石油地球物理勘探中多波联合烃类检测技术应用现状：振幅属性分析、频率属性特征分析、AVO特征差异分析及联合反演

ABSTRACT Multi-attribute analysis of multi-component seismic data can provide abundant information on seismic hydrocarbon reservoirs. It can exploit the different responses of compressional and shear waves to the reservoir, in conjunction with colour blending and fusion technology applied to seismic images, to enable the human eye to better discriminate features in seismic data, thereby improving the accuracy of reservoir characterisation. The integration of multi-component data and sophisticated visualisation techniques helps to reduce the number of possible models of the reservoir. Thus, we designed a reservoir prediction method based on unsupervised learning and colour feature blending. First, a large number of compressional and shear wave seismic attributes were extracted using cluster analysis to conduct unsupervised learning to optimise the attributes. Then, using the different responses of compressional and shear waves to oil and gas, and an understanding of rock physics, three types of composite attribute were constructed to highlight oil and gas anomalies by multi-component seismic attributes. Finally, the three composite attributes were transformed to the colour space by a first-order linear transformation and RGB colour blending. Applying this scheme to reservoir prediction shows that unsupervised learning and colour blending techniques could help the human eye perceive geological anomalies, highlight common hydrocarbon characteristics, reduce differences and decrease interpretation ambiguity. The prediction results are essentially consistent with actual data and can be used to predict favourable exploration areas.

Searching for hydrocarbon reserves in deep subsurface is the main concern of wide community of geophysicists and geoscientists in petroleum industry. Exploration seismology has substantially contributed to finding and developing giant fields worldwide. The technology has evolved from two to three-dimensional method, and later added a fourth dimension for reservoir monitoring. Continuous depletion of many old fields and the increasing world consumption of crude oil pushed to consistently search for techniques that help recover more reserves from old fields and find alternative fields in more complex and deeper formations either on land and in offshore. In such environments, conventional seismic with the compressional (P) wave alone proved to be insufficient. Multicomponent seismology came as a solution to most limitations encountered in P-wave imaging. That is, recording different components of the seismic wave field allowed geophysicists to map complex reservoirs and extract information that could not be extracted previously. The technology demonstrated its value in many fields and gained popularity in basins worldwide. In this review study, we give an overview about multicomponent seismology, its history, data acquisition, processing and interpretation as well as the state-of the-art of its applications. Recent examples from world basins are highlighted. The study concludes that despite the success achieved in many geographical areas such as deep offshore in the Gulf of Mexico, Western Canada Sedimentary Basin (WCSB), North Sea, Offshore Brazil, China and Australia, much work remains for the technology to gain similar acceptance in other areas such as Middle East, East Asia, West Africa and North Africa. However, with the tremendous advances reported in data recording, processing and interpretation, the situation may change.

… of multicomponent seismology and how it can be applied to characterize hydrocarbon … In recent years, multicomponent seismic has been used to detect reservoir fluid directly, the ‘…

Elastic rock properties can be estimated from prestack seismic data using amplitude variation with offset analysis. P‐wave, S‐wave and density ‘reflectivities’, or contrasts, can be …

… Then, sample data are fed into the DNN for training and testing. After optimized network … attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics …

A seismic source excites a rich variety of elastic waves in the Earth, so it seems reasonable to try to use them all to create a more compelling picture of the subsurface. While P-wave imaging has been enormously successful in this regard, there are conditions when it is less so. But, the demands of energy discovery and recovery require an increasingly comprehensive portrayal of reservoir lithologies, stresses, fractures, and fluids. The multicomponent seismic method is a superset of conventional seismic technology and has the potential to answer to some of these demands. Recording horizontal motion, as well as vertical vibrations and pressures, allows further capturing of the full seismic wavefield, and the additional resultant pictures can provide greater comprehension of subsurface properties, fluids, and their changes. We might liken this to a more complete conversation with “loud” waves (P-waves arriving first with high amplitudes) and “shy” waves (S-waves with lower voices and a more complicated message).

Abstract The multi-component seismic exploration technique has drawn great attention in the petroleum industry because it enlarges the S-wave information, reduces the ambiguity in reservoir prediction, and improves the prediction and identification possibility of reservoir fluids. To push forward the application and development of the technique, the 2D and 3D multi-component technical tests have been carried out since 2002 in the Sulige Gasfield, Ordos Basin; the Guang'an Gasfield, Sichuan Basin; the Xushen Gasfield, Songliao Basin; and the Sanhu area, Basin, Qaidam Basin. The imaging of the converted wave is better than that of the P-wave for the gas-bearing structures in the deep igneous body of the Songliao Basin and the Sanhu area, and can define the reservoir boundaries more accurately. In the description of the tight sand gas reservoirs of the Sulige and Guang'an Gasfields, the converted wave imaging improves the accuracy of reservoir prediction and fluid identification, paving the way for the arrangement of wells.

Reservoir hydrocarbon detection is of great interest for reservoir characterization and quality assessment. However, deep carbonate reservoirs exhibit weak seismic response features, making it extremely difficult to extract and utilize reservoir information from seismic data, which leads to significant challenges for seismic-based reservoir detection techniques. The sparsity of the labeled samples often limits the application of supervised machine learning for seismic reservoir detection. This study proposes a multiseismic features constrained unsupervised machine learning approach for carbonate reservoir hydrocarbon detection in areas with few or no wells, which combines the multiple reservoir fluid feature extraction methods seismic-print analysis, high-resolution seismic attenuation gradient estimation, seismic dispersion analysis, and prestack simultaneous inversion, as well as advanced unsupervised machine learning isolation forest anomaly detection algorithm, to effectively extract and utilize the implicit reservoir pore-fluid information in seismic data. This method jointly uses multiple methods to extract multiseismic data features, which can overcome the problem that using a single method to extract seismic data features cannot fully reflect the reservoir pore-fluid information. Using unsupervised machine learning for multisource data feature fusion reservoir hydrocarbon detection can solve the problem that supervised machine learning's requirement for labeled data in deep-buried reservoir detection applications cannot be met. Actual field data application shows that the hydrocarbon detection results were consistent with the actual geologic understanding, which proves that the presented method is feasible and effective. This study provides a valuable insight and reference for reservoir detection in deep carbonate reservoirs with weak seismic responses.

… Detection and mapping of local small hydrocarbons deposits, likely associated with … -traditional methods, one of which is multicomponent seismic survey. This allows obtaining all the …

… reservoirs in the search for hydrocarbons. Multicomponent shear-wave reflection data have … Their relative merits are discussed and their potentials for detecting fractures are compared. …

Current study demonstrates that multicomponent 3D survey is an efficient tool not only to detect gas but also to delineate its migration pathways in shallow sediments. We have determined that thermogenic gas detected in the study area is represented by amplitude and velocity anomalies, including bright spots, gas clouds, gas sags, and phase changes. Evidence of these anomalies attributed to gas presence is provided by well correlation and amplitude variation with offset crossplot analysis and is further supported by the imaging disparity between P- and converted-S-wave data. Current mapping results suggested a predominant vertical gas migration through faults from the unreported petrochemical products in some sulfur caverns. This conclusion is contrary to earlier studies, which suggest a primarily lateral migration from a gas storage cavern 1219 m (4000 ft) to the west.

… data are nonlinear and non‐stationary in nature and have multi‐component signals, … to analyse seismic data is provided for reservoir characterization and hydrocarbon detection. Finally…

A comprehensive drilling of wells has been conducted in the Permian Qixia Formation in the central Sichuan Basin, revealing a significant number of dolomite reservoirs. High- and medium-porosity dolomite reservoirs are the main gas-producing reservoirs in the Qixia Formation. Seismic PP-wave data show a ‘bright spot’ for high-porosity dolomite reservoir formations but weak responses for medium-porosity dolomite reservoir formations, which is attributed to the inability of P waves to distinguish between medium-porosity reservoirs and limestone. However, medium-porosity dolomite and limestone have different S-wave velocities. Therefore, in this study, the identification of different-porosity dolomite reservoirs using multi-component seismic data was investigated. A comprehensive analysis of the elastic waves by forward modeling shows that the PS-wave amplitude is more sensitive to medium-porosity dolomite than the PP-wave amplitude. Therefore, medium-porosity dolomite reservoirs can be predicted using the amplitude attributes of the PS wave, and high-porosity dolomite reservoirs can be characterized using the PP wave. Meanwhile, the elastic parameter λρ (the product of Lame constant λ and density ρ), which is highly correlated with the dolomite content, can be used as an indicator of dolomite formations. Furthermore, compared to the results of PP-wave inversion, the elastic parameters derived from the joint inversion of PP- and PS-waves exhibited a better correspondence with the well-logging results. The comprehensive use of the seismic amplitude responses of PP and PS waves and multi-component seismic joint inversion can effectively predict high- and medium-porosity dolomite reservoirs. The predicted results can support the exploration and development of the Qixia Formation.

… distribution, fracture detection, oil and gas identification, and the … multi-component seismic information. The second is to compare and optimize multi-wave and multi-component seismic …

Multi-component seismic exploration provides more valuable information for the prediction of underground structure, lithology, fluids and fractures. Most studies on lithology estimation and fluid description using PP- and PS-wave seismic data are mainly focusing on relatively shallow targets with high porosity. Focusing on the deep carbonate (dolomite) reservoirs with low porosity (generally less than 5%), we first analyze characteristics of seismic response difference between PP-wave and PS-wave, and then we combine logging data and seismic forward modeling to implement the identification of fluids using amplitude difference between PP and PS waves. Datasets are acquired over grain beach dolomite reservoirs in the Cambrian Longwangmiao (LWM) formation, the central Sichuan Basin, Western China. The reservoirs are filled with different types of fluids (e.g. gas, water, bitumen, etc.). Based on comparisons between seismic responses of PP-wave and PS-wave, we observe that at the location of the gas-bearing layer, PP-wave amplitude exhibits strong peaks and PS-wave amplitude shows relatively weak peaks, which is different from that obtained for the case of the water-saturated layer. However, at the location of bitumen-filled reservoirs, both PP- and PS-wave amplitudes exhibit strong peaks, and the reflection amplitude of PP-wave is stronger than that of PS-wave. Therefore, the amplitude difference of PP and PS waves provides a valuable information and feasibility for fluid identification. We verify that the maximum peak amplitude attribute of PS-wave may better characterize the distribution of porous dolomite reservoirs than that of PP-wave, and using the attribute of maximum peak amplitude difference between PP and PS waves, we may distinguish gas-bearing and water-saturated layers. Comparing with actual drilling results, we conclude that the proposed PP/PS interpretation technique is feasible for the fluid identification in deep carbonate reservoirs.

… For each of the polarization attributes p, a, r, and 1, we define average attributes by weighting with the geometric mean of instantaneous amplitudes in the same manner as for <li in …

… AVO attributes. In addition, an improved method for extracting the amplitude attributes of multicomponent … the reliability of reservoir prediction with multicomponent seismic technology. …

… PP-wave and PS-wave attributes from multi-wave and multi-component exploration are able … We extract 17 interformational amplitude attributes from PP- and PS-wave data of the same …

Acquiring four-component seismic data with wide-azimuth geometry provides an opportunity to build a very complete seismic picture for reservoir description. The recording of the full vector wavefield allows creation of both PS-wave data as well as P-wave images which contain different but complementary information. It also provides full-azimuth illumination of the subsurface. Azimuthal images improve definition of structural features, such as faults, that may only be illuminated within certain preferential shot-receiver azimuths. Differences in azimuthal images can also be very sensitive fault indicators in the case of small vertical displacements. Furthermore, and of crucial importance at the reservoir scale, wide-azimuth P-wave and PS-wave data lend themselves to the evaluation of azimuthal anisotropy. These attributes provide valuable spatial constraints in the characterization of heterogeneously distributed subseismic scale fractures.

Abstract Nier Ribeiro illustrates how inversion of multi-component seismic data can significantly improve turbidite sandstone reservoir characterization in the Campos Basin offshore Brazil.

… attributes, amplitude accentuating attributes, amplitude versus offset (AVO) attributes, seismic inversion attributes … , and the selective integration of these attributes leads to an efficient …

… , PS seismic sections may exhibit geologically simcant changes in amplitude or character of … Through the analysis of multicomponent seismic data, important rock properties such as …

… attributes help reliable estimates of thin layer properties, usually missing in conventional amplitude attribute … Complex trace analysis for computing instantaneous amplitude attributes is …

Local seismic attributes measure seismic signal characteristics not instantaneously at each signal point and not globally across a data window but locally in the neighborhood of each point. I define local attributes with the help of regularized inversion and demonstrate their usefulness for measuring local frequencies of seismic signals, local similarity between different datasets, and local focusing of a seismic image. A multicomponent image registration example from a ninecomponent land survey illustrates practical applications of local attributes for measuring frequency and phase differences between registered images.

… multifrequency attributes, which can describe local time-frequency features of seismic data. Then, we propose a sensitive attribute analysis (SAA) method to reduce frequency attribute …

… multicomponent seismic data, we combine the apparent velocity, and energy and frequency attributes and propose an improve vector filter for suppressing vector seismic trace surface …

Over the years, amplitude variation with-offset (AVO) analysis has been used successfully to predict reservoir properties and fluid contents, in some cases allowing the spatial location of gas-water and gas-oil contacts. In this paper, we show that a 3-D AVO technique also can be used to characterize fractured reservoirs, allowing spatial location of crack density variations. The Cedar Hill Field in the San Juan Basin, New Mexico, produces methane from the fractured coalbeds of the Fruitland Formation. The presence of fracturing is critical to methane production because of the absence of matrix permeability in the coals. To help characterize this coalbed reservoir, a 3-D, multicomponent seismic survey was acquired in this field. In this study, prestack P-wave amplitude data from the multicomponent data set are used to delineate zones of large Poisson's ratio contrasts (or high crack densities) in the coalbed methane reservoir, while source-receiver azimuth sorting is used to detect preferential directions of azimuthal anisotropy caused by the fracturing system of coal. Two modeling techniques (using ray tracing and reflectivity methods) predict the effects of fractured coal-seam zones on angle-dependent P-wave reflectivity. Synthetic common-midpoint (CMP) gathers are generated for a horizontally layered earth model that uses elastic parameters derived from sonic and density log measurements. Fracture density variations in coalbeds are simulated by anisotropic modeling. The large acoustic impedance contrasts associated with the sandstone-coal interfaces dominate the P-wave reflectivity response. They far outweigh the effects of contrasts in anisotropic parameters for the computed models. Seismic AVO analysis of nine macrobins obtained from the 3-D volume confirms model predictions. Areas with large AVO intercepts indicate low-velocity coals, possibly related to zones of stress relief. Areas with large AVO gradients identify coal zones of large Poisson's ratio contrasts and therefore high fracture densities in the coalbed methane reservoir. The 3-D AVO product and Poisson's variation maps combine these responses, producing a picture of the reservoir that includes its degree of fracturing and its possible stress condition. Source-receiver azimuth sorting is used to detect preferential directions of azimuthal anisotropy caused by the fracturing system of coal.

… promise of multicomponent seismic data goes much further beyond these pathological cases. … of multicomponent AVO-Inversion that will unravel more information from multicomponent …

Shear-wave amplitude variation with offset (AVO) analysis can be used to map changes in density, shear-wave velocity, and fracturing at reservoir scale by allowing the influence of each factor to be separately extracted from the observed seismic response. Weighted least-squares inversion of the anisotropic reflection coefficients was implemented to find the shear-wave splitting coefficient and velocity-contrast parameters. A time-lapse nine-component, 4-D seismic survey acquired over Vacuum field in Lea County, New Mexico, was used to test our methodology of shear-wave AVO analysis and to compare the results with well production and azimuthal P-wave AVO analysis. Weighted least-squares shear-wave AVO stacks of the splitting parameter were found to be excellent predictors of well fluid-production performance, implying a strong link between seismically inferred fracturing and reservoir-scale permeability of the San Andres dolomites at Vacuum field. Analysis of the shear-wave velocity contrast indicated the presence of a second set of open fractures to the south of a carbon dioxide injector well where a 4-D anomaly associated with injection had been observed.

… Multi-component AVO response of thin beds based on reflectance spectrum theory* Abstract: Seismic AVO analysis … pore fluid is more useful for seismic exploration. However, traditional …

… Since both the seismic source and the geology create a large variety of wave types, … multicomponent seismic recording and analysis techniques to disentangle and use all of the seismic …

Abstract The Zoeppritz equation describes the relationship between reflection and transmission coefficients, incidence angles and elastic properties. Currently, most of the prestack joint AVO or AVA (amplitude variation with offset or angle) inversion methods are based on various approximations of the exact Zoeppritz equation. However, because these approximations have certain assumptions and limitations, such as limited incident angles or weak contrast of elastic parameters on either side of the interface, the conventional linear prestack joint AVO inversion based on different approximations isn't suitable, which reduces the inversion accuracy theoretically. In this paper, we develop a multi-objective prestack joint AVO inversion method using both PP and PS seismic data based on the exact Zoeppritz equation. To solve the nonlinear multi-objective inversion problems, we use a developed fast nondominated sorting genetic algorithm (NSGA II) to estimate three elastic parameters, including P-wave velocity, S-wave velocity and density. Compared with the conventional weighted stacking optimization means of objective functions, this approach is robust because it can simultaneously cope with more than one objective function without introducing weight coefficients. Model tests illustrate that the nonlinear multi-objective prestack joint AVO inversion method shows great potential to estimate three elastic parameters with the exact Zoeppritz equation. We also analyze the influence of the initial model and the search window on the inversion results. Additionally, the seismic data with random noise examples reveal the stability and anti-noise interference ability of our joint AVO inversion method.

Theoretically and experimentally, the shear-wave velocity of a porous rock has been shown to be less sensitive to fluid saturants than the compressional wave velocity. Thus, observation of the ratio of the seismic velocities for waves which traverse a changing or laterally varying zone of undersaturation or gas saturation could produce an observable anomaly which is independent of the regional variation in compressional wave velocity.One source of shear-wave data in reflection seismic prospecting is mode conversion of P waves to shear waves in marine areas of high water bottom P-wave velocity. A relatively simple interpretative technique, based on amplitude variation as a function of the angle of incidence, is a possible discriminant between shear and multiple compressional arrivals, and data for a real case are shown. A normal moveout velocity analysis, carefully coupled with this offset discriminant, leads to the construction of a shear-wave reflection section which can then be correlated with the usual compressional wave section.Once such a section has been constructed, the variation in the ratio of the seismic velocities can be mapped, and potentially anomalous subsurface regions observed.

Multicomponent seismic recording (measurement with vertical- and horizontal-component geophones and possibly a hydrophone or microphone) captures the seismic wavefield more completely than conventional single-element techniques. In the last several years, multicomponent surveying has developed rapidly, allowing creation of converted-wave or P-S images. These make use of downgoing P-waves that convert on reflection at their deepest point of penetration to upcoming S-waves. Survey design for acquiring P-S data is similar to that for P-waves, but must take into account subsurface VP/VS values and the asymmetric P-S ray path. P-S surveys use conventional sources, but require several times more recording channels per receiving location. Some special processes for P-S analysis include anisotropic rotations, S-wave receiver statics, asymmetric and anisotropic binning, nonhyperbolic velocity analysis and NMO correction, P-S to P-P time transformation, P-S dip moveout, prestack migration with two velocities and wavefields, and stacking velocity and reflectivity inversion for S-wave velocities. Current P-S sections are approaching (and in some cases exceeding) the quality of conventional P-P seismic data. Interpretation of P-S sections uses full elastic ray tracing, synthetic seismograms, correlation with P-wave sections, and depth migration. Development of the P-S method has taken about 20 years, but has now become commercially viable.

The combined use of P- and S-wave seismic reflection data is appealing for providing insights into active petroleum systems because P-waves are sensitive to fluids and S-waves are not. The method presented herein relies on the simultaneous acquisition of P- and S-wave data using a vibratory source operated in the inline horizontal mode. The combined analysis of P- and S-wave reflections is tested on two potential hydrocarbon seeps located in a prospective area of the St. Lawrence Lowlands in Eastern Canada. For both sites, P-wave data indicate local changes in the reflection amplitude and slow velocities, whereas S-wave data present an anomalous amplitude at one site. Differences between P- and S-wave reflection morphology and amplitude and the abrupt decrease in P-velocity are indirect lines of evidence for hydrocarbon migration toward the surface through unconsolidated sediments. Surface-gas analysis made on samples taken at one potential seeping site reveals the occurrence of thermogenic gas that presumably vents from the underlying fractured Utica Shale forming the top of the bedrock. The 3C shear data suggest that fluid migration locally disturbs the elastic properties of the matrix. The comparative analysis of P- and S-wave data along with 3C recordings makes this method not only attractive for the remote detection of shallow hydrocarbons but also for the exploration of how fluid migration impacts unconsolidated geologic media.

Converted seismic waves (specifically, downgoing P-waves that convert on reflection to upcoming S-waves) are increasingly being used to explore for subsurface targets. Rapid advancements in both land and marine multicomponent acquisition and processing techniques have led to numerous applications for P-S surveys. Uses that have arisen include structural imaging (e.g., “seeing” through gas-bearing sediments, improved fault definition, enhanced near-surface resolution), lithologic estimation (e.g., sand versus shale content, porosity), anisotropy analysis (e.g., fracture density and orientation), subsurface fluid description, and reservoir monitoring. Further applications of P-S data and analysis of other more complicated converted modes are developing.

Seismic signals are usually nonlinear and nonstationary. The Fourier transform (FT) based on stationary signal processing theory cannot depict the frequency components at any moment. However, the time–frequency analysis (TFA) methods have the capability of describing the partial features of signal both in time and frequency domains. S transform (ST), as a common TFA method, has great time–frequency (TF) combination characteristics, but the changing trend of the window function is fixed and the TF resolution cannot be adjusted. In addition, for seismic signals, the peaks of the frequency distribution in the TF spectrum bias the actual Fourier spectrum, which will affect the accuracy of data analysis. Therefore, we propose a new TFA method called the deconvolutive improved S transform (DIST). The DIST introduces one parameter to the window function other than multiple parameters to improve the flexibility in the application process. The normalization factor is also removed from the window function to avoid the frequency bias. Moreover, the deconvolution in DIST can further improve the accuracy of TF representation. The comparison of the TFA results of synthetic seismic signals shows that the DIST has better TF resolution and energy aggregation than other TFA methods in this article. By adding different degrees of noise to synthetic seismic signals, we conclude that DIST has better noise robustness. Finally, we apply DIST to different field data for hydrocarbon detection, and the results are basically consistent with the drilling data.

Instantaneous spectral analysis (ISA) is a continuous time-frequency analysis technique that provides a frequency spectrum for each time sample of a seismic trace. ISA achieves both excellent time and frequency localization utilizing wavelet transforms to avoid windowing problems that complicate conventional Fourier analysis. Applications of the method include enhanced resolution, improved visualization of stratigraphic features, thickness estimation for thin beds, noise suppression, improved spectral balancing, and direct hydrocarbon indication. We have seen four distinct ways in which ISA can help in the detection of hydrocarbons: (1) anomalously high attenuation in thick or very unconsolidated gas reservoirs, (2) low-frequency shadows in reservoirs where the thickness is not sufficient to result in significant attenuation, (3) preferential illumination at the “tuning” frequency which can be different for gas or brine-saturated rocks, and (4) frequency-dependent AVO. In this paper, we describe the ISA technique, compare it to other spectral decomposition methods, and show some examples of the use of ISA to detect low-frequency shadows beneath gas reservoirs.

… How do shear waves cause, or influence, compressive wave behavior both in the … of compressive seismic waves? Are we occasionally measuring converted shear waves? To this point …

… in hydrocarbon exploration has been its ability to detect the signature of relatively resistive … In contrast, if we combined seismic reflection data and MT in a joint inversion and associated …

Accurately estimating reservoir parameters from geophysical data is vitally important in hydrocarbon exploration and production. We have developed a new joint-inversion algorithm to estimate reservoir parameters directly, using both seismic amplitude variation with angle of incidence (AVA) data and marine controlled-source electromagnetic (CSEM) data. Reservoir parameters are linked to geophysical parameters through a rock-properties model. Errors in the parameters of the rock-properties model introduce errors of comparable size in the reservoir-parameter estimates produced by joint inversion. Tests of joint inversion on synthetic 1D models demonstrate improved fluid saturation and porosity estimates for joint AVA-CSEM data inversion (compared with estimates from AVA or CSEM inversion alone). A comparison of inversions of AVA data, CSEM data, and joint AVA-CSEM data over the North Sea Troll field, at a location for which we have well control, shows that the joint inversion produces estimates of gas saturation, oil saturation, and porosity that are closest (as measured by the rms difference, the L1 norm of the difference, and net values over the interval) to the logged values. However, CSEM-only inversion provides the closest estimates of water saturation.

Geophysical joint inversion requires the setting of a few parameters for optimum performance of the process. However, there are yet no known detailed procedures for selecting the various parameters for performing the joint inversion. Previous works on the joint inversion of electromagnetic (EM) and seismic data have reported parameter applications for data sets acquired from the same dimensional geometry (either in two dimensions or three dimensions) and few on variant geometry. But none has discussed the parameter selections for the joint inversion of methods from variant geometry (for example, a 2D seismic travel and pseudo-2D frequency-domain EM data). With the advantage of affordable computational cost and the sufficient approximation of a 1D EM model in a horizontally layered sedimentary environment, we are able to set optimum joint inversion parameters to perform structurally constrained joint 2D seismic traveltime and pseudo-2D EM data for hydrocarbon exploration. From the synthetic experiments, even in the presence of noise, we are able to prescribe the rules for optimum parameter setting for the joint inversion, including the choice of initial model and the cross-gradient weighting. We apply these rules on field data to reconstruct a more reliable subsurface velocity model than the one obtained by the traveltime inversions alone. We expect that this approach will be useful for performing joint inversion of the seismic traveltime and frequency-domain EM data for the production of hydrocarbon.

Studies of frequency dependence of seismic data anomalies on partially gas-saturated reservoir have been performed for many years. Essentially, the frequency-dependent seismic signature represents a potential and largely untapped source of information for the detections of subsurface target properties. Through analyzing the anomalous feathers of amplitude variations with the angle of incidence and frequency (AVAF), both theoretically and algorithmically, it is possible to discriminate hydrocarbon from variations in other reservoir properties. For a layered structure model, however, it can be challenging to employ the conventional Zoeppritz equation-based method that may not accurately describe complex reflections considering the effects of both the layered structure of a reservoir and the attenuative and dispersive property of rocks. We introduce a novel hydrocarbon detection approach based on Bayesian inversion of frequency- and angle-dependent reflection signatures from a tight gas sandstone reservoir having strong attenuation and velocity dispersion. The proposed inversion scheme employs the propagator matrix method as a description of seismic responses for the stratified model and spectral decomposition technique to obtain multifrequency amplitude information. The synthetic test and real application show the proposed inversion approach has the potential to be useful in detections of hydrocarbon accumulation.

The West Delta Deep Marine concession offshore Egypt’s Simian field was exposed to simultaneous pre-stack inversion to test its quantitative interpretation potential. In heterogeneous submarine channel reservoirs, characterising reservoir lithology and fluid distribution and isolating gas sand, brine sand, and background shale are the key Simian field challenges. Due to poor water sand mapping, numerous Simian field wells had surprising early water production rates. Therefore, this study may investigate if pre-stack seismic data and sophisticated inversion techniques can precisely pinpoint Simian field lithology, facies changes, and fluid distribution. Concurrent pre-stack inversion estimates rock parameters, including acoustic or P-wave impedance (Zp), shear impedance or S-wave impedance (Zs), and density (ρ), which are strongly related to lithology. Before the inversion procedure, two wells were analysed in a rock physics investigation, and three angle gathers (0–15°, 15–30°, and 30–45°) were pre-stacked inverted for Zp, Zs, P-wave velocity (Vp), S-wave velocity (Vs), Vp/Vs ratio, and ρ. Lambda-Mu-Rho (LMR) analysis involves obtaining Lamé parameters by inverting Zp and Zs simultaneously, resulting in Lamda-Rho (Incompressibility) (λρ) and Mu-Rho (Rigidity) (µρ) volumes. The training process involved pre-stack inversion analysis employing angle stack seismic data and well log data, cross-validation, and Vp, Vs, and Vp/Vs volumes. Vp/Vs and Zp volumes predicted Sw values that matched well gas-water contact. The study used a test well (Simian-Di) and validated the result at a blind well location (Simian-Dj) to evaluate a pre-stack inversion approach. It found accurate predictions, suggesting better output and economic efficiency.

An alternating joint inversion method for controlled-source electromagnetic (CSEM) and seismic data is developed. The structural constraint is used for correlating and constraining the electromagnetic (EM) resistivity and seismic velocity parameters during the inversion. The structural coupling used is the joint total variation (JTV) constraint, which is incorporated into the objective function of the individual EM or seismic inversion to enforce the structural similarity between the resistivity and velocity. In this paper, the conventional cross-gradient constraint is not preferred as it can only be used for enforcing structural similarity for 2-D or 3-D case since it is always zero for 1-D joint inversion. The JTV constraint can be applied for 1-D joint inversion, as well as for the 2-D/3-D case, which is of a broader interest. The improved Gauss–Newton (GN) is used for minimizing the objective function and for reconstructing the subsurface resistivity and velocity. The alternating joint inversion algorithm is applied for integrating land CSEM data with cross-well seismic data for subsurface reservoir evaluation and water–oil identification. Numerical examples show that the developed joint inversion can improve the inversion results significantly over those from the separate EM or seismic inversion.

A Bayesian approach is proposed to estimate litho-fluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Since computing this probability for all possible facies configurations may be impractical, a Markov Chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geological scenario and a well from a selected marine oil field. Two other wells from the same reservoir were used to gather prior distributions. Data from the well, calibration of the rock physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward modeling adaptations on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical datasets are then inverted for litho-fluid facies models under different prior inputs. Two challenging geoelectric scenarios were also tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating both geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also show potential for quantitative reservoir interpretations.

… The CSEM measurement has the ability to detect hydrocarbon and gas reservoir since they have much higher resistivity than the surrounding seabed. Consequently, the number of the …

To improve the accuracy of hydrocarbon detection, seismic amplitude variation with offset (AVO), seismic amplitude variation with frequency (AVF), and direct hydrocarbon indicators (DHI) had been adopted in conventional methods. However, seismic amplitude responses are not straightforward with fluid properties but greatly affected by lithology and porosity. In this paper we present an integrated hydrocarbon detection method based on Full Frequency Seismic Inversion and pre-stack seismic inversion (Vp/Vs). The integrated hydrocarbon detection method mainly contains 5 steps. 1) Seismic data conditioning. Selecting sensitive frequencies to optimize the resolution of seismic data. 2) Rock physics analysis based on the rock type analysis. 3) Utilizing a new Full Frequency Seismic Inversion (FFI) method to improve the resolution and accuracy of seismic inversion. 4) Hydrocarbon detection based on the pre-stack inversion (Vp/Vs) based in the rock physical analysis. 5) Integration pre-stack inversion (Vp/Vs), well log interpretation, lithology and porosity from FFI for hydrocarbon detection QC and optimization to improve the accuracy of hydrocarbon detection results. This method was successfully applied in the K oil field. Comparative seismic data conditioning revealed that the sensitive frequency, enhancing seismic resolution. The Full Frequency Seismic Inversion (FFI) process involved 4 key components: a low-frequency model (<7 Hz) based on well logging data and controlled by seismic facies trends, conventional deterministic inversion (7-57 Hz) based on high-quality seismic data, mid- to high-frequency inversion (7-120 Hz) using seismic motion inversion, and high-frequency inversion (7-270 Hz) using geostatistical methods driven by well logs and seismic facies trends. The advantage of this method lies in its ability to integrate high-frequency well log information with seismic facies trends in areas without wells, obviously improving resolution and accuracy. The FFI method better predicts the lithology and porosity distribution. Pre-stack AVO and Vp/Vs inversions further enhanced hydrocarbon distribution predictions based on FFI results. Significant hydrocarbon response areas were identified by blind wells and new wells validation showing a 92% correlation between wells interpretations and hydrocarbon detection results. Well 07, drilled in the most promising area, achieved a production rate of 4,700 bbl/d, the highest oil production in the K oil field. The integrated hydrocarbon detection method has 3 main advantages. ① Seismic data conditioning can improve the quality of seismic data. ②Full Frequency Seismic Inversion (FFI) can improve the resolution and accuracy of seismic inversion, deepen geological understanding, and reduce the influence of lithology and porosity. ③The accuracy of hydrocarbon detection can be improved through multidisciplinary integrated analysis and QC. This method is applicable to clastic reservoirs and carbonate reservoirs.

… Joint inversion partially counters this inherent problem by narrowing the solution … as hydrocarbon-filled reservoirs often exhibit high resistivity contrast, allowing better reservoir detection …

Spectral decomposition based on sparse constrained inversion, as proposed in recent years, is a high-resolution time-frequency domain analysis method. However, the traditional inverse spectral decomposition (ISD) algorithm is based on an inverse wavelet transform (IWT) whose mother wavelet is a Ricker wavelet function instead of an adaptive wavelet, which causes poor continuity and instability of the ISD results. In this paper, we have extended ISD into other linear transforms and have developed an ISD based on inverse S transform (ISD-IST), which has higher continuity and stability due to the more adjustable and adaptable mother wavelet of the S transform. Comparison of conventional methods and the ISD-IST shows that the ISD (ISD-IWT and ISD-IST) methods have higher resolution and accuracy and can provide more reliable time-frequency phase spectra, and it further verifies the greater continuity and stability of ISD-IST over ISD-IWT. Frequency-dependent AVO (FAVO) inversion is regarded as a potential hydrocarbon identification technique (Wilson et al 2009 SEG Technical Program Expanded Abstracts pp 341–5). However, it is frequency-decomposition based, which means that inversion results can be greatly affected by the effect of time-frequency analysis (Wu et al 2010 SEG Technical Program Expanded Abstracts pp 425–9; Wu et al 2014 Geophys. Prospect. 62 1224–37). Combining the FAVO inversion and high-resolution time-frequency decomposition ISD allows better dispersion attributes to be obtained. Results from application in Southwest China demonstrate that the high frequency-resolution of ISD is helpful in the extraction of gas-induced frequency anomalies from post-stack data, and its high time-resolution helps to obtain high time-resolution gas-induced dispersion results from FAVO inversion. The results of FAVO-ISD not only agree better with well information, but also supply more credible boundary information for the reservoirs. ISD-IST can provide more continuous results and smoother boundary information for the gas reservoir than ISD-IWT.

A complex structural geology generally leads to significant consequences for hydrocarbon reservoir exploration. Despite many existing wells in the Kadanwari field, Middle Indus Basin (MIB), Pakistan, the depositional environment of the early Cretaceous stratigraphic sequence is still poorly understood, and this has implications for regional geology as well as economic significance. To improve our understanding of the depositional environment of complex heterogeneous reservoirs and their associated 3D stratigraphic architecture, the spatial distribution of facies and properties, and the hydrocarbon prospects, a new methodology of three-dimensional structural modeling (3D SM) and joint geophysical characterization (JGC) is introduced in this research using 3D seismic and well logs data. 3D SM reveals that the field in question experienced multiple stages of complex deformation dominated by an NW to SW normal fault system, high relief horsts, and half-graben and graben structures. Moreover, 3D SM and fault system models (FSMs) show that the middle part of the sequence underwent greater deformation compared to the areas surrounding the major faults, with predominant one oriented S30°–45° E and N25°–35° W; with the azimuth at 148°–170° and 318°–345°; and with the minimum (28°), mean (62°), and maximum (90°) dip angles. The applied variance edge attribute better portrays the inconsistencies in the seismic data associated with faulting, validating seismic interpretation. The high amplitude and loss of frequency anomalies of the sweetness and root mean square (RMS) attributes indicate gas-saturated sand. In contrast, the relatively low-amplitude and high-frequency anomalies indicate sandy shale, shale, and pro-delta facies. The petrophysical modeling results show that the E sand interval exhibits high effective porosity (∅eff) and hydrocarbon saturation (Shc) compared to the G sand interval. The average petrophysical properties we identified, such as volume of shale (Vshale), average porosity (∅avg), ∅eff, water saturation (SW), and the Shc of the E sand interval, were 30.5%, 17.4%, 12.2%, 33.2% and, 70.01%, respectively. The findings of this study can help better understand the reservoir’s structural and stratigraphic characteristics, the spatial distribution of associated facies, and petrophysical properties for reliable reservoir characterization.

In an anisotropic reservoir, it is crucial to determine the anisotropic media fluid factor that corresponds to the reservoir’s anisotropic properties. To address the lack of research on accurate hydrocarbon identification and fluid factors in anisotropic reservoirs, relevant research work has been carried out. First, the relationship between the anisotropic parameters and the physical parameters of the fluid-bearing reservoir is discussed based on petrophysical mechanisms. Subsequently, a new anisotropic media fluid factor is developed that exhibits higher fluid sensitivity. According to some studies, the dispersion and attenuation of seismic waves will significantly affect the reservoir’s amplitude variation with offset and azimuth (AVAZ) response. Consequently, an anisotropic frequency-dependent reflection coefficient is constructed using the frequency-dependent anisotropic media fluid factor to better utilize the frequency information in seismic data. The prestack seismic frequency-dependent inversion method is employed to extract the abundant azimuthal anisotropy information and frequency information from the wide-azimuth seismic data. Synthetic and field data examples provide evidence of the high reliability and stability of the prestack seismic frequency-dependent anisotropic inversion method. The development of prestack seismic frequency-dependent inversion, based on AVAZ response and frequency response, offers a novel theoretical method for hydrocarbon identification in anisotropic reservoirs.

This study integrates seismic inversion and rock physics techniques to evaluate the hydrocarbon potential of an offshore field in the Niger Delta. Five wells revealed three reservoir sands with favourable reservoir properties, including gross thickness (49.2–81.4 m), porosity (0.18–0.2), permeability (565–1481 mD), and water saturation (0.16–0.54). A robust wavelet extraction process was implemented to guide seismic inversion, and a well log-centric approach was employed to validate the resulting acoustic impedance data. Rock physics analysis established correlations between acoustic impedance (Zp), porosity, fluid content, and lithology, enabling the identification of hydrocarbon-filled sands, brine-saturated sands, and shales. These relationships enabled the discrimination of hydrocarbon-filled sands [5000–8000 (m/s)(g/cc)], from brine-saturated sands [5600–8400 (m/s)(g/cc)], and shales [5000–9000 (m/s)(g/cc)] within the inverted seismic data. The inverted acoustic impedance section showed a general increase with depth, reflecting the typical compaction effects in the Niger Delta. Analysis of the impedance distribution across horizon time slices revealed prospective zones with low impedance values [below 6300 (m/s)(g/cc)], particularly in horizons 1 and 2. These newly identified zones exhibit the strongest potential for hydrocarbon accumulation and warrant further investigation. This study demonstrates the effectiveness of using well log and rock physics constrained seismic inversion for hydrocarbon exploration in an offshore field in the Niger Delta.

The paper presents a short overview about the application of joint inversion in geophysics. It gives also an alternative explanation for the term of “different data sets” and discusses what types of inversion procedures can be considered as joint inversion. Nowadays there are no standard standpoints using the appellation joint inversion. What is joint inversion? Based on the information matrix an answer could be given for this question what could be regarded as various types of data sets that are inverted simultaneously. We would like to expand the explanation—that is professed by many researchers—of the method that regards only the simultaneous inversion of data sets based on different physical parameters as joint inversion.

N-12 AVO AND ELASTIC IMPEDANCE S. MALLICK Summary: Traditional AVO analysis involves computation of the AVO intercept and gradient from a linear fit of P-wave reflection amplitude to the sine square of the angle-of-incidence. This linear fit is based on the approximate P-wave reflection coefficient formulation in intercept-gradient form given by Bortfeld (1961) and Shuey (1985) among others. Under the assumption of a background P-S velocity ratio of two the AVO intercept and gradient values can also be combined to obtain additional AVO attributes such as pseudo S-wave data and Poisson’s ratio contrast. AVO intercept and pseudo S-wave data are

… , AVO inversion cannot provide the expected answer. This problem is avoided by elastic impedance, … This method of obtaining elastic properties is called AVO inversion. Alternatively, in …

… as the first inversion for reflectivity in AVO inversion (deconvolution) and the first inversion for EI in EI inversion (elastic impedance inversion). Wavelet stripping in AVO inversion can be …

… In the derivation of Connolly’s elastic impedance, we assume that the ratio between the squares of S- and P-wave velocities, K = β2/α2, is constant. For the reflection impedance, the …

The usefulness of seismic AVO attributes increased dramatically when elastic inversion of partial stacks was introduced. Utilizing the mathematical concept of elastic impedance the technology from the inversion of full stack data for acoustic impedance could be transferred to partial stacks. Each partial stack was inverted for elastic impedance and the elastic impedance results were combined to estimates of physical properties like acoustic impedance, shear impedance and Vp/Vs. This type of AVO inversion has, however, some serious drawbacks that recent developments in simultaneous inversion have overcome. This new simultaneous inversion method is based on global optimization and includes features for working with partial offset stacks and calculation of the high frequency variation of the angle of incidence as part of the inversion. The method is implemented such that the same numerical kernel is used for inversion in different physical domains and of different data types including AVO surface seismic, PS data and time-lapse data. Applying simultaneous seismic inversion it is possible in general to estimate shear impedance, Vp/Vs or Poisson's ratio with the same resolution as the acoustic impedance even though these properties are mainly related to the far stack seismic data with lower resolution. Furthermore very reliable density estimates can be derived from the seismic data, which have proven very useful in prediction of certain lithologies and saturations. Elastic inversion The introduction of the elastic impedance concept in the late 1990's [Connolly, 1999] was a significant improvement in theuse of seismic AVO attributes and increased the accuracy of lithology, fluid and porosity prediction in oil exploration and reservoir characterization. In the implementation of elastic inversion, however, severe assumptions were made, such as a constant average Poisson's ratio. Elastic impedance is furthermore an angle dependent quantity so often several elastic impedances from several partial stacks are combined into estimates of angle independent quantities such as acoustic impedance, shear impedance, VP/VS and gradient impedance. The calculated angle independent quantities, however, give synthetic seismic for each partial stack that has a poorer match (greater misfit) to the seismic data compared to the synthetic seismic direct from the elastic inversions, i.e. the information from the seismic data has not been fully utilized in the calculation of the angle independent quantities, a fact almost never displayed or quantified although it is a very important issue. Another issue is the frequency content of the partial stacks that go into the elastic inversions, where the far offset data will contain lower frequencies due to attenuation and NMO stretch. The problem arises when the elastic impedances are combined into angle independent quantities. Combining data with different frequency content gives noise on the estimates of the angle independent quantities. Therefore frequency balancing must, in many cases, be performed on the seismic data, limiting the resolution on the results. Simultaneous inversion In simultaneous inversion all the seismic data are simultaneously inverted for angle independent quantities [Ma, 2002]. See Figure 1 for a drawing of the data flow in elastic and simultaneous inversion.

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of P- and S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

Elastic impedance inversion is the latest development in the field of hydrocarbon exploration and production. The present research focuses on the improvement of the use of elastic impedance inversion, easing exploration of hydrocarbons. The seismic velocities change with variation in geological constraints. Constant K, which is S-wave to P-wave ratio of the nth layer and n+1 layer across the interface, it must be changed accordingly. This research focuses on testing the effects of K as a constant in the elastic impedance equation. As using the same value of K for all types of formations can give rise to severe errors in the interpretation of data. The importance of the value of K for particular Amplitude Variation with Offset AVO type (I-IV) is studied using different Elastic Impedance Equations. The Reflection Coefficient (RC) curves for each AVO class are generated using Zoeppritz approximation and Elastic Impedance equations. The comparison of RC curves shows significant variations at far offsets in each AVO type using the Constant value of K. When K Calculated is used, AVO type I and Type II shows a good match at near, mid and far offsets. Type III does not change due to the changing value of K. Type IV gives good agreement at near and intermediate offsets. This variation in curves, with the change in the value of K, indicates that it is a significant factor of interpretation using elastic impedance. The application of findings on well logs has given a satisfactory confirmation of the present results. This research can be helpful to resolve severe errors in the interpretation due to the constant value of K.

… geophysics there is no direct interest in acoustic and elastic impedances (IP and IS), or … The elastic properties that underlie the velocities and impedances, the compressibility (bulk) …

Elastic inversion provides an estimate of elastic parameters by the use of Amplitude Variations with Angle data. This information gives the geoscientist additional parameters for the interpretation of lithology, porosity and fluid type. By applying a stratigraphic inversion method to the angle volumes the seismic-wavelet effect is minimized thus reducing the likelihood of wavelet variations within the gathers causing false AVAs. Additionally inversion unravels both the amplitude shape and strength into an interval attribute as opposed to reflectivity that is an interface attribute. This allows a volumetric interpretation of the data producing more accurate understanding of the reservoir volume. Introduction Seismic attributes play a critical role in the efforts to reduce risk associate with reservoir prediction and description. Post stack inversion has proven to be a significant tool that allows geologic calibration and a volumetric understanding of the impedance data. This method allows the interpreter to go beyond contact event interpretation and begin understands the amplitude strength and wavelet shape and its relationship to the vertical and lateral impedance profiles. This begins the understanding of the spatial geologic parameters. Acoustic impedance alone may not always be enough to describe the geologic parameters in complex environments. Acoustic impedance is the result of the Pwave velocity and density. The Pwave velocity is a function of the mineral constituents, the manner in which they are arranged and the pore volume fluid. In cases where two of these unknowns are constant the third parameter may be interpreted. However in cases where these are not constant ambiguous results will occur. When attempting to relate geologic parameters such aslithology, porosity, and fluid type in complex environments additional parameters related to the rock physics must be utilized. To provide these attributes elastic impedance inversion is used. When these additional elastic parameters are used with acoustic impedance they offer a strong tool for increasing the quality of the interpretation of lithology, porosity, and fluid type. Fig.1 shows the crossplot relationships of common lithologies and fluid types. Fig. 1 Crossplot of P-wave velocity and Vp/Vs for various lithologies and fluids.(Available in full paper) Method Overview A common practice of the AVO analysis is to obtain the intercept and gradient from prestack data. These correspond to A and B terms in Shueys equation. R(?)= A + Bsin2 ? + C sin2?tan2? After these two parameters are calculated they may be transformed into parameters such as P reflectivity, S reflectivity, and Poisson's reflectivity. Although valuable attributes to assist in the interpretation of fluid type this method does not address the variations in wavelet and NMO stretching as a function of offset. In a previous paper by Cambois1 it was shown that variations in the wavelets may produce a false avo phenomenon referred to as "intercept leakage". To address the wavelet variations Connolly2 developed a method called "elastic impedance". This method involves inverting the angle volumes for the angle dependant elastic impedance. These angle dependant elastic impedances can then be converted to P and S impedance.

Amplitude variation with offset techniques are used by exploration, development, and production teams to assist hydrocarbon identification in clastic depositional settings. While exploration groups tend to use AVO attributes for detection and risk quantification, exploitation and production groups use AVO attributes for reservoir characterization and even fluid-front monitoring. Accurate geoscience and engineering reservoir charcterization (parametrization) improve prediction of hydrocarbon reserves and reservoir production. It is therefore essential to understand which seismic attributes will best contribute to the characterization of the reservoir. This paper focuses on the accuracy of AVO attributes commonly used in reservoir characterization. In particular, the effectiveness of intercept-gradient, lambda-mu-rho λ-μ-ρ), and elastic impedance AVO attributes, and their ability to accurately predict reservoir extent are presented. Derivative AVO attribute volumes that are caliabrated to well data are also compared.

… and basic principles of AVO analysis and Elastic Impedance inversion methods ranging from … In general it starts with well data processing such as making the log of Elastic Impedance …

… , 49, 155 rock-physics foundation for AVO analysis, 15 density estimation, 16 differences in … methodology, 186 PP-wave AVO attributes, 185 3D azimuthal AVO attribute, 187 on USB …

Amplitude variation with offset (AVO) inversion of an anisotropic data set is a challenging task. Nonnegligible differences in the anisotropy parameters between the various lithologies make the seismic data AVO response completely different from the isotropic synthetic seismogram. In this case, it is difficult to invert for VP/VS and density consistent with well-log data. AVO inversion using pseudoisotropic elastic properties is a practical solution to this problem. Verification of this method was performed using data from an offshore Western Australia field. It was found that wavelet extraction and density inversion are improved significantly by replacing the isotropic elastic properties with the pseudoisotropic properties. Inverted density shows reasonable quality and therefore can be included in the reservoir characterization study. Postinversion analyses can be performed effectively on the pseudoisotropic elastic properties because crossplot analysis shows the increased separation of different lithofacies due to contrasts in anisotropy parameters. This result could have significant implications for other fields, as shale constitutes most of the overburden in conventional oil and gas fields and often shows strong elastic anisotropy.

… AVO attributes (eg, intercepts and gradients). Consequently it can be difficult for those more familiar with the conventional AVO … new AVO attributes for background Vs/Vp ratio and AVO …

Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

… of a seismic signal and the energy distribution of a seismic … spectral decomposition-based approach for hydrocarbon … mode decomposition (VMD) associated with TKE to seismic data, …

This paper presents a new methodology for computing a time-frequency map for nonstationary signals using the continuous-wavelet transform (CWT). The conventional method of producing a time-frequency map using the short time Fourier transform (STFT) limits time-frequency resolution by a predefined window length. In contrast, the CWT method does not require preselecting a window length and does not have a fixed time-frequency resolution over the time-frequency space. CWT uses dilation and translation of a wavelet to produce a time-scale map. A single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet. Previous workers have converted a time-scale map into a time-frequency map by taking the center frequencies of each scale. We transform the time-scale map by taking the Fourier transform of the inverse CWT to produce a time-frequency map. Thus, a time-scale map is converted into a time-frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented. We refer to such a map as the time-frequency CWT (TFCWT). We validate our approach with a nonstationary synthetic example and compare the results with the STFT and a typical CWT spectrum. Two field examples illustrate that the TFCWT potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization.

ABSTRACT In the West Delta Deep Marine (WDDM) concession many important gas reservoirs have been recently discovered. To improve the possibility of discovering new natural gas resources, an integrated prediction method approach is used in this study to identify the deep-water gas reservoirs. Post-stack seismic coloured inversion and the spectral decomposition attribute, combined with composite logs of five wells, are used to indicate the lateral and vertical variations of the gas channels in El Wastani Formation. Data conditioning and petrophysical analysis are performed. Both techniques help and improve visual evidence to detect gas channels. Coloured inverted seismic profile show relatively low impedance zones represent the gas channels. Spectral decomposition attribute solves the problem related to thin gas-sand layers through three different frequency magnitude values. The spatial distribution of the gas-channels properties indicates a good calibration at the well location with the effective porosity in the range of 15–35%. Comparing the results of the coloured seismic inversion with the spectral decomposition attribute, the low-frequency range through the spectral decomposition analysis is consistent with the low impedance values represents the gas channels. Furthermore, the methodology followed in the current study is advantageous to identify the gas reservoirs in various basins with similar geological settings.

PreviousNext You have accessSEG Technical Program Expanded Abstracts 2003Application of spectral decomposition to gas basins in MexicoAuthors: Michael Dean BurnettJohn CastagnaGenaro ZigaLeonel FigónTrinidad MartinezMariano TéllezRaúl VilaEfraín MendezMichael Dean BurnettFusion Geophysical, Norman, Oklahoma, John CastagnaFusion Geophysical, Norman, Oklahoma, Genaro ZigaPetroleos Mexicanos, Leonel FigónPetroleos Mexicanos, Trinidad MartinezPetroleos Mexicanos, Mariano TéllezPetroleos Mexicanos, Raúl VilaPetroleos Mexicanos, and Efraín MendezPetroleos Mexicanoshttps://doi.org/10.1190/1.1817827 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Permalink: https://doi.org/10.1190/1.1817827FiguresReferencesRelatedDetailsCited byCases of generalized low-frequency shadows of tight gas reservoirsRenhai Pu, Qiang Han, and Pengye Xu12 August 2021 | Interpretation, Vol. 9, No. 4Wavelet Analysis of Fracturing Pressure Data23 January 2018 SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 03 Jan 2005 CITATION INFORMATION Michael Dean Burnett, John Castagna, Genaro Ziga, Leonel Figón, Trinidad Martinez, Mariano Téllez, Raúl Vila, and Efraín Mendez, (2003), "Application of spectral decomposition to gas basins in Mexico," SEG Technical Program Expanded Abstracts : 2332-2332. https://doi.org/10.1190/1.1817827 Plain-Language Summary PDF DownloadLoading ...

Abstract Shoreface deltaic depositional systems (UDS) form excellent stratigraphic traps. The UDS are developed during the adverse sea-level conditions, which have a vital impact on the quantitative prediction of stratigraphic reservoirs. The prediction of accurate thickness and distribution of porous reservoir along the UDS is always a challenging job for exploration geophysicist. Since these oil and gas plays are purely thin-bedded reservoirs. They require a specific tuning frequency to resolve and predict the accurate thickness of the hydrocarbon-bearing zone. The band-limited seismic attributes are not reliable tools for developing cost-effective stratigraphic plays. The Fourier transformation tool such as continuous wavelet transforms (CWT) and thickness modelling for UDS (TMUDS) are executed on the gas-bearing UDS, Pakistan. Beyond the conventional amplitudes, the 37-Hz tuning block demarcates the gas-bearing reservoirs. The amplitude attenuation at 48-Hz validates the presence of coarse-grained sediments within the upper shoreface intervals of deltaic system. The blending of seismic, 37-Hz, and sweetness magnitudes completely recognizes the dominant reservoir fluids, which were unable to detect using the traditional mapping. The sweetness profile predicts a 20 m thick reservoir facie. The band-limited TMUDS reveals some tuning effects of ambiguous lithology and fluids. However, the post-stack seismic data processing within the designed amplitude spectrum of 12–55 Hz have greatly enhanced the physical parameters of thickness and presence of hydrocarbons. The 36-Hz trace sub-bands have predicted a 48 m thick reservoir facie along the UDS. The 36-Hz ESB confirms the presence of hydrocarbon-bearing coarse-grained deltaic facies, which are accumulated along the intersection point of shelf-slope of UDS. The quantitative plots of predicted thickness and acoustic impedances [AI] [g/c.c.*m/s] at 36-Hz CWT shows a strong correlation coefficient of R2 > 0.90. These observations take a lead over the band-limited amplitude-based seismic attributes for quantification for UDS. Hence, only the post-stack seismic data processing and interpretation based on the CWT wavelet can enhance the vertical thickness of the reservoir and the lateral extent with the least error. This workflow has robust stratigraphic implications for explorations for stratigraphic fairway for Lower Goru and acts as an analogue for local to regional clastic depositional systems within Asia and world similar depositional settings.

John P. Castagna, University of Houston, and Shengjie Sun, Fusion Geophysical discuss a number of different methods for spectral decomposition before suggesting some improvements possible with their own variation of ‘matching pursuit’ decomposition. In seismic exploration, spectral decomposition refers to any method that produces a continuous time-frequency analysis of a seismic trace. Thus a frequency spectrum is output for each time sample of the seismic trace. Spectral decomposition has been used for a variety of applications including layer thickness determination (Partyka et al, 1999), stratigraphic visualization (Marfurt and Kirlin, 2001), and direct hydrocarbon detection (Castagna et al., 2003; Sinha et al., 2005). Spectral decomposition is a non-unique process, thus a single seismic trace can produce various time-frequency analyses. There are a variety of spectral decomposition methods. These include the DFT (discrete Fourier Transform), MEM (maximum entropy method), CWT (continuous wavelet transform), and MPD (matching pursuit decomposition). None of these methods are, strictly speaking, ‘right’ or ‘wrong’. Each method has its own advantages and disadvantages, and different applications require different methods. The DFT and MEM involve explicit use of windows, and the nature of the windowing has a profound effect on the temporal and spectral resolution of the output. In general, the DFT is preferred for evaluating the spectral characteristics of long windows containing many reflection events, with the spectra generally dominated by the spacing between events. The MEM is often difficult to parameterize and may produce unstable results. The CWT is equivalent to temporal narrow-band filtering of the seismic trace and has an advantage over the DFT for broad-band signals in that the window implicit in the wavelet dictionary is frequency dependent. The CWT has a great disadvantage, however, in that the wavelets utilized must be orthogonal. The commonly used Morlet wavelet, for example, has poor vertical resolution due to multiple side lobes. Furthermore, for typical seismic signals, the implicit frequency dependent windowing of the CWT is not particularly important, and experience has shown that a DFT with a Gaussian window of appropriate length produces almost the same result as a CWT with a Morlet wavelet. MPD (Mallat and Zhang, 1993) is a more computationally intensive process than the others, but, as will be shown in this paper, it has superior temporal and spectral resolution if a compact mother wavelet is utilized. Matching pursuit decomposition involves cross-correlation of a wavelet dictionary against the seismic trace. The projection of the best correlating wavelet on the seismic trace is then subtracted from that trace. The wavelet dictionary is then cross-correlated against the residual, and again the best correlating wavelet projection is subtracted. The process is repeated iteratively until the energy left in the residual falls below some acceptable threshold. As long as the wavelet dictionary meets simple admissibility conditions, the process will converge. Most importantly, the wavelets need not be orthogonal. The output of the process is a list of wavelets with their respective arrival times and amplitudes for each seismic trace. The inverse transform is accomplished simply by summing the wavelet list and the residual, thus reconstructing the original trace. The wavelet list is readily converted to a timefrequency analysis by superposition of the wavelet frequency spectra. Simple matching pursuit has difficulty in properly determining the precise arrival time of interfering wavelets – usually it will slightly misplace the wavelets which will also result in a slightly incorrect wavelet center frequency. Also, it can be seen that the process is path dependent: a slight change in the seismic trace may result in an entirely different order of subtraction. Thus, it may result in lateral instability of the non-uniqwue time-frequency analyses. Cross-correlation of the wavelet dictionary against the seismic trace is essentially a continuous wavelet transform, so it can be seen that the method involves iteratively performing hundreds, if not thousands, of wavelet transforms for each seismic trace. In this paper, we utilize a variation of matching pursuit called exponential pursuit decomposition (EPD). The method treats complex interference patterns as containing ‘gravity wells’ at the correct wavelet locations, and the selected wavelet location is iteratively attracted to the correct location. The profound advantage of EPD over other methods is that there is no windowing, and corresponding spectral smearing. The spectra for reflections from isolated interfaces that can be resolved by the method are the same as the seismic wavelet producing those reflections. The method can thus be used with confidence for direct hydrocarbon indication and stratigraphic visualization for thin beds. The classical Heisenberg Uncertainty Principle states that the product of temporal and frequency resolution is constant. One must normally pay the price of decreasing resolution in one domain, to increase resolution in the other. In EPD, there is no windowing and it is the bandwidth of the digital seismic data that limits resolution, not the windowing process. Thus, the Heisenberg Uncertainty Principle does not come into play. As a result, EPD provides better temporal AND spectral resolution than the other methods. In comparing spectral decomposition methods, it is important to keep in mind what the goal of the analysis is.

Low-frequency seismic anomalies have long been a subject of interest to geoscientists involved in hydrocarbon exploration since such ‘gas-shadows’ can be a direct hydrocarbon indicator (DHI). Published studies have demonstrated evidence for them potentially resulting from increased seismic attenuation and velocity dispersion, as a result of hydrocarbon saturation. The topic gained wide interest during the 2000s with well-cited publications by Castagna et al. (2003), Ebrom (2004), Chapman et al. (2005, 2006) and Odebeatu et al. (2006), to name a few. The consensus of these studies was that hydrocarbon related frequency effects are predicted to be detectable on stacked seismic data. Furthermore, it has been suggested that low frequencies tend to show the highest sensitivity to fluid changes. (e.g. Korneev et al., 2004). The effect has been shown not only from seismic data; studies involving laboratory tests and borehole data provide similar conclusions. Overall there is general agreement that the effect exists. However the physical cause remains inconclusive. Several studies have also shown evidence that hydrocarbon reservoirs have an amplitude-versus-offset (AVO) frequency dependence (e.g. Chapman et al. (2005, 2006), Odebeatu et al. (2006), Liu et al. (2006), Ren et al. (2007), Zhang et al. (2007), Chen et al. (2008), Wu et al. (2014)). In the modelled case of gas-saturated sands, low frequencies have tended to show the greatest change in amplitude with offset (Figure 1a). The idea of using AVO and Spectral Decomposition (SD) to understand frequency anomalies and potential links to reservoir fluid content has been approached by several of these authors. The published work largely used model-based techniques to predict AVO effects for different frequencies (Figure 1b), and then applied this information to aid interpretation of anomalies observed on iso-frequencies sections. The results convincingly suggest that there are differences in the spectral content of seismic data which could be exploited for improved hydrocarbon identification. The results of these pioneering publications is the basis from which the workflow presented in this paper was conceived. Here these ideas are approached from an interpretation perspective and applied in a practical manner using commercial interpretation software.

… Spectral decomposition is a robust tool for seismic data … In this paper, we show an example of spectral decomposition … -frequency, high-amplitude hydrocarbon detection in the Mananon …

Carbonate reservoir of Kais Formation in Salawati Basin, West Papua, is the most famous oil and gas reservoir in the eastern part of Indonesian Archipelago since 1970’s. Nowadays, new prospects in this area are more challenging and most relevant near the infrastructure of previous oil and gas fields. In this study, a relatively new seismic dataset was investigated to figure out new prospects in carbonate reservoir rocks in the area of interest. In this preliminary study, where seismic data are not supported by well data, direct hydrocarbon indicator (DHI), seismic attribute, and spectral decomposition (CWT: continuous wavelet transform) allow the authors to characterize the reservoir geometry and to predict pore fluids within the reservoir rocks. The reservoir geometry of carbonate reef of Kais Formation (C1) was identified by seismic reflectors with high amplitude contrast at the top C1. The hydrocarbon indicator was predicted by DHI where dim spots, flat spots, and polarity reversals are indicative of hydrocarbon prospects. From the attribute analysis, the attribute instantaneous amplitude detected the top carbonate C1, whereas pore fluids were predicted from high sweetness attribute. In addition, spectral decomposition CWT method confirms the top C1, identified as saturated rock by the frequency of 10 Hz, 20 Hz, and 30 Hz. Based on a seismic study in the researched area, the target zone is expected to be a very promising hydrocarbon reservoir, specifically a carbonate reservoir. As a result, the preferred well-test location is in a region with access to the Kais Formation limestone reef layer. This study can assist in reservoir characterization, especially in areas with limited well control.

We have developed an example of hydrocarbon detection for an Ordovician cavern carbonate reservoir in western China with a burial depth exceeding 6600 m using amplitude variation with offset (AVO) and spectral decomposition. We selected six production wells, three prolific oil wells, and three brine wells to test the hydrocarbon detection method. The three oil wells have been producing for more than three years, and the three water wells only produce brine. We performed spectral decomposition to the angle gathers and analyzed amplitude variation patterns with incidence angles for different spectral components. Specifically, we compared the time corresponding to the peak spectral amplitude for different spectral components for the oil- and brine-saturated carbonate reservoirs. The main findings are as follows: (1) Oil-saturated cavern carbonate reservoirs show decreasing peak time with increasing frequency; i.e., the high-frequency components travel faster than do the low-frequency components. The maximum time difference between the 10 and 50 Hz spectral components could reach 35 ms. In contrast, the brine-saturated carbonate reservoirs do not exhibit conspicuous variation in the peak time, (2) AVO attributes extracted from the low-frequency spectral gathers are more robust than those extracted from the original seismic gathers, (3) oil-saturated cavern carbonate reservoirs cause strong energies in the low-frequency spectral components and severe attenuation to the high-frequency spectral components at large incidence angles. In contrast, the brine-saturated carbonate reservoirs do not produce such phenomenon. Rock physics analysis for carbonate reservoirs under different saturation conditions was conducted. The synthetic gathers were generated for carbonate reservoirs under oil- and brine-saturated conditions. The spectrally decomposed synthetic gathers are in agreement with the real gathers. The results indicate that AVO analysis of spectrally decomposed prestack gathers could be used as an effective hydrocarbon detection method for carbonate reservoirs.

… Next, we apply the different spectral decomposition techniques to real seismic data from Alberta, Canada. The data are from a hydrocarbon‐bearing sand reservoir located at shallow …

… This study includes some innovations to combinative application of different seismic methods and correlates them with geological evidences. It became puzzling, when we found that …

… to properly interpret seismic phase changes caused by the introduction of hydrocarbons, it … , a combination of spectral decomposition and phase decomposition can be employed to …

… Through case studies, the spectral decomposition with AVF analysis is demonstrated, … hydrocarbons. This work highlights the transformative role of spectral decomposition in seismic …

The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.

… We attempt herein to explore a real seismic section for indications about hydrocarbon responses. The target is gas-saturated sand located. We, first, use CWT to investigate its frequency …

Analyzing seismic data, particularly when engaging in attribute analysis and spectral decomposition is time-consuming and requires human resources involvement. Usually, interpreters manually select and blend frequencies to identify geological patterns in seismic data. To do a proper analysis, hundreds of combinations should be generated, which introduces notable human bias. The study objectifies the automation of spectral decomposition interpretation through the application of machine learning techniques aiming to identify and classify distinct seismic facies. The proposed algorithm includes an integrated approach of Self-Organizing Maps (SOM) and K-Means. The input data is the frequency, spectrally decomposed from seismic data and projected on a target seismic horizon. To ensure geological relevance, a broad frequency range is considered, and steps are computed using the Structural Similarity Index Measurement algorithm. The normalized data goes through the SOM algorithm and clusters similar patterns along the frequency axis. To reduce the number of clusters, SOM nodes go through the K-means algorithm. The quality of clustering is evaluated by analyzing the unified distance matrix with weights colored by the relevant cluster. The proposed algorithm generates a single horizon containing clustered seismic patterns. These patterns are then compared to the geologist's interpretation outcome, which was based on the "best matching" spectral decomposition RGB blending. The algorithm successfully identifies and clusters mud volcanoes along with their invasion zones and chimneys into separate clusters. It also clusters channelized facies into 2-3 distinct clusters, automatically separates floodplain areas, and identifies poor data areas. Due to the input of a wide frequency range, the algorithm goes beyond expected geological features. It enhances channel continuity and automatically identifies thinner channelized features that were not depicted on manually created maps. The algorithm's capabilities provide valuable insights and improve the overall accuracy of seismic data interpretation. This paper introduces a novel algorithmic approach for interpreting seismic data that goes beyond traditional methods, and offers a fresh perspective on seismic data interpretation, by enabling fully automated and quick seismic facies clustering, reducing human bias, and improving feature identification. It offers practical applications in reservoir characterization and hydrocarbon exploration, as well as valuable insights for industry professionals, researchers, and decision-makers involved in seismic data interpretation and exploration activities.