Facies analysis from pre‐stack inversion results in a deep offshore turbidite environment

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.

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 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 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.

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.

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.

This study examines the methods of analysis and interpretation of geological and geophysical data aimed at identifying and describing clinoform structures and their properties in the Ustyurt region. The work utilizes 3D seismic survey data, geophysical well studies, and the results of seismic facies analysis and PMLI inversion to assess the porosity and gas saturation of the formations. The objective of this work is to study the clinoform structure of Paleogene gas deposits in the Ustyurt region and to identify the geological patterns governing the formation of productive horizons within clinoform structures. This aims to improve prediction accuracy and the efficiency of oil and gas field development. The focus is on analyzing attribute maps such as Signal Envelope, Vp/Vs, and Fluid Factor, which have allowed for the identification of zones with improved reservoir properties and the determination of promising drilling sites. These maps have shown a high degree of correlation with GIS data, enhancing the reliability of forecasts and optimizing field development processes. The study demonstrates that a comprehensive approach, including seismic facies analysis and PMLI inversion, is an effective tool for identifying productive horizons and zones with high reservoir properties. The results obtained provide opportunities for improving the accuracy of geological predictions and increasing the efficiency of gas extraction in the region. Future research is expected to further refine existing models and develop new methods for hydrocarbon exploration and extraction in challenging geological environments. Keywords: Ustyurt region; Paleogene; clinoform structures; seismic attributes; PMLI inversion.

A 3D integrated saturation model was built for the Sierras Blancas Formation of the Neuquén Basin, Argentina. The saturation model was based on core, logs and seismic data. History match of reservoir pressure and well productivities were taken into account to accurately determine the gas in place and productive reservoir boundaries, specifically using 3D seismic water saturation in the gas condensate formation. The Sierras Blancas Formation is an eolian deposit. In tight, wet and diagenetically altered regions, the seismic inversion porosity and acoustic impedance based models were not adequate to describe the gas in place distribution. Further, the effective gas permeabilities in the tighter part of the reservoir are a strong function of the initial water saturation as evidenced by fewer condensate and water blocking problems of horizontal wells that navigated through low water saturation, high permeability regions. Any relationship between seismic impedance and porosity correlation degraded in areas affected by secondary diagenetic processes therefore necessitating the use of a saturation parameter. 30 vertical wells that had DT curves were selected based on their production and spatial location in order to establish a correlation between log saturation and seismic attributes. Seismic saturation cubes were generated by multiattribute seismic analysis and resampled into the simulation scale model. Log saturations were then co-kriged with the 3D seismic saturation. Water saturations obtained from the initialized simulation scale model were compared with the 2D saturation logs, the 3D seismic and the geological model scales. An objective function was defined to match the 3D seismic water saturation with the initialized simulation model water saturation. Model parameters were iterated until a satisfactory match with the initialized simulation model was obtained.By focusing the saturation match at the initialization stage, seismic, geological, petrophysical and SCAL models were ensured to be consistent prior to the full history match. Well history matching was consequently achieved much more simply and quickly. This paper presents a new detailed methodology of 3D pseudo-seismic water saturation generation, modeling and simulation used to accurately define OGIP, the productive boundaries of the reservoir, and to design trajectories for new horizontal wells.

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.

Facies models of two coalfields in Canada have been developed through seismic facies analysis. Fluvial-deltaic Upper Cretaceous coals near Camrose, Alberta, are characterized by hummocky, subparallel, high-amplitude reflections that are laterally continuous over distances of several kilometers. Deposition in an extensive delta-plain swamp is inferred. Coals of Pennsylvanian age in the Pictou coalfield, Nova Scotia, occur in two distinct seismic facies. Parallel to subparallel, relatively continuous reflections are interpreted to define a basinal facies comprising thick coals and lacustrine sediments. Basinal margin facies contain a greater abundance of variable clastic sediments and are recognized by discontinuous, subparallel to chaotic reflections. Further improvements n stratigraphic analysis will require synthetic seismograms to analyze individual reflections in terms of the thickness and separation of seams, and to enable interseam multiples to be recognized.

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.

Актуальность исследования обусловлена необходимостью увеличения сырьевого потенциала месторождений Западной Сибири, приуроченных к доюрским отложениям, с минимальными затратами на дополнительную разведку. Такая возможность связана с резким развитием технологий сейсмической разведки в последние годы. Современная сейсмическая 3D съёмка позволяет прослеживать характерные особенности геологического строения палеозойских отложений, которые было невозможно различить на профильных сейсмических разрезах 2D. Для получения наиболее объективных результатов целесообразно применение комплекса методов, включающих, помимо сейсморазведки, методы геофизических исследований скважин. Цель: выделить перспективные участки для освоения доюрского комплекса по данным сейсморазведки на примере одного из месторождений Томской области, приуроченных к палеозойскому фундаменту Западной Сибири. Объекты: отложения палеозойского фундамента юго-восточной части Западно-Сибирской плиты, характеризующиеся сложным вещественным составом горных пород, складчато-блоковым тектоническим строением, наличием естественной трещиноватости карбонатных коллекторов, порово-каверново-трещиным типом коллектора. Методы: специальный анализ сейсмических атрибутов; сейсмический фациальный анализ; комплексирование сейсмических методов и результатов интерпретации скважинных данных. Результаты. Выполнен анализ пространственного изменения сейсмических атрибутов, на основе которого выделены зоны повышенной трещиноватости и проведена оценка преобладающего направления систем трещин. Применение сейсмофациального анализа позволило выявить приуроченность участков повышенной плотности трещин к породам с улучшенными коллекторскими свойствами. Построенная схема перспектив нефтегазоносности палеозойских отложений на примере одного из месторождений Западной Сибири удовлетворительно согласуется с результатами геофизических исследований скважин и данными лабораторного изучения керна.

Summary Given paper describes how integration of different methods such as core data, well logs, production logging, seismic data and well test analysis was used to solve the problem with determination of gas accumulation boundaries in sediment complex PK1–3 of the Field M. Given work is devoted to the block with wells 2,36,49,85,127,148 of the field, since it is characterized by high uncertainty, sc. recently drilled wells 1V, 2V and 120 have produced oil although according to present day geological concept they were considered to be gas saturated in intervals investigated by production logging. Besides well 127, which was presumably oil saturated, has produced gas. Accounting mismatching production data and geological concept the author has supposed that PK1–3 gas accumulation is characterized by more complex structure than it was supposed by predeccesors and it is represented by reservoir compartmentalization and high heterogeneity. Therefore the main aim of work was to revise distribution of gas saturated reservoir within PK1–3 sediment complex. To reach the aim the author has set the following tasks: to revise geological correlation and gas oil contact; to carry out fault interpretation by means of seismic and well test data; to determine areal facies distribution on the base of integrated core, log motifs and seismic facies analysis. Thus, estimation of gas saturated reservoir portion was realized by two stages: defining the boundary of gas accumulation in depth on the base of well logs, production data and fault interpretation; reservoir distribution determination on the base of seismic facies analysis within derived gas accumulation boundary.

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.

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.