-fill fan, and muddy sheet fan types, to a channel-levee system type. As the next step, the facies maps of each depositional sequence were overlaid with bottom simulating reflector (BSR) distributions as a proxy of methane hydrates. The overlaid maps indicate that the BSRs occur on feeder channels, distributary channels, and proximal lobes of submarine fans, suggesting that methane hydrates selectively occur in coarser grained portions of a submarine fan. The facies maps were also used for calculation of average net-to-gross ratio of the methane hydrate occurrence zones, as the maps provide information on the spatial distributions of seismic facies class, which has individual value of net-to-gross ratio. Finally, this study analyzed reservoir-scale geologic bodies, such as depositional lobes and braided channels of a submarine fan, using 3D seismic survey data, and constructed a detailed geologic model for simulations of methane-hydrate generation and production.

Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid content of the strata being imaged. Unfortunately, it is not always clear whether the seismic data has a natural clustering structure. Cluster analysis consists of a family of approaches that have significant potential for classifying seismic trace shapes into meaningful clusters. The clustering can be performed using a supervised process (assigning a pattern to a predefined cluster) or an unsupervised process (partitioning a collection of patterns into groups without predefined clusters). We evaluate and compare different unsupervised clustering algorithms (e.g., partition, hierarchical, probabilistic, and soft competitive models) for pattern recognition based entirely on the characteristics of the seismic response. From validation results on simple data sets, we demonstrate that a self-organizing maps algorithm implemented in a visual data-mining approach outperforms all other clustering algorithms for interpreting the cluster structure. We apply this approach to 2D seismic models generated using a discrete, known number of different stratigraphic geometries. The visual strategy recovers the correct number of end-member seismic facies in the model tests, showing that it is suitable for pattern recognition in highly correlated and continuous seismic data.

Over the last project six months, our project activities have concentrated on three areas: (1) performing a stochastic inversion of pattern 16 seismic data to deduce reservoir permeability, (2) development of the geochemical inversion strategy and implementation of associated software, and (3) completing the software implementation of TProGS and the geostatistical analysis that provides the information needed when using the software to produce realizations of the Midale reservoir. The report partially the following deliverables: D2: Model development: MCMC tool (synthetic fluid chemistry data); deliverable completed. D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product; deliverable requirements partially fulfilled. D5: Field-based single-pattern simulations work product; deliverable requirements partially fulfilled. When completed, our completed stochastic inversion tool will explicitly integrate reactive transport modeling, facies-based geostatistical methods, and a novel stochastic inversion technique to optimize agreement between observed and predicted storage performance. Such optimization will be accomplished through stepwise refinement of: (1) the reservoir model - principally its permeability magnitude and heterogeneity - and (2) geochemical parameters - primarily key mineral volume fractions and kinetic data. We anticipate that these refinements will facilitate significantly improved history matching and forward modeling of CO{sub 2} storage. Our tool uses the Markov Chain Monte Carlo (MCMC) methodology. Deliverable D1, previously submitted as a report titled ''Development of a Stochastic Inversion Tool To Optimize Agreement Between The Observed And Predicted Seismic Response To CO{sub 2} Injection/Migration in the Weyburn-Midale Project'' (Ramirez et al., 2009), described the stochastic inversion approach that will identify reservoir models that optimize agreement between the observed and predicted seismic response. The software that implements this approach has been completed, tested, and used to process seismic data from pattern 16. A previously submitted report titled ''Model verification: synthetic single pattern simulations using seismic reflection data'', Ramirez et al. 2010, partially fulfilled deliverable D3 by summarizing verification activities that evaluate the performance of the seismic software and its ability to recover reservoir model permeabilities using synthetic seismic reflection data. A future progress report will similarly describe summarizing verification activities of the geochemical inversion software, thereby completing deliverable D3. This document includes a chapter that shows and discusses permeability models produced by seismic inversion that used seismic data from pattern 16 in Phase 1A. It partially fulfills deliverable D5: Field-based single-pattern simulations work product. The D5 work product is supposed to summarize the results of applying NUFT/MCMC to refine the reservoir model and geochemical parameters by optimizing observation/prediction agreement for the seismic/geochemical response to CO{sub 2} injection/migration within a single pattern of Phase 1A/1B. A future progress report will show inversion results for the same pattern using geochemical data, thereby completing deliverable D5. This document also contains a chapter that fulfills deliverable D2: Model development: MCMC tool (synthetic fluid chemistry data). The chapter will summarize model development activities required to facilitate application of NUFT/MCMC to optimize agreement between the observed and predicted geochemical response to CO{sub 2} injection/migration. Lastly, this document also contains a chapter that partially fulfills deliverable D4: Model development/verification: MCMC tool (TProGS, field seismic/chemistry data) work product. This work product is supposed to summarize model development activities required for (1) application of TProGS to Weyburn, (2) use of TProGS within the MCMC tool, and (3) application of the MCMC tool to address field seismic and geochemical data. The chapter included here fulfills requirements 1 and 2. Requirement 3 will be addressed in a future progress report.

Seismic stratigraphic interpretation of megasequence and sequence framework in the Upper Cambrian through Devonian formations in the central part of the Darling Basin, western New South Wales, Australia

Seismic facies analysis is not a deterministic and simple task. Usually, facies analysis is performed through the following steps [1]: 1. Geological oriented spatial and temporal segmentation of seismic traces (input space); 2. Seismic attributes selection (variable space); 3. Choosing the optimal number of classes (facies) and algorithm iterations; 4. Training and classification of the selected attributes using some statistical or neural networks methods (pattern space); 5. Building and interpreting facies map. Normally, the geological oriented spatial and temporal segmentation should be carefully done, because any horizon interpretation error could lead to wrong or very noisy facies results. The attributes selection is another complex task, because they should be physically consistent and statistically independent. It’s common to use the whole seismic trace amplitudes around the region of interest [2]. Nowadays, Self Organizing Maps (SOM), or Kohonen Maps [3], has become one of the most popular tools to build seismic facies maps [4]. But, it’s still empirical how to choose the number of classes and the best seismic attributes to discriminate geological features from seismic data. This project presents a new alternative to extract seismic pattern attributes and a new methodology to build seismic facies maps. We propose using Wavelet Transform to identify singularities in each geological oriented segment of the temporal seismic trace and then using SOM in a two-level approach [5]. To illustrate this technique it was applied to real data from a deep-water field in the Campos Basin, Brazil.