Subsurface Geobody Imaging Using CMY Color Blending with Seismic Attributes

The Tuaheni Landslide Complex, located on the upper slope of the northern Hikurangi Margin in New Zealand, is a unique place to research on slow slip creep-like deformation and seabed failure, as well as their possible relationship with the presence of gas hydrates, cold seeps, and fluid migration. Based on the visual interpretation of seismic data, it is sometimes very difficult to identify various subsurface structures and tectonic features. We study certain seismic attributes, namely the reflection strength, instantaneous frequency, instantaneous phase, and the Hilbert transform, in the Tuaheni Landslide Complex and observe that these attributes play a very important role in identifying and interpreting various subsurface geological features and bed boundaries that are not clearly visible in the seismic sections. In general, these seismic attributes are studied to identify hydrocarbons such as oil and gas. However, in this present study these seismic attributes nicely illustrate the fluid migration pathways, the decollement of the sediment slide, the base of the debris flow, the base of the deformed sediment and gas migration, etc., along two perpendicular seismic profiles crossing the Site U1517 of IODP Expedition 372. The instantaneous phase and Hilbert transform attribute depict the bed boundaries and discontinuities, whereas the reflection strength and instantaneous frequency attributes characterize the various strata in terms of whether they are associated with fluid at their bases. The possible role of tectonic activity and seafloor slope failure due to gas hydrate dissociation and vice versa is clearly visible through fluid-filled weak zones in the seismic attribute volumes. Gas hydrates are dissociating and BSRs are abruptly pinching out towards the seafloor due to the movement of hot fluid and free gas, enhancing seafloor sliding and local tectonic activities together.

Sand-lithofacies distributions are a vital component of reservoir characterisation and the generation of successful reservoir models, particularly in thin reservoirs thickness. The integration between seismic attributes and conceptual geologic models is important to identify the sand distributions and reservoir geometries. The aim of this work is illustrating the uses of seismic data in revealing the structural and stratigraphic elements of two main reservoir units. It is important to identify and list the attributes, which can be involved in analysing the reservoirs and their degree of involvement in the characterisation of the hydrocarbon accumulation zones. The study was carried out for the reservoirs: Abu Roash C and Abu Roach E in Sitra oil and gas concession, which is located in the northern part of the Egyptian Western Desert. Four seismic attributes are used: 1- Coherence (Variance) 2- 3D Curvature attributes were used for resolving mainly the structural features and also the stratigraphic features 3- Instantaneous phase 4- Instantaneous frequency attributes were used mainly for resolving the stratigraphic features. Through the integration of selected seismic attributes with post-stack seismic data, reservoir distributions and architectures are accurately delineated for the Upper Cretaceous reservoir (Abu Roash ‘C’ and ‘E’). Five wells were used to match the results of the seismic attributes and establish the regional trends of reservoir parameters. The integration of wells data, conventional seismic interpretation and extracted the relevant seismic attributes help in reaching a robust geologic model for the reservoirs of interest. Stratigraphic and structural interpretation objectives based on extracting the seismic attributes for reservoir characterisation become more dominant, as they go deeper in seismic data, to derive more subsurface geologic data than traditional interpretation. 3D structure model of the concern area was established by interpreting the available seismic data as both reservoirs implicated by two fault trends: NW-SE and ENE-WSW. By the end a 3D geological model was defined based on the results of the conventional interpretation of the seismic and seismic attributes to reflect all structural and stratigraphic elements of the two reservoir units.

This work has the purpose to understand the relation that are between seismic attributes extracted from 2D high-resolution seismic data and the sediment surveyed. To accomplish this goal, we performed a research using 2D high-resolution seismic data acquired on the Continental Shelf of the Antarctic Peninsula, nearby the South Shetland Islands, and sediment samples acquired with gravity corer overlapping each seismic line surveyed. The geologic samples were used as control samples to make the binding between seismic traces - sediment cores and to fulfill the sediment classification software training outline. One computational script was written to extract the seismic attributes from the seismic data and in sequence the attributes were statistically analyzed. The attributes were used as variables during the statistical process and they were: Amplitude (AMP), Instantaneous Phase (PHI), Instantaneous Frequency (FREQ), Envelope (ENV), Time Derivative of the Envelope (DTENV), Second Derivative of the Envelope (DT2ENV) and Acceleration of Phase (ACPHI). The statistical analyses were: Principal Components (PCA), Dendrograms and K-Means Classification. Four kinds of seismic attributes groups were coherently formed on statistical analyses. In theory basis, the PHI shouldn't be grouped with any other attribute because of its physical behavior completely distinct from the others attributes here studied. The sets of attributes formed that presented ACPHI were absolutely coherent, in a way that all others attributes grouped within ACPHI present the same physical and spatial behavior such as DTENV, AMP, ENV and FREQ, this is due to the absorption of both the amplitude and frequency of the wave propagated through the medium. Therefore, it is believed that both more coherent sets of attributes are AMP and ENV identified by all the statistical analyses and DTENV and ACPHI formed by the PCA and Dendrograms analyses, and are both well supported by the other analyses that assured an intrinsic physical and spatial relation between the grouped attributes. The subsequent step on the Semi-Automatic Sediment Classification Process using 2D high-resolution seismic data were to prepare the set of attributes that grouped coherently, to identify the range of values of each attribute grouped that correspond to the location of each core sample, locate on the reflectors in the seismic section where those ranges of values from the set of attributes are found at the same place and finally suppose that on these regions identified by the script we have the same kind of sediment found on the geological samples.

ABSTRACTSeismic detection of faults, dykes, potholes and iron‐rich ultramafic pegmatitic bodies is of great importance to the platinum mining industry, as these structures affect safety and efficiency. The application of conventional seismic attributes (such as instantaneous amplitude, phase and frequency) in the hard‐rock environment is more challenging than in soft‐rock settings because the geology is often complex, reflections disrupted and the seismic energy strongly scattered. We have developed new seismic attributes that sharpen seismic reflections, enabling additional structural information to be extracted from hard‐rock seismic data. The symmetry attribute is based on the invariance of an object with respect to transformations such as rotation and reflection; it is independent of the trace reflection amplitude, and hence a better indicator of the lateral continuity of thin and weak reflections. The reflection‐continuity detector attribute is based on the Hilbert transform; it enhances the visibility of the peaks and troughs of the seismic traces, and hence the continuity of weak reflections. We demonstrate the effectiveness of these new seismic attributes by applying them to a legacy 3D seismic data set from the Bushveld Complex in South Africa. These seismic attributes show good detection of deep‐seated thin (∼1.5 m thick) platinum ore bodies and their associated complex geological structures (faults, dykes, potholes and iron‐rich ultramafic pegmatites). They provide a fast, cost‐effective and efficient interpretation tool that, when coupled with horizon‐based seismic attributes, can reveal structures not seen in conventional interpretations.

Accurate three-dimensional acoustic impedance modeling in offshore clastic reservoirs remains a significant challenge due to sparse well control and the highly nonlinear relationship between seismic attributes and subsurface elastic properties. This study introduces an integrated, physics-guided machine learning (ML) workflow that combines rock-physics-driven pseudo-well generation with neural networks to directly map seismic attributes to acoustic impedance under data-limited conditions. A soft-sand rock physics workflow was applied, in which grain moduli were determined using the Voigt-Reuss-Hill average. The dry rock frame was modeled at critical porosity by Hertz-Mindlin contact theory and then interpolated toward zero porosity using the Modified Hashin-Shtrikman lower bound. Gassmann fluid substitution was subsequently performed. Using this approach, 45 pseudo-wells were generated and conditioned through lithofacies classification and spatial statistics, mitigating the risk of overfitting associated with the three available real wells. Six seismic attributes-envelope, RMS amplitude, instantaneous phase, instantaneous frequency, quadrature trace, and sweetness-were selected as predictors. Two neural architectures, a multi-layer feedforward network (MLFN) and a radial basis function network (RBFN), were trained and benchmarked using a leave-one-well-out cross-validation scheme. The MLFN achieved higher predictive accuracy (CC = 0.87, NRMSE = 0.493) compared to the RBFN (CC = 0.79, NRMSE = 0.613), which may reflect its greater capacity to model broader hierarchical relationships between seismic attributes and acoustic impedance. The resulting impedance volume delineates laterally coherent high-impedance sandstone units and low-impedance porous intervals consistent with geological interpretation. These results suggest that integrating physics-guided pseudo-well augmentation with feed-forward neural networks offers a practical and computationally efficient approach for acoustic impedance inversion in data-limited offshore settings. Future work may explore validation across diverse geological settings to assess the robustness and transferability of the proposed methodology. This study provides a basis for hybrid and uncertainty-aware inversion frameworks that may help address complexities in heterogeneous reservoir systems, highlighting the importance of reproducible and widely applicable data-driven seismic inversion methods under sparse well control.

Summary Geological subtle difference identification poses great challenge to seismic interpretation. Although some effective seismic attributes (e.g. instantaneous amplitude, instantaneous phase, instantaneous frequency, coherence etc.) have been developed to map changes in seismic data, subtle changes in detail are still difficult to be distinguished by these methods. Therefore, it is necessary to explore the better attribute with far greater detail than these conventional ones. Unlike most seismic attributes based on linear theory, logistic nonlinear convergence rate attribute we proposed in this paper generated from a nonlinear system - logistic equation. Logistic equation is a famous polynomial nonlinear iterative dynamical system which is often cited as a chaos producer. However, few works pay attention to nonlinear convergence of the equation. This paper just focused on this part and discussed application of logistic nonlinear convergence. Method experiments demonstrated that logistic nonlinear convergence rate can be conducive to magnify subtle differences. And the case study in Tarim illustrated that our method did be sensitive to subtle differences between reservoirs and its geological background in seismic responses, and can well delineate the distribution of reservoirs

3D seismic reflection study was applied to Abu Amood oil field which is located at the southern part of Iraq within DhiQar province that carried out by oil Exploration Company to an area of 1534.88 Km2 for studying Nahr Umr Formation. Synthetic seismogram was prepared by using available data of well (AAM-1) in order to define and picking the reflectors on the seismic section. These reflectors are (Top of Nahr Umr Formation and middle unit of Nahr Umr Formation which represents the layer of sand). The seismic section time slice maps confirmed that the Nahr Umr Formation was not affected by faults and the faults may probably present in the Ratawai and Yamama Formations, where the variance attribute applied on seismic sections showed that the area was affected by normal fault, this fault was very deep. The time, velocity, and depth maps are drawn for the top of Nahr Umr Formation to get the structural picture: these maps showed that Abu Amood oil field was a semi symmetrical structure that has an axis trending NW-SE, the slope of NE limb is greater than SW limb. Isochrone and Isopach maps are drawn for Nahr Umr Formation to display thickness of Nahr Umr Formation which gradually increases to the west as well as NW and SW parts of the study area and more gradually increases to the East and NE part of the study area. Seismic attributes were applied to the study area (instantaneous phase, instantaneous frequency and variance attribute) these attributes showed the presence of Direct Hydrocarbone Indicator at Nahr Umr Formation in Lower Cretaceous age as a Dim spot at In-lines 51500 and 49500 by applying instantaneous phase and bright spot at AAM-1 well by applying instantaneous frequency.

Problem of combining well logs with seismic data is still a topic of discussions. In this paper full wave sonic logs and seismic attributes are proposed as an innovative way of combining these two measurements. Data from the sandy shaly Miocene formations and the carbonates from Carboniferous formation in the Carpathian Foredeep were analyzed in this paper. Attributes for acoustic waves (P, S and Stoneley) - Amplitude Envelope and Instantaneous Frequency - were calculated. On the other hand, the best set of seismic attributes for prediction the log well data was chosen using multi-linear regression. Next, the log derived attributes were upscaled to the seismic wavelength. Finally, these two types of attributes were compared and some relations were found.

Heavy crude oil is known as oil that is highly viscous and of a higher density than that of conventional oil. Sand reservoirs containing heavy oil generally consist of unconsolidated sediments deposited at a shallow burial depth, with high porosity and permeability. In seismic exploration, acoustic impedance inversion is a commonly used tool in reservoir prediction. However, due to the unconsolidated characteristic of heavy oil reservoirs, the wave impedance difference between heavy oil sandstones and mudstones becomes less apparent, thus limiting the ability of impedance inversion to accurately characterize the reservoir. Therefore we must expand our characterization of the target heavy oil reservoirs to include correlation analysis of different seismic attributes to the unconsolidated reservoir thickness. The results show that there has a strong correlation between the seismic attribute value of instantaneous frequency and unconsolidated reservoir thickness, more than other seismic attributes in the target strata. Thus the instantaneous frequency attribute can be used to predict qualitatively the lateral distribution of unconsolidated reservoirs, which in turn, indicates the vertical variation of thickness for the unconsolidated reservoirs. By using frequency attributes which are sensitive to unconsolidated sediments, coupling with additional geologic information, we can predict the distribution of sedimentary facies accurately in the study area, which results in a more reliable prediction for the lateral and vertical distributions of heavy oil reservoirs.

As petroleum resources run out, subtle stratigraphic reservoirs have become an important source of future discoveries. Continental tertiary deposits of fluvio-deltaic origin are known to produce such complex reservoirs and pose a challenge for characterization. We propose a novel bio-integrated framework called multi-modal machine learning fusion to tackle this challenge. The approach bases reservoir characterization on biological interpretation of sensory data. Using seismic attributes as varied modal inputs and rock physics relationships as input data, we perform multi-modal machine learning fusion to predict the gamma ray spatial distribution. Before processing however, the seismic data is filtered with a dip steered filtering algorithm to remove random noise. The resulting volume is then used in computing the seismic attributes. Effective seismic attributes utilized were, instantaneous amplitude, instantaneous frequency, spectral decomposition, seismic inversion and similarity. Results from a case study in the Bohai Bay Basin in Eastern China demonstrate that multi-modal machine learning fusion can predict the spatial distribution of lithology using gamma ray logs. The conclusions from this study can be applied in analogous reservoirs in similar petroleum provinces in other regions of the world and can also be applied to other geophysical interpretational methods.

A robust algorithm for the simultaneous estimation of dip, azimuth and curvature in the depth migrated domain is developed. The algorithm makes use of instantaneous‐attribute beamforming in small windows aligned with locally coherent migrated events. This is the following two‐step procedure: inline and cross‐line linear beam stacks to compute the two local dips at image points; 360‐degree azimuthal parabolic beamforming to estimate azimuthally dependent curvatures with respect to the plane tangent to the instantaneous‐phase surface at the image point. Amplitude and phase content of migrated traces are processed independently using complex trace analysis in order to facilitate this procedure. The instantaneous phase is of central importance since it describes the lateral continuity of events and defines instantaneous dips corresponding to instantaneous apparent phase velocity and group velocity. This attribute is used to compute the instantaneous frequency that defines the beam wavelet at the peak of waveform envelope. The instantaneous amplitude or envelope represents an input to model‐based wavefield (Born‐Kirchhoff) de‐remigration of picked events, useful for dip QC. The QC analysis involves thresholding of modeled migrated amplitudes predicted by Kirchhoff remigration of the Born demigrated wavefield. This also includes simple thresholding of the instantaneous frequency. The analysis is shown to be capable of eliminating undesired events. The algorithm was tested using a 3‐D marine streamer dataset with high noise. The success with field migrated data demonstrates that the algorithm is particularly useful for slope‐limited tomographic velocity updates and structural interpretation when the signal‐to‐noise ratio (SNR) is low.

Over the past few years, the use of machine learning has gained considerable momentum in many industries, including exploration seismic. While supervised machine learning is increasingly being used in seismic data analysis, some obstacles hinder its widespread application. Seismic facies classification—a crucial aspect in this field—particularly faces challenges such as the selection of appropriate input attributes. Plethora of seismic attributes have been created over the years, and new ones are still coming out. Yet, several have been deemed redundant or geologically meaningless. In the context of machine learning, it is crucial to avoid these redundant and irrelevant attributes as they can result in overfitting, building unnecessary complex models, and prolonging computational time. The current study incorporates an attribute selection approach to seismic facies classification and evaluates the importance of several available seismic attributes. Two datasets from the AN Field and the Dangerous Grounds region offshore Malaysia were utilized. Several attribute selection techniques were evaluated, with most of them yielding perfect attribute subsets for the AN dataset. However, only the wrapper and embedded methods could produce optimal subsets for the more complex Dangerous Grounds dataset. In both datasets, distinguishing the targeted seismic facies was mainly dependent on amplitude, spectral, and gray-level co-occurrence matrix attributes. Furthermore, spectral magnitude components played a significant role in classifying the facies of the Dangerous Grounds broadband data. The study demonstrated the importance of attribute selection, established a workflow, and identified significant attributes that could enhance seismic facies classification in Malaysian basins and similar geologic settings.

In order to accurately identify the distribution of the karst water conduction channel after water inrush, the ground penetrating radar (GPR) is used for fine detection of development of karst water conduction in front of the heading surface after water inrush, and the instantaneous amplitude, instantaneous phase and instantaneous frequency are extracted from the radar signal by Hilbert transform. Then, the three instantaneous properties are combined for multi-parameter comprehensive analysis of the karst water conduction channel. As shown in the study results, the instantaneous amplitude can better show the boundary characteristics and morphological size of the karst water conduction channel. The instantaneous phase can clearly show the heterogeneous characteristics inside the karst water conduction channel. The instantaneous frequency can reflect the water richness of the karst water conduction channel from the angle of energy decay. The combination of the three instantaneous attributes can avoid the explanatory bias due to the analysis of single waveform characteristics, and effectively improve the resolution and accuracy of the detection of the karst water conduction channel.

Examples of seismic diffraction imaging from the Austin Chalk and Eagle Ford Shale, Maverick Basin, South Texas

The defect inspection in traditional metal processing usually adopts the manual visual inspection, which is easy to cause problems such as false detection and leakage. Besides, the detection rate of existing metal defect inspection has been affected by the high reflectivity of metal surface. For avoiding the problems of existing detection algorithms, a metal defect detection system based on laser triangulation and laser vision is given. The system is composed of industrial camera and strip light source. In order to avoid the interference of metal reflection on defect detection, the green strip light is used as the light source of the system. Based on the proposed system, this paper presents a metal surface detection algorithm with three primary color channels, that is, metal surface defects are identified by identifying the RGB values of metal surface pixels. Finally, through static experiments, the detection rate of the metal defect surface detection system based on laser vision is 100%. The defect detection method proposed in this paper can effectively avoid the influence of high reflectivity of metal surface on the detection rate, and can be widely used in the field of metal processing.