Search Results (9)

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

In recent years, significant exploration successes and research progress in volcanic hydrocarbon reservoirs across China’s offshore basins have highlighted their importance as key targets for deep hydrocarbon exploration. In the Shimentan Formation of the Xihu Sag, East China Sea Shelf Basin (ECSSB), low-yield gas flows have been encountered through exploratory drilling; however, no major reservoir breakthroughs have yet been achieved. Assessing the large-scale reservoir potential of volcanic sequences in the Shimentan Formation is thus critical for guiding future exploration strategies. Based on previous exploration studies of volcanic reservoirs in other Chinese basins, this study systematically evaluates the hydrocarbon potential of these volcanic units by microscopic thin section identification, major element analysis, integrates drilling data with seismic interpretation techniques—such as coherence cube slicing for identifying volcanic conduits, dip angle analysis for classifying volcanic edifices, and waveform classification for delineating volcanic lithofacies. The main findings are as follows: (1) The Shimentan Formation is primarily composed of intermediate to acidic pyroclastic rocks and lava flows. Volcanic facies are divided into three facies, four subfacies, and six microfacies. Volcanic edifices are categorized into four types: stratified, pseudostratified, pseudostratified-massive, and massive. (2) Extensive pseudostratified volcanic edifices are developed in the Hangzhou Slope Zone, where simple and compound lava flows of effusive facies are widely distributed. (3) Comparative analysis with prolific volcanic reservoirs in the Songliao and Bohai Bay basins indicates that productive reservoirs are typically associated with simple or compound lava flows within pseudostratified edifices. Furthermore, widespread Late Cretaceous rhyolites in adjacent areas of the study region suggest promising potential for rhyolitic reservoir development in the Hangzhou Slope Zone. These results provide a robust geological foundation for Mesozoic volcanic reservoir exploration in the Xihu Sag and offer a methodological framework for evaluating reservoir potential in underexplored volcanic regions. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Granite and gneiss buried hill reservoirs are controlled by their lithology and dark mineral content. Therefore, lithological identification and dark mineral content analysis are important research tools in the early stage of buried hill exploration. In this paper, the relationships between the seismic facies and lithology, magnetic susceptibility, and magnetic anomalies of granite and gneiss are analyzed based on the lithological characteristics of the LiShui depression (LS depression) in the East China Sea Basin (ECSB). The waveform classification method is used to identify granite and gneiss, and the waveform classification results reveal that areas with continuous distribution of a single seismic trace model or two seismic trace models represent good continuity, and can be interpreted as gneiss. Areas with a mixed distribution of multiple seismic trace models represent chaotic and poor continuity, which can be interpreted as granite. The mixed linear zone with multi-seismic trace models is a fault zone, and the rock is cataclasite. In addition, reduction to the pole (RTP) and downward continuation technique for magnetic data processing were used to determine the dark mineral content. Overall, the granite and gneiss can be divided into three types based on magnetic anomaly data: high, moderate, and low magnetic anomaly areas. The areas in which granite with moderate and low magnetic anomalies is distributed are the favorable exploration target areas. The above method provides a technical means of lithological identification in the early stage of buried hill exploration. Full article

The interpretation of subsurface fluid flow features in seismic reflection data is a key part of identifying the presence of hydrocarbon and active petroleum systems. Currently, this kind of study is mainly conducted utilizing offshore seismic reflection data with very limited cases utilizing onshore seismic reflection data. In addition, the Alamein basin is an area of prolific study in onshore Egypt, with most related studies concentrating on basin analysis and reservoir characterization. Therefore, in our study we aimed to make practical and effective use of onshore seismic reflection data with seismic attribute analysis to describe seismic facies, delineating and interpreting subsurface fluid flow features. The relatively vertical V-shaped and pipe or concave-up-shaped features with distorted reflections inside them are revealed through the analysis of variance, sweetness, chaos, instantaneous frequency and the RMS amplitude of seismic attributes. These subsurface fluid flow features are a product of mature source rock that migrates hydrocarbon vertically through faults (especially deep-seated faults) and fracture systems (fault and fracture-related subsurface fluid flow features). The classification scheme presented in our study could be implemented in the onshore case worldwide. Full article

The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions. Full article

Hybrid event beds represent the combined effect of multiple geological processes, which result in complex depositional geometries and distinct facies distribution in marine environments. Previous work on hybrid event beds highlights the classification, origin, and types of hybrid facies. However, in the present study, we discuss the development of hybrid event beds in submarine lobes with an emphasis on the analysis of proximal to distal, frontal to lateral relationships and evolution during lobe progradation. Detailed geological fieldwork was carried out in the classical deep-marine Late Paleogene Crocker Fan to understand the relationship between the character of hybrid bed facies and lobe architecture. The results indicate that hybrid facies of massive or structureless sandstone with mud clasts, clean to muddy sand, and chaotic muddy sand with oversized sand patch alternations (H1–H3) are well developed in proximal to medial lobes, while distal lobes mainly contain parallel to cross-laminated clean to muddy hybrid facies (H3–H5). Furthermore, lateral lobes have less vertical thickness of hybrid beds than frontal lobes. The development of hybrid beds takes place in the lower part of the thickening upward sequence of lobe progradation, while lobe retrogradation contains hybrid facies intervals in the upper part of stratigraphy. Hence, the development of hybrid beds in submarine lobe systems has a significant impact on the characterization of heterogeneities in deep-marine petroleum reservoirs at sub-seismic levels. Full article

This study focuses on the sequence stratigraphy and the dominated seismic facies in the Central Taranaki basin. Four regional seismic sequences namely SEQ4 to SEQ1 from bottom to top and four boundaries representing unconformities namely H4 to H1 from bottom to top have been traced based on the reflection terminations. This was validated using well logs information. An onlapping feature on the seismic section indicates a new perspective surface separated between the upper and lower Giant formation, which indicates a period of seawater encroachment. This study focused extensively on deposition units from SEQ4 to SEQ1. The seismic facies, isochron map, and depositional environment were determined, and the system tract was established. This study was also able to propose a new perspective sequence stratigraphy framework of the basin and probable hydrocarbon accumulations and from the general geological aspect, SA-Middle Giant Formation (SEQ3) could act as potential traps. Full article

Formation evaluation is a critical requirement in oil and gas exploration and development projects. Although it may be costly, wireline logs need to be acquired to evaluate and understand the subsurface formation. Gamma ray and resistivity are the two main well-log data used for formation evaluation purposes. However, outside the well, formation evaluation becomes difficult, as these logs are not available. Hence, it is important to have other data equivalent to the gamma ray or resistivity logs, which can be derived from other technique, such as seismic data. As a consequence, the dependency on well-log data can be avoided. Thus, the complexity in formation evaluation outside the well, such as the determination of facies, lithology, and fluid content, as well as petrophysical properties can be solved accurately even without well-log data. The objective of this paper was to demonstrate an application of the SQp and SQs attributes for facies classification. These attributes were derived from attenuation attributes through rock physics approximation by using basic elastic properties: P-wave, S-wave, and density. A series of tests were carried out to show the applicability of these attributes on well-logs and real seismic data from offshore the Malaysia Peninsular. Simultaneous inversion was used in the data sets to produce the three-dimensional (3D) SQp and SQs attributes required as inputs of a neural network engine in defining the facies distribution. The results showed that the SQp attribute was very similar to the gamma ray, while the SQs attribute was similar to the resistivity responses even in different reservoir conditions, including low resistivity low contrast and coal masking environment. In conclusion, the SQp motif, which is similar to the gamma ray motif, can potentially be used for facies classification/identification. Together with the SQs attribute, the SQp attribute can be used as input for the facies classification workflow. The application of the SQp and SQs attributes successfully identified the gas sand distribution and separated it clearly from the brine distribution in an offshore Malaysian field. Full article