Recurrent autoencoder model for unsupervised seismic facies analysis

Summary Deep learning have led to a certain extent breakthrough in seismic facies analysis(SFA). The greatest challenge in the SFA domain is how to cope with large seismic datasets and the limited amount of annotated samples, especially when employing supervised machine learning algorithms that require labelled data and larger training sample. we develop a supervised seismic facies analysis via an improved Auxiliary Classifier Generative Adversarial Network(ACGAN).To solve the problem of imbalanced seismic facies class distribution, and limited training data with labels, we used an improved ACGAN to generate synthetic seismic data sets, and then train Convolutional Neural Networks(CNN) using theses generated synthetic data. Fault data and seismic facies data test demonstrated that generated seismic data via the improved ACGAN can be used for synthetic data augmentation as for increased the performance of CNN and avoided overfitting compared to original seismic data training set. Therefore, we believe that the proposed method can effectively alleviate the problems of insufficient and imbalanced seismic data in SFA, and also can generalize to other seismic data classification applications.

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 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.

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.

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.

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.

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.

In a companion paper (Nivlet et al.,2005), we have described an application of seismic pre‐stack inversion to retrieve acoustic and shear impedance models in Girassol field. The next step, which will be discussed in this paper, is the lithoseismic interpretation of the inversion results. Traditional seismic facies analysis consists in extracting various amplitude maps of a reservoir level, and producing probabilistic or deterministic seismic facies maps. In this paper, we will present a workflow reaching beyond this traditional approach, by producing a 3D geological facies proportion model, which can then be used as a direct constraint to high resolution geological modeling techniques. The workflow consists of 5 main steps: • well log interpretation calibrated on core data; • upscaling of the facies database at the seismic scale; • choice of appropriate seismic attributes to discriminate betweenn the different facies, compensating for instance for the shale compaction with depth; • probabilistic seismic facies analysis from the chosen seismic attribute cubes supervised by upscaled well information; • post‐interpretation of seismic facies analysis results in terms of mean proportions of geological facies; • consistency check between the facies model and seismic impedances.

Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties. Independent of the adopted methodology to perform the seismic facies analysis, the geological oriented spatial and temporal segmentation of the reservoir regi

To solve the problem of low resolution of seismic data from granite weathering crust and difficulty in predicting effective reservoir distribution. Based on the research idea of combining geology and seismology, seismic facies characteristics and effective reservoir distribution of granite weathering crust are studied by means of frequency division reconstruction processing, seismic facies analysis and sensitive attributes interpretation. This method consists of the following four main processes: (1) Using improved Morlet wavelet function for frequency division and reconstruction processing of seismic data; (2) From the perspective of geological evolution, analyzing the geological origin of seismic facies; (3) Based on the results of well- seismic combination, clarifying the types of effective reservoirs and predicting their distribution; (4) Comprehensive research indicating favorable reservoirs distribution. The results indicate that (1) a considerably improved resolution of seismic data processed by frequency division reconstruction is obtained based on the improved Morlet wavelet function. Thus, granite reservoirs then have clearer internal reflection characteristics. (2) Affected by differences in weathering, the seismic facies in granite weathering crusts can be classified as follows: Strong-amplitude facies with amplitudes of 23,700 − 32,800, corresponding to eluvium, represents the late stage of weathering crust evolution with high-intensity weathering. Medium-amplitude facies with amplitudes of 5,250 − 24,000, corresponding to dissolution layer, represents a middle stage of weathering crust evolution with moderate weathering. And weak-amplitude-blank facies with amplitudes of 0–5,330, corresponding to disintegration layer, represents an early stage of weathering crust evolution with weak weathering. (3) Effective reservoirs can be classified according to well-seismic calibrations and combined with reservoir spaces, porosities and permeabilities, and the logging and oil-bearing characteristics of the reservoirs. Type I effective reservoir comprises medium-amplitude facies, with favorable reservoir conditions and high-yield oil-bearing properties; this type is distributed in the western slopes, with an elevation of 1,300-2,500 m. Type II effective reservoir comprises strong-amplitude facies, with poor oil-bearing properties; this type is mainly distributed in the southeast and north slopes of the study area, with a relatively limited range, and the elevation is mostly below 1,500 m. Type III effective reservoir comprises weak-amplitude-blank facies, with poor reservoir conditions and poor oil-bearing properties; this type is widely distributed at various elevations. (4) The areas where medium-amplitude facies have developed are the most favorable reservoir exploration zones for granite in Dongying Sag, Bohai Bay Basin.

Seismic stratigraphy has developed almost as three separate disciplines. The first, pioneered by Vail and others (1977), is seismic sequence analysis. Sequence analysis is the study of recognizing and correlating regional stratal surfaces to define genetically related rock units that represent discrete chronostratigraphic intervals (Van Wagoner et al., 1988). Seismic data are used extensively in sequence stratigraphy, as primary seismic reflections are generated by time-correlative bedding surfaces, and surfaces that separate sets of contemporaneous depositional systems (systems tracts) (Van Wagoner et al., 1988).

Seismic facies analysis was performed on approximately 1540 km of 2D high-resolution multi-channel seismic data from the German North Sea. Six seismic facies were defined in the study area. Facies 1 covers the Quaternary sand depositional system. Facies 2, 3, and 4 represent three types of tunnel-valley infill. These valleys were formed during different glacial periods in the study area. All of them are characterized by erosional incision into underlying deposits. Facies 5 probably represents the youngest post-glacial sediments after the retreat of last glacier. Facies 6 is a typical seismic reflector for peat.