Hydrocarbon Identification Research Articles

Analyzing the seismic response characteristics of P, SV, and SH waves to reservoir parameters using physical modeling

Changes in reservoir parameters cause differences in seismic response characteristics, which can reflect the formation lithology and fluids. We conduct seismic physical modeling and seismic response characteristic analysis of P, SV, and SH wavefields. A seismic physical model is developed in the laboratory, which included several groups of sandstone blocks for simulating reservoir parameters, such as different fluid and clay content. Two-dimensional wavefield data of P-P, SV-SV, and SH-SH waves are acquired and processed in the laboratory. Compared with P waves, SV and SH waves are insensitive to pore fluids, and the SH stack profile is superior to the SV stack profile. The reflection events at the interface are slightly better for the SH-wave section than for the SV-wave section. The reflection coefficient of P waves varies greatly on amplitude variation with angle (AVA) gathers and is significantly influenced by factors such as fluids. The variation in the SV-wave reflection coefficient on AVA gathers is not as significant as that of P waves, and SV and SH waves are more conducive in identifying whether the block contained clay. The SH wave is more reliable for seismic imaging. Overall, this study can assist in combining different seismic wave data for better hydrocarbon identification and reservoir description.

A seismic elastic moduli module for the measurements of low-frequency wave dispersion and attenuation of fluid-saturated rocks under different pressures

Knowledge about the seismic elastic modulus dispersion, and associated attenuation, in fluid-saturated rocks is essential for better interpretation of seismic observations taken as part of hydrocarbon identification and time-lapse seismic surveillance of both conventional and unconventional reservoir and overburden performances. A Seismic Elastic Moduli Module has been developed, based on the forced-oscillations method, to experimentally investigate the frequency dependence of Young's modulus and Poisson's ratio, as well as the inferred attenuation, of cylindrical samples under different confining pressure conditions. Calibration with three standard samples showed that the measured elastic moduli were consistent with the published data, indicating that the new apparatus can operate reliably over a wide frequency range of f∈[1–2000, 106] Hz. The Young's modulus and Poisson's ratio of the shale and the tight sandstone samples were measured under axial stress oscillations to assess the frequency- and pressure-dependent effects. Under dry condition, both samples appear to be nearly frequency independent, with weak pressure dependence for the shale and significant pressure dependence for the sandstone. In particular, it was found that the tight sandstone with complex pore microstructure exhibited apparent dispersion and attenuation under brine or glycerin saturation conditions, the levels of which were strongly influenced by the increased effective pressure. In addition, the measured Young's moduli results were compared with the theoretical predictions from a scaled poroelastic model with a reasonably good agreement, revealing that the combined fluid flow mechanisms at both mesoscopic and microscopic scales possibly responsible for the measured dispersion.

Prediction of organic richness in shale gas reservoirs using a novel organic-inorganic decoupling seismic inversion method

Organic richness is associated with total hydrocarbon content and influences gas generation in shale reservoirs. The prediction of organic enrichment using seismic methods provides valuable information for identifying promising shale gas areas. Current seismic methods typically estimate organic richness using empirical relationships between organic matter (OM) content and elastic properties; these may be suitable for specific areas but cannot be generalized for universal applications. Therefore, developing a physics-based method for predicting OM content in shale gas reservoirs is critical. In this study, we proposed a novel organic-inorganic decoupling amplitude variation with offset (AVO) method by integrating the classical AVO equation with solid substitution theory. OM was considered solid pore-fillings saturated in the shale matrix. The proposed decoupling equation was extended to establish a frequency-dependent inversion framework for computing OM-related dispersion attributes, allowing for direct estimations of organic richness. Theoretical analyses suggested that the accuracy of the decoupling equation could be achieved via adjustable parameters determined using the rock physics method. Model tests demonstrated that the OM-related dispersion attributes were more sensitive to variations in OM content than the traditional P-wave velocity dispersion attribute and the commonly used elastic properties. The results of real data applications validated the superiority of our proposed OM-related dispersion attributes for reliable organic richness estimation, with the estimated results agreeing well with gas production in the study area. As a result, several promising shale gas zones were identified for further exploration. Our proposed method extends traditional fluid factors and can deal with the cases of both fluid and solid saturations in hydrocarbon resources, with widespread applications for hydrocarbon identification in various unconventional reservoirs.

Reservoir Hydrocarbon Identification Method Based on Prestack Seismic Frequency-Dependent Anisotropic Inversion

Reservoir Hydrocarbon Identification Method Based on Prestack Seismic Frequency-Dependent Anisotropic Inversion

Direct hydrocarbon identification in shale oil reservoirs using fluid dispersion attribute based on an extended frequency-dependent seismic inversion scheme

The identification of hydrocarbons using seismic methods is critical in the prediction of shale oil reservoirs. However, delineating shales of high oil saturation is challenging owing to the similarity in the elastic properties of oil- and water-bearing shales. The complexity of the organic matter properties associated with kerogen and hydrocarbon further complicates the characterization of shale oil reservoirs using seismic methods. Nevertheless, the inelastic shale properties associated with oil saturation can enable the utilization of velocity dispersion for hydrocarbon identification in shales. In this study, a seismic inversion scheme based on the fluid dispersion attribute was proposed for the estimation of hydrocarbon enrichment. In the proposed approach, the conventional frequency-dependent inversion scheme was extended by incorporating the PP-wave reflection coefficient presented in terms of the effective fluid bulk modulus. A rock physics model for shale oil reservoirs was constructed to describe the relationship between hydrocarbon saturation and shale inelasticity. According to the modeling results, the hydrocarbon sensitivity of the frequency-dependent effective fluid bulk modulus is superior to the traditional compressional wave velocity dispersion of shales. Quantitative analysis of the inversion results based on synthetics also reveals that the proposed approach identifies the oil saturation and related hydrocarbon enrichment better than the above-mentioned conventional approach. Meanwhile, in real data applications, actual drilling results validate the superiority of the proposed fluid dispersion attribute as a useful hydrocarbon indicator in shale oil reservoirs.

High-resolution seismic impedance inversion integrating the closed-loop convolutional neural network and geostatistics: an application to the thin interbedded reservoir

Abstract Seismic impedance inversion is one of the key techniques for quantitative seismic interpretation. Most conventional post-stack seismic impedance inversion approaches are based on the linear theory, whereas the relationship between seismic response and impedance is highly nonlinear. Thus, it is challenging to implement conventional inversion methods to obtain high-resolution impedance for reservoir investigation. Convolutional neural network (CNN), a superior deep neural network, has a strong learning ability, which can learn from data and establish complex nonlinear mapping. However, CNN-based methods are generally heavily dependent on amounts of labeled data. Hence, an alternative seismic inversion approach is proposed that combines the closed-loop CNN and geostatistics. The closed-loop CNN is less dependent on labeled data, characterized by utilizing labeled data and unlabeled data simultaneously to train the neural network. The two subnets represent forward modeling and inversion respectively, constraining each other during the neural network training. Geostatistics can be used to enrich the training data for neural network training, taking into account geological and geophysical prior information. Synthetic data testing reveals that the proposed inversion scheme can obtain more reasonable results benefiting from labeled training data augmentation. The proposed inversion scheme is applied to the field data for identifying thin interbedded reservoir within delta depositional system. The predicted results obtained by the proposed inversion scheme are consistent with well log data and geological settings, offering insights into reservoir characterization and hydrocarbon identification.

Reservoir characterization for hydrocarbon detection using Amplitude Variation with Angles constrained by localized rock physics template

Seismic data are prone to ambiguities because different scenarios of geological structures and rock properties can produce similar seismic reflections. The poorly compacted sandstone reservoirs of the ‘Jay’ Field produced seismic amplitude anomalies which may not be diagnostic of hydrocarbon presence. In this study, the different seismic amplitude reflections from ‘Jay’ Field reservoirs were investigated to determine the effect of fluids and lithologies on the observed seismic amplitudes for possible hydrocarbon identification away from well location. Amplitude Variation with Angles (AVA) and Rock physics techniques were used to analyze amplitude responses from sandstone reservoirs. These seismic amplitudes were extracted from the near and far partial angle stacks to identify changes in amplitudes with angles. Reservoir properties were examined to determine their influence on seismic responses through Rock Property Template (RPT) analysis. Well logs revealed that three of the seven identified reservoirs (Sands A, B and C) are brine saturated, Sand D is oil while Sands E, F and G are gas bearing. The reservoirs stratification is mainly the juxtaposition of low impedance sandstone and high impedance shale, such that P-velocity and density significantly influence impedance contrast in sandstone reservoirs and shale lithologies respectively. Changes in seismic amplitudes from near to far angles around Well location show that the gas reservoirs produce different AVOs that correspond to Classes II, III and IV respectively. The established Well log RPT supports AVA results of gas presence in the identified area away from Well location. This helps to reduce the risk associated with seismic amplitude ambiguities and enhanced certainty of gas presence at the location away from Well log information. In similar geological scenario, this work can serve as a useful guide to gas sand detection from seismic data.

The testing of Qp and QsQp equation in Kujung I Formation, East Java, Indonesia

Hydrocarbon-bearing reservoir shows attenuation on seismic section, but the use of attenuation in hydrocarbon exploration is less popular than other attributes. This is due to the sophisticated and advanced technology required to measure attenuation. Meanwhile, some studies were designed to estimate attenuation by formulating rock quality factor equations from a wire-line log, although others run on sandstone samples. Therefore, this study aims to estimate rock quality factors in carbonate rock of Kujung Formation using three different methods, discriminate hydrocarbon-bearing reservoir, and identifying the impact of carbonate facies. The results showed the P-wave quality factor method is sensitive to discriminate high gas saturation on facies A. Meanwhile, the QsQp ratio is sensitive to discriminate shale and carbonate. The SQp and SQs methods are sensitive to discriminate hydrocarbon-bearing reservoirs and less affected by carbonate facies. The novelty of this is to estimate the rock quality factor from various methods and identify the impact of facies in hydrocarbon identification, especially for carbonate reservoirs in Northeast Java Basin. Therefore, a new understanding of facies impact on rock quality factor contributes to choosing the best methods in discriminating pore-filling fluid identification.

Seismic wave attenuation in carbonate rock and its implication to hydrocarbon-bearing reservoir identification, East Java, Indonesia

The heterogeneity of carbonate rock properties triggers propagated seismic wave to get some attenuations. This mechanism allows that attenuation can be applied in hydrocarbon exploration to identify lithology, porosity, fluid content and fracture identification. Unfortunately, to measure the degree of attenuation, the sophisticated tools and advanced processing technology are required. Thus, some methods were formulated to estimate attenuation. This study is aimed to estimate and compare attenuation from wireline log interpretation by using four different methods. The study is also supported by a series of exploration wells penetrating Kujung I Formation in northwestern edge of North East Java Basin, Indonesia. The result shows that only Qs from Zhang et al method is overestimate. P-wave quality factor of Klimentos and Zhang methods and scaled quality factor (SQp and SQs) method are sensitive to identify hydrocarbon. Furthermore, lithofacies also affects estimated rock quality factor. The novelty of study is to estimate and compare the attenuation from different method and review its result and application in hydrocarbon identification, especially for carbonate reservoir in North East Java basin, Indonesia. So that, a new understanding of attenuation characteristic in carbonate reservoir contributes to discriminate pore fluid identification in hydrocarbon exploration.

Application of common reflection surface (CRS) to velocity variation with azimuth (VVAz) inversion of the relatively narrow azimuth 3D seismic land data

Abstract The fracture direction and its intensity are critical properties related to hydrocarbon characterization and identification. Both these properties have an essential role in identifying the direction of hydrocarbon migration, determining the sweet spot area, and optimizing the drilling design. The velocity variation with azimuth (VVAz) is a well-known method to estimate the fracture direction and its intensity. This method is of widespread interest because it predicts the properties based on seismic data without any practical constraints. Despite this interest, the technique requires rich azimuth 3D seismic data in our case, which is rare. This study aims to apply regularization and interpolation by including the wave front attributes based on the Common Reflection Surface (CRS) method before the VVAz inversion. The motivation of using the CRS method is to enrich the current azimuth of the 3D seismic data and improve the S/N ratio. The synthetic and the real 3D seismic data are evaluated to examine the interpolation scheme of the proposed CRS method’s performance. Based on the evaluation of the 3D seismic data after regularization, the amplitude versus offset (AVO) phenomena, and the VVAz inversion results are relatively consistent (or matched) with the model. A similar result is found for the case of real 3D seismic data. A significant positive correlation between the fracture intensity of FMI and the real seismic data of about 0.9 is obtained. Therefore, CRS can be used as a regularization and interpolation method before the VVAz inversion of the relatively narrow azimuth 3D seismic data.

Modeling the effects of fracture infill on frequency-dependent anisotropy and AVO response of a fractured porous layer

In a fractured porous hydrocarbon reservoir, wave velocities and reflections depend on frequency and incident angle. A proper description of the frequency dependence of amplitude variations with offset (AVO) signatures should allow effects of fracture infills and attenuation and dispersion of fractured media. The novelty of this study lies in the introduction of an improved approach for the investigation of incident-angle and frequency variations-associated reflection responses. The improved AVO modeling method, using a frequency-domain propagator matrix method, is feasible to accurately consider velocity dispersion predicted from frequency-dependent elasticities from a rock physics modeling. And hence, the method is suitable for use in the case of an anisotropic medium with aligned fractures. Additionally, the proposed modeling approach allows the combined contributions of layer thickness, interbedded structure, impedance contrast and interferences to frequency-dependent reflection coefficients and, hence, yielding seismograms of a layered model with a dispersive and attenuative reservoir. Our numerical results show bulk modulus of fracture fluid significantly affects anisotropic attenuation, hence causing frequency-dependent reflection abnormalities. These implications indicate the study of amplitude versus angle and frequency (AVAF) variations provides insights for better interpretation of reflection anomalies and hydrocarbon identification in a layered reservoir with vertical transverse isotropy (VTI) dispersive media.

Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone

Knowledge of dispersion and attenuation is essential for better reservoir characterization and hydrocarbon identification. However, limited by reliable laboratory data at seismic frequency bands, the roles of rock and fluid properties in inducing dispersion and attenuation are still poorly understood. Here we perform a series of laboratory measurements on Bentheimer and Bandera sandstone under both vacuum-dry and partially water-saturated conditions at frequencies ranging from 2 to 600 Hz. At vacuum-dry conditions, the bulk dispersion and attenuation in Bandera sandstone with more clay contents are distinctly larger than those in Bentheimer sandstone, suggesting clay contents might contribute to the inelasticity of the rock frame. The partially water-saturated results show the combined effects of rock permeability and fluid saturation on bulk dispersion and attenuation. Even a few percent of gas can substantially dominate the pore-fluid relaxation by providing a quick and short communication path for pore pressure gradients. The consequent bulk dispersion and attenuation are negligible. Only as the samples are approaching fully water-saturated conditions, rock permeability begins to play an essential role in the pore-fluid relaxation. For Bandera sandstone with lower permeability, a partially relaxed status of pore fluids is achieved when the gas saturation is lower than 5%, accompanied by significant attenuation and dispersion. Cited as : Wei, Q., Wang, Y., Han, D., Sun, M., Huang, Q. Combined effects of permeability and fluid saturation on seismic wave dispersion and attenuation in partially-saturated sandstone. Advances in Geo-Energy Research, 2021, 5(2): 181-190, doi: 10.46690/ager.2021.02.07

An Integrated Petrophysical Workflow for Fluid Characterization and Contacts Identification Using NMR Continuous and Stationary Measurements in a High-Porosity Sandstone Formation, Offshore Norway

The Alvheim Field in the Norwegian North Sea was discovered in 1998. Two wells were drilled in 2018 in the Gekko structure to confirm oil column height and to evaluate reservoir quality in the Heimdal Formation. A comprehensive wireline logging program, including NMR and formation testing, was optimized to reduce formation evaluation uncertainty. Evaluating fluid properties, oil column height, and reservoir quality were primary objectives. Well A was first drilled on the south of the structure, followed by Well B on the north of the structure. Reservoir quality encountered in both wells was very good, and a project to develop these resources is currently in the selection phase. Formation evaluation uncertainty encompassing pore geometry distribution, permeability, reservoir quality, and hydrocarbon identification are mitigated by studying the nuclear magnetic resonance (NMR) log response. NMR fluid typing has been widely used in the oil industry since the 1990s. NMR fluid typing today is a combination of the contrast of spin relaxation time T1, the spin-spin relaxation time T2 (T1T2), and the diffusivity (T2D) of formation fluids (Chen et al., 2016). NMR fluid typing can be obtained from a continuous log and/or stationary log measurements. This paper showcases excellent, textbook-quality NMR data, as well as the integration of NMR data in the petrophysical workflow. High-confidence fluid properties and fluid contacts are determined. This paper also highlights a comparison of NMR data acquired in stationary vs. continuous depth-based log modes in both wells. The continuous log data quality is equivalent to stationary data, implying continuous log data quality is sufficient for reliable NMR fluid properties evaluation without depending on time-consuming stationary NMR measurements. Reducing logging operations rig time is very advantageous in the North Sea, where drilling rig operations cost is high, and enhanced rig time management is constantly required.

Hydrocarbon identification using the AVO response correction method based on high-resolution complex spectral decomposition

Amplitude-variation-with-offset (AVO) technique is one of the primary quantitative hydrocarbon discrimination methods with prestack seismic data. However, the prestack seismic data are usually have low data quality, such as nonflat gathers and nonpreserved amplitude due to absorption, attenuation, and/or many other reasons, which usually lead to a wrong AVO response. The Neogene formations in the Huanghekou area of the Bohai Bay Basin are unconsolidated clastics with a high average porosity, and we find that the attenuation on seismic signal is very strong, which causes an inconsistency of AVO responses between seismic gathers and its corresponding synthetics. Our research results indicate that the synthetic AVO response can match the field seismic gathers in the low-frequency end, but not in the high-frequency components. Thus, we have developed an AVO response correction method based on high-resolution complex spectral decomposition and low-frequency constraint. This method can help to achieve a correct high-resolution AVO response. Its application in Bohai oil fields reveals that it is an efficient way to identify hydrocarbons in rocks, which provides an important technique for support in oil and gas exploration and production in this area.

Hydrocarbon Identification Based on Bright Spot Technique by Using Matching Pursuit and RGB Blending

In seismic exploration, matching pursuit (MP), which decomposes wavelets based on best signal matching, can not only extract accurate frequency information, but also can pick up the strong amplitudes of possible bright spots adaptively according to the threshold or iteration control. RGB (Red-Green-Blue) blending technique of spectral bands can make full use of the information of all frequency bands, which can reflect the general frequency changes of seismic data reducing the interpretation ambiguity. Therefore, in order to improve the precision of interpretation based on bright spot technique, the authors take both amplitude and frequency into account and propose a new approach for hydrocarbon identification by combining both methods. First, we choose the decomposed wavelets of strong amplitudes related to hydrocarbons in MP algorithm to predict the possible bright spots, so as to remove the effect of unrelated reflections in the bright spots interpretation. Then, RGB blending technique is used to the low-, mid-, and high-frequency spectral bands of the bright spot prediction section to assist for the hydrocarbon identification. Finally, the validity of the method is verified by both the model and field data tests. Results demonstrate that the proposed method can improve the precision of hydrocarbon identification, and reduce the interpretation ambiguity of the reservoirs.

Spectral Decomposition AVO attributes for identifying potential hydrocarbon-related frequency anomalies

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.

X-ray fluorescence analysis of the Bakken and Three Forks Formations and logging applications

The ability to characterize subsurface lithology downhole is critical for hydrocarbon identification and subsequent production. Well logging methods frequently used in industry are gamma ray, spontaneous potential, and resistivity principles. Few publications have addressed the feasibility of using X-ray fluorescence (XRF) as a logging method. The exploitation of deep shale petroleum systems will demand greater stratigraphic resolution; X-ray fluorescence well logging (XRFWL) may accurately and efficiently identify geochemical signatures in rock samples, which could give information about hydrocarbon bearing strata in both vertical and horizontal drilling applications when depth matching core data to wells is performed.This case study analyzed eight core sections from the lower Bakken and Three Forks Formation of the Williston Basin in North Dakota using X-ray fluorescence. The Charlotte 1-22H core sequence from Continental Resources, Inc. (Continental Resources) was also included to assess the elemental composition of the stratigraphic interval spanning the Lodgepole, Bakken, Pronghorn, and Three Forks Formations. Core sections were exposed to X-ray at 45-keV excitation voltages to provide fluorescence spectra; count rate values were obtained and elemental ratios were calculated to assess the geochemical composition of each stratigraphic interval. From these XRF analyses we created chemostratigraphic logs representing the subsurface lithology of the Williston Basin diagenetic processes in the host unit were then assessed based on elemental ratio quantity. This novel case study of the Bakken and Three Forks Formations in North Dakota may not necessarily be dispositive for identifying other geologic units.Understanding the spatial distribution of lightweight, mid-range, and trace metal elements in the Bakken hydrocarbon system might lead to a better comprehension of what affects the accuracy and precision of typical industry logs. The ability to measure the elemental composition of core samples and drill cuttings precisely and accurately will allow for further insight into how elemental composition changes can be correlated with diagenetic changes of the Bakken Petroleum System. Using a portable XRF (pXRF) brings this analysis method closer to the drilling operations and provides quick turnaround of samples.

Inverse Opal Photonic Crystals as an Optofluidic Platform for Fast Analysis of Hydrocarbon Mixtures.

Most of the reported optofluidic devices analyze liquid by measuring its refractive index. Recently, the wettability of liquid on various substrates has also been used as a key sensing parameter in optofluidic sensors. However, the above-mentioned techniques face challenges in the analysis of the relative concentration of components in an alkane hydrocarbon mixture, as both refractive indices and wettabilities of alkane hydrocarbons are very close. Here, we propose to apply volatility of liquid as the key sensing parameter, correlate it to the optical property of liquid inside inverse opal photonic crystals, and construct powerful optofluidic sensors for alkane hydrocarbon identification and analysis. We have demonstrated that via evaporation of hydrocarbons inside the periodic structure of inverse opal photonic crystals and observation of their reflection spectra, an inverse opal film could be used as a fast-response optofluidic sensor to accurately differentiate pure hydrocarbon liquids and relative concentrations of their binary and ternary mixtures in tens of seconds. In these 3D photonic crystals, pure chemicals with different volatilities would have different evaporation rates and can be easily identified via the total drying time. For multicomponent mixtures, the same strategy is applied to determine the relative concentration of each component simply by measuring drying time under different temperatures. Using this optofluidic sensing platform, we have determined the relative concentrations of ternary hydrocarbon mixtures with the difference of only one carbon between alkane hydrocarbons, which is a big step toward detailed hydrocarbon analysis for practical use.

Hydrocarbon identification by application of improved sparse constrained inverse spectral decomposition to frequency-dependent AVO inversion

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 fast complex domain-matching pursuit algorithm and its application to deep-water gas reservoir detection

The main emphasis of exploration and development is shifting from simple structural reservoirs to complex reservoirs, which all have the characteristics of complex structure, thin reservoir thickness and large buried depth. Faced with these complex geological features, hydrocarbon detection technology is a direct indication of changes in hydrocarbon reservoirs and a good approach for delimiting the distribution of underground reservoirs. It is common to utilize the time–frequency (TF) features of seismic data in detecting hydrocarbon reservoirs. Therefore, we research the complex domain-matching pursuit (CDMP) method and propose some improvements. First is the introduction of a scale parameter, which corrects the defect that atomic waveforms only change with the frequency parameter. Its introduction not only decomposes seismic signal with high accuracy and high efficiency but also reduces iterations. We also integrate jumping search with ergodic search to improve computational efficiency while maintaining the reasonable accuracy. Then we combine the improved CDMP with the Wigner–Ville distribution to obtain a high-resolution TF spectrum. A one-dimensional modeling experiment has proved the validity of our method. Basing on the low-frequency domain reflection coefficient in fluid-saturated porous media, we finally get an approximation formula for the mobility attributes of reservoir fluid. This approximation formula is used as a hydrocarbon identification factor to predict deep-water gas-bearing sand of the M oil field in the South China Sea. The results are consistent with the actual well test results and our method can help inform the future exploration of deep-water gas reservoirs.