Quantitative Seismic Interpretation Research Articles

Effect of reservoir heterogeneities on elastic and seismic properties of Lower Cretaceous Sand Intervals, Lower Indus Basin of Pakistan

The use of rock physics modeling and petrophysical analysis has become an integral part of quantitative seismic interpretation. In the present study, the above-mentioned techniques are applied to the reservoir interval of Basal Sand of Cretaceous age widely distributed in the Lower Indus Basin of Pakistan. The data used for this purpose consists of complete suits of wireline logs from four well A, B, C and D. The primary goal of this work is to understand the trends of seismic parameters such as P-wave velocity (VP), S-wave velocity (VS), bulk modulus, bulk density as a function of fluid saturation and porosity in this sand interval. Petrophysical analysis based on well logs is used to obtain petrophysical properties such as porosity, the volume of shale, fluid saturation, P-wave velocity (VP), S-wave velocity (VS) and density at reservoir conditions. The results show that seismic parameters are very sensitive to fluid saturation, fluid types and rock-forming sediments. Reservoir porosity and fluid saturation control the elastic response of Basal Sand due to which seismic velocities decrease with an increase in porosity and fluid saturation.

Multivariate probabilistic rock-physics models using Kumaraswamy distributions

Rock-physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well-log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock-physics models to deterministic measurements, I have applied the models to the probability density function (PDF) of the measurements. This approach has been previously adopted in the literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the nonsymmetric behavior. The proposed approach is based on the Kumaraswamy PDF for continuous random variables, which allows modeling double-bounded nonsymmetric distributions and is analytically tractable, unlike beta or Dirichlet distributions. I have developed a probabilistic rock-physics model applied to double-bounded continuous random variables distributed according to a Kumaraswamy distribution and derived the analytical solution of the probability distribution of the rock-physics model predictions. The method is evaluated for three rock-physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksöz’s inclusion model.

Quantitative seismic interpretation of the Lower Cretaceous reservoirs in the Valdemar Field, Danish North Sea

A quantitative seismic interpretation study is presented for the Lower Cretaceous Tuxen reservoir in the Valdemar Field, which is associated with heterogeneous and complex geology. Our objective is to better outline the reservoir quality variations of the Tuxen reservoir across the Valdemar Field. Seismic pre-stack data and well logs from two appraisal wells form the basis of this study. The workflow used includes seismic and rock physics forward modelling, attribute analysis, a coloured inversion, and a Bayesian pre-stack inversion for litho-fluid classification. Based on log data, the rock physics properties of the Tuxen interval reveal that the seismic signal is more governed by porosity than water-saturation changes at near-offset (or small angle). The coloured and Bayesian inversion results were generally consistent with well-log observations at the reservoir level and conformed to interpreted horizons. Although the available data have some limitations and the geological setting is complex, the results implied more promising reservoir quality in some areas than others. Hence, the results may offer useful information for delineating the best reservoir zones for further field development and selecting appropriate production strategies.

Fault Detection on Seismic Structural Images Using a Nested Residual U-Net

Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. We develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images.

Dolomitization geometry and reservoir quality from supervised Bayesian classification and probabilistic neural networks: Midland Basin Leonardian Wichita and Clear Fork Formations

Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian Basin. Early workers have found that the platform and shelf-top carbonates were dolomitized, whereas slope and basinal carbonates remained calcitic, proposing a reflux dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and although exhibiting high resolution vertically, such determinations are laterally sparse. We have used supervised Bayesian classification and probabilistic neural networks (PNN) on a 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity, we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well-log crossplots of neutron porosity and acoustic impedance to determine the a priori proportions of the lithology and the probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of the acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. The acoustic impedance volume was obtained by model-based poststack inversion, and the neutron porosity volume was obtained by the PNN. Our results best supported a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while the dolomitization is decreasing, but with sea-level forcing. With this study, we determined that diagenesis and the corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis

The determination of subsurface elastic property models is crucial in quantitative seismic data processing and interpretation. This problem is commonly solved by deterministic physical methods, such as tomography or full-waveform inversion. However, these methods are entirely local and require accurate initial models. Deep learning represents a plausible class of methods for seismic inversion, which may avoid some of the issues of purely descent-based approaches. However, any generic deep learning network capable of relating each elastic property cell value to each sample in a seismic data set would require a very large number of degrees of freedom. Two approaches might be taken to train such a network: first, by invoking a massive and exhaustive training data set and, second, by working to reduce the degrees of freedom by enforcing physical constraints on the model-data relationship. The second approach is referred to as “physics-guiding.” Based on recent progress in wave theory-designed (i.e., physics-based) networks, we have developed a hybrid network design, involving deterministic, physics-based modeling and data-driven deep learning components. From an optimization standpoint, a data-driven model misfit (i.e., standard deep learning) and now a physics-guided data residual (i.e., a wave propagation network) are simultaneously minimized during the training of the network. An experiment is carried out to analyze the trade-off between two types of losses. Synthetic velocity building is used to examine the potential of hybrid training. Comparisons demonstrate that, given the same training data set, the hybrid-trained network outperforms the traditional fully data-driven network. In addition, we perform a comprehensive error analysis to quantitatively compare the fully data-driven and hybrid physics-guided approaches. The network is applied to the SEG salt model data, and the uncertainty is analyzed, to further examine the benefits of hybrid training.

Rock property modelling and sensitivity analysis for hydrocarbon exploration in OSSY field, Niger Delta Basin

The application of quantitative interpretation techniques for hydrocarbon prospect evaluation from seismic has become so vital. The effective employment of these techniques is dependent on several factors: the quality of the seismic and well data, sparseness of data, the physics of rock, lithological and structural complexity of the field. This study adopts reflection pattern, amplitude versus offset (AVO), Biot–Gassmann fluid substitution and cross-plot models to understand the physics of the reservoir rocks in the field by examining the sensitivity of the basic rock properties; P-wave velocity, S-wave velocity and density, to variation in lithology and fluid types in the pore spaces of reservoirs. This is to ascertain the applicability of quantitative seismic interpretation techniques to explore hydrocarbon prospect in the studied field. The results of reflection pattern and AVO models revealed that the depth of interest is dominated by Class IV AVO sands with a high negative zero offset reflectivity that reduces with offset. The AVO intercept versus gradient plot indicated that both brine and hydrocarbon bearing sands can be discriminated on seismic. Fluid substitution modelling results revealed that the rock properties will favourably respond to variation in oil saturation, but as little as 5% gas presence will result in huge change in the rock properties, which will remain constant upon further increments of gas saturation, thereby making it difficult to differentiate between economical and sub-economical saturations of gas on seismic data. Rock physics cross-plot models revealed separate cluster points typical of shale presence, brine sands and hydrocarbon bearing sands. Thus, the response of the rock properties to the modelling processes adopted favours the application of quantitative interpretation techniques to evaluate hydrocarbon in the field.

Micromechanics-based rock-physics model for inorganic shale

The full set of transversely isotropic elastic stiffness constants of inorganic shale (mudrock with total organic carbon less than 1.5%) can be successfully modeled and, therefore, predicted based on the mineral composition, mineral stiffnesses, clay platelet orientation distribution function, and microgeometry of the pore space. A fundamentally novel concept drawing from the Maxwell homogenization scheme allows a zero-porosity mineral matrix of the mudrock to be expressed as a polycrystal of variable composition and clay mineral alignment. Introduction of the brine-saturated pore space allows us to account for realistic 3D pore types and their combinations as well as elastic interactions, opening the way for better integration of rock physics and geomechanics with modern petrographic investigations and better shale velocity/anisotropy prediction as a function of diagenetic porosity reduction. We were able to calibrate the model using a limited subset of high-quality ultrasonic measurements on shale and constrain main pore geometries such as tetrahedra and irregular spheroids, often reported in modern scanning electron microscopy images. The model is then used to constrain the anisotropy tensor elements of illite-dominated clay, impossible to measure directly, and explore the main compositional and microstructural controls on the anisotropic elasticity of inorganic shale, including the most troublesome [Formula: see text] stiffness and its derivative — the anisotropy parameter [Formula: see text], which is of paramount importance in quantitative seismic interpretation.

Integration of Cluster Analysis and Rock Physics for the Identification of Potential Hydrocarbon Reservoir

Rock physics has proven its credibility for the quantitative seismic interpretation of reservoir and reservoir characterization. In this study, we implemented K-means clustering to group data points more objectively and without prior misconceptions into clusters. Furthermore, we used the grouping suggested by the algorithm for our study. We integrated rock physics and K-means cluster analysis to determine a possible hydrocarbon reservoir using three well logs data. Initially, K-means clustering was implemented on density logs and based on the arithmetic mean of each density log cluster, sandstone and shale-dominant parts were identified and separated. Then, rock physics parameters were computed for the sandstone-dominant part of the well logs. Based on the cross-plots of Lame’s constants with density product, and Lame’s constants ratio, additional lithology discrimination was done for the identification of a clean gas-sand zone. In the clean gas-sand zone, two different pore fluids zones were identified based on Vp/Vs ratio, Poisson ratio, P- and S-wave impedances. Finally, statistical analysis was carried out to observe the underlying frequency distribution of rock physics parameters. The integration of cluster analysis and rock physics gave us significant information about the presence of different fluids and the existence of a potential hydrocarbon reservoir.

3D modelling of flow units and petrophysical properties in brazilian presalt carbonate

Rock typing into flow units (FU) is a well-known technique for characterizing flow heterogeneities in reservoirs and producing reliable estimations of petrophysical properties, as porosity and permeability. Several methods that correlate pore-throat size with permeability and porosity in the core and well-log domains are available in the literature, being the flow zone indicator (FZI) method the most common used in both clastic and carbonate reservoirs. Extrapolating the flow units rock typing from the core and well-log scales into the whole reservoir is a major challenge due to the lack of correlation with sedimentological facies and available data scale differences. Most 3D generations of flow units and petrophysical properties are merely a geostatistical procedure, without any spatial data constraints (like seismic attribute). We propose a new approach to create flow units occurrence probability volumes as well as seismic derived porosities and permeabilities that are more robust than the usual estimate of petrophysical properties considering sedimentological facies. Considering the seismic scale characteristics and using the concept of decametre flow units and quantitative seismic interpretation in a Brazilian presalt dataset, we indirectly quantified superimposed small-scale effects that obliterate/generate porosity on a larger scale and established a minimum number of correlatable FUs with elastic seismic attributes. We produced 3D volumes of permeability and porosity that are still capable of obtaining complex reservoir flow characteristics and could be directly considered as variables in lateral interpolation of reservoir parameters, seismic 4D interpretations and seismic-assisted history matching.

Dual attenuation peaks revealing mesoscopic and microscopic fluid flow in partially oil-saturated Fontainebleau sandstones

SUMMARY We conducted stress–strain oscillation experiments on dry and partially oil-saturated Fontainebleau sandstone samples over the 1–2000 Hz band at different confining pressures to investigate the wave-induced fluid flow (WIFF) at mesoscopic and microscopic scales and their interaction. Three tested rock samples have similar porosity between 6 and 7 per cent and were partially saturated to different degrees with different oils. The measurement results exhibit a single or two attenuation peaks that are affected by the saturation degree, oil viscosity and confining pressure. One peak, exhibited by all samples, shifts to lower frequencies with increasing pressure, and is mainly attributed to grain contact- or microcrack-related squirt flow based on modelling of its characteristics and comparison with other experiment results for sandstones. The other peak is present at smaller frequencies and shifts to higher frequencies as the confining pressure increases, showing an opposite pressure dependence. This contrast is interpreted as the result of fluid flow patterns at different scales. We developed a dual-scale fluid flow model by incorporating the squirt flow effect into the patchy saturation model, which accounts for the interaction of WIFFs at microscopic and mesoscopic scales. This model provides a reasonable interpretation of the measurement results. Our broad-frequency-band measurements give physical evidence of WIFFs co-existing at two different scales, and combining with modelling results, it suggests that the WIFF mechanisms, related to pore microstructure and fluid distribution, interplay with each other and jointly control seismic attenuation and dispersion at reservoir conditions. These observations and modelling results are useful for quantitative seismic interpretation and reservoir characterization, specifically they have potential applications in time-lapse seismic analysis, fluid prediction and reservoir monitoring.

3D broadband seismic inversion: Case study of exploration cretaceous sandstone, North Arafura Basin

This paper presents applications of Absolute and Relative Acoustic Impedance of seismic inversion for qualitative and quantitative seismic interpretation from 3D Broadband PSDM Post-stack Seismic data. A case study from the exploration area of Late Cretaceous – Early Cretaceous Clastics in Arafura Sea, eastern Indonesia has been perform in this work, where inside the study area have no well information for seismic inversion at all. The present work shows the promising contribution of applying 3D Broadband Seismic Velocity for background trend of 3D Post-Stack Model Based Seismic Inversion and perform interpretation from 3D Broadband Relative Acoustic Impedance to reduce the uncertainty associated with the seismic interpretation, to recognize the porous sand section (as an indication of hydrocarbon reservoirs) and to understand the distribution of the reservoir based on seismic data-driven, to later for define the high certainty location of exploration well.

Lithofacies-dependent rock physics templates of an unconventional shale reservoir on the North Slope, Alaska

Over the past 20 years, oil and gas companies have turned their attention to producing petroleum directly from organic-rich shale. Successful exploration, appraisal, and production strategies for source rocks critically depend on reliable identification of their organic components (kerogen, in particular) and generation potential. There is mounting demand to evaluate organic richness in terms of quantity (i.e. total organic carbon) and quality (i.e. hydrogen index) from seismic data, which is usually the only source of information in the early development period of emerging shale plays. We delineated major seismic lithofacies on the Alaska North Slope using elastic, seismic, and petrophysical properties. We performed a well-established quantitative seismic interpretation workflow to integrate geochemical data in the lithofacies definition. Rock physics templates of seismic parameters, Acoustic Impedance, (AI), versus P-wave to S-wave velocity ratio, (VP/VS), are constructed for each lithofacies to assess variations in pore fluid and lithology. We proposed correlations between source rock properties (hydrogen index, total organic carbon) and petrophysical properties (bulk density, porosity, sonic velocity ratio) of the major lithofacies. These correlations, together with facies-specific rock physics templates, can be utilized to predict organic richness and source rock properties away from drilled wells. The models are validated by training data from 2 regional wells to observe their applicability on the Alaska North Slope.

Quantitative Seismic Interpretation Identifies Sweet Spots in Organic-Rich Mudrocks

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197290, “Quantitative Seismic Interpretation Work Flow for Sweet-Spot Identification in Organic-Rich Mudrocks,” by Mustafa A. Al Ibrahim, Tapan Mukerji, SPE, and Allegra Hosford Scheirer, Stanford University, prepared for the 2019 SPE Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed. Estimating the lateral heterogeneity of geochemical properties of organic-rich mudrocks is important for unconventional resource plays. Well-penetration data from emerging plays are sparse and so traditional geostatistical methods will not yield good results. In this study, the authors use a quantitative seismic interpretation work flow (QSI) based on a rock-physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik formation of the Alaskan North Slope. By combining the rock-physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet-spot detection. Introduction Data for physical rock parameters are generally restricted to well locations. To extend data laterally, two general directions can be taken: a data-oriented approach and a physics-oriented approach. The data-oriented approach is powerful but requires a large amount of data commonly not available in emerging areas where well penetration is scarce. Physics-oriented approaches can be used to obtain the desired parameters used in decision-making; however, the results are highly dependent on the inputs, such as TOC and hydrogen index, and these typically are not defined regionally. The QSI is a powerful tool that can be used to estimate the lateral and vertical heterogeneity of rocks beyond wellbores. QSI combines both of the previously mentioned approaches by using well data to calibrate rock-physics models and then using statistical methods to derive relationships between rock-physics models and seismic data. In the complete paper, the authors demonstrate a work flow to apply the QSI to organic-rich mudrocks by incorporating a constructed rock-physics template for mudrocks. Joint estimation of rock properties is used to estimate the geochemical and mechanical properties important in determining sweet spots. Multiple possible realizations of Earth models are generated to capture the nonuniqueness associated with the inversion problem. Study Area The Triassic Shublik source rock is the main petroleum source in the North Slope of Alaska. The organic-rich calcareous interval is interpreted to be deposited during a marine upwelling period. Lateral and vertical heterogeneity in the Shublik formation has been documented with multiple facies identified.

Characterization of a shale-gas reservoir based on a seismic amplitude variation with offset inversion for transverse isotropy with vertical axis of symmetry media and quantitative seismic interpretation

The Lower Silurian shale-gas formation in the south of the Sichuan Basin represents a strong transverse isotropy with vertical axis of symmetry (VTI) feature. Successful characterization of shale-gas formation requires handling the great influence of anisotropy in the seismic wave propagation. Seismic amplitude variation with offset (AVO) inversion for VTI media using PP-waves only is a difficult issue because more than three parameters need to be estimated and such an inverse problem is highly ill posed. We have applied an AVO inversion method for VTI media based on a modified approximation of the PP-wave reflection coefficient. This approximation consists of only three model parameters: the acoustic impedance (attribute [Formula: see text]), shear modulus proportional to the anellipticity parameter (attribute [Formula: see text]), and the approximated horizontal P-wave velocity (attribute [Formula: see text]), which can be well-inverted and have great interpretation capability in shale-gas reservoir characterization. A statistical-rock-physics method was then applied to the inverted attributes for quantitative interpretation of the shale-gas reservoir. A Markov random field is combined with Bayesian rule to improve the continuity and accuracy of the interpretation results. Shales can be successfully discriminated from surrounding formations by using the attribute pair [Formula: see text]-[Formula: see text], and the organic-rich gas-bearing shale can be successfully identified by using the attribute pair [Formula: see text]-[Formula: see text]. Comparison between the prediction results and well logs demonstrates the feasibility of the inversion and quantitative interpretation approaches.

Pore fluid estimation using effective workflow of Poisson impedance analysis

ABSTRACTIn the fields of oil and gas exploration, various quantitative seismic interpretation techniques have been developed to identify lithology and pore fluids based on the rock physics. However, discrimination between oil and brine in pores is often difficult because the seismic response of oil and brine are similar. Poisson impedance (PI) is an effective indicator for distinguishing oil from brine in pores. Target correlation coefficient analysis (TCCA) is widely used to calculate the “c” factor of PI. However, PI analysis using the TCCA method can lead to misinterpretation when the resistivity log is affected by lithology and porosity, as well as the pore fluid type. To overcome this problem, we developed a more effective PI workflow composed of three steps that focus on lithology impedance, porosity impedance, and fluid impedance. In each step, we exclude the unnecessary area for hydrocarbon exploration such as shale, tight, and brine-saturated areas. We applied our method to marine field data that contained shale and tight intervals and demonstrated that the proposed approach can be used to identify potential oil reservoirs more effectively from seismic data.

Quantitative seismic interpretation of rock brittleness based on statistical rock physics

Rock brittleness is one of the important properties for fracability evaluation, and it can be represented by different physical properties. The mineralogy-based brittleness index (BIM) builds a simple relationship between mineralogy and brittleness, but it may be ambiguous for rocks with a complex microstructure; whereas the elastic moduli-based brittleness index (BIE) is applicable in the field, but BIE interpretation needs to be constrained by lithofacies information. We have developed a new workflow for quantitative seismic interpretation of rock brittleness: Lithofacies are defined by a criterion combining BIM and BIE for comprehensive brittleness evaluation; statistical rock-physics methods are applied for quantitative interpretation by using inverted elastic parameters; acoustic impedance and elastic impedance are selected as the optimized pair of attributes for lithofacies classification. To improve the continuity and accuracy of the interpreted results, a Markov random field is applied in the Bayesian rule as the spatial constraint. A 2D synthetic test demonstrates the feasibility of the Bayesian classification with a Markov random field. This new interpretation framework is also applied to a shale reservoir formation from China. Comparison analysis indicates that brittle shale sections can be efficiently discriminated from ductile shale sections and tight sand sections by using the inverted elastic parameters.

Testing popular rock-physics models

In the spirit of classic rock physics, and as an ideal foundation for conventional quantitative interpretation workflows, we consider several popular models relating elastic rock properties to their composition, microstructure, and effective stress on the background of a worldwide log data set, incorporating sands and shales characterized by the maximum dynamic impedance range. We demonstrate that the patchy cement model, ellipsoidal inclusion model, and siliciclastic diagenesis model may be calibrated successfully against the world data set and used in seismic rock property log restoration/editing. We also demonstrate that some of these models present obvious challenges in terms of the information derived from quantitative seismic interpretation. Notably, the key input parameters used in these rock-physics models may show little resemblance to the rock parameters actually observed in geologic studies. Replacing the true rock parameters with the effective ones may do disservice to the science of rock physics in general.

Scale effects of velocity dispersion and attenuation (Q−1) in layered viscoelastic medium

We have used numerical modeling of normal incidence P-waves in periodic and nonperiodic viscoelastic layered media to help improve understanding of the scale effects of heterogeneity on velocity dispersion and attenuation. The improved understanding of these effects facilitates better interpretation and integration of data acquired at different scales, such as seismic data, well-log data, and laboratory measurements. We developed a direct method for estimating velocity and attenuation for viscoelastic media, the viscoelastic Kennett-Frazer (vKF) method, which is an invariant imbedding (reflectivity) method that uses reflection and transmission transfer functions. The vKF method is used to estimate rigorous dispersion and attenuation curves for periodic and nonperiodic cases. The results from our studies validate and quantify the intuitive qualitative understanding that dispersion and attenuation for a layered viscoelastic medium depend on the ratio of wavelength of the waves ([Formula: see text]) and the spatial period of the medium ([Formula: see text]), similar to the elastic case. We also decoupled the total effective attenuation obtained from the viscoelastic case into scattering attenuation that can be modeled from the elastic case and the intrinsic effective attenuation that is present in addition to the scattering attenuation in the viscoelastic case. The calculated intrinsic effective attenuation curves matched the theoretical values at the ray theory and effective medium theory limits. We derived analytical expressions for the long-wavelength limit of velocity and attenuation in a viscoelastic medium. Our expressions can be used directly for the upscaling of well logs to seismic scale considering viscoelastic effects. The expressions indicate the coupling between effective velocity and effective intrinsic attenuation in the long-wavelength limit. Finally, we use the derived expressions to determine the difference between elastic versus viscoelastic upscaling and to highlight the impact on traveltime and amplitude by properly considering viscoelastic information, especially for quantitative seismic interpretation workflows.

Prediction of porosity and gas saturation for deep-buried sandstone reservoirs from seismic data using an improved rock-physics model

In recent years, many important discoveries have been made in global deep oil and gas exploration, which indicates that deep exploration has gradually become one of the most important areas in current and future hydrocarbon exploration. However, the prediction of deep reservoirs is very challenging due to their low porosity and complex pore structure characteristics caused by the burial depth and diagenesis. Rock physics provides a link between the geologic reservoir parameters and seismic elastic properties and has evolved to become a key tool of quantitative seismic interpretation. Based on the mineral component and pore structure analysis of studied rocks, we propose an improved rock-physics model by introducing a third feldspar-related pore for deep-buried sandstone reservoirs to the traditional Xu–White model. This modelling process consists of three steps: first, rock matrix modelling using time-average equations; second, dry rock modelling using a multi-pore analytical approximation; and third, fluid-saturated rock modelling using a patchy distribution. It has been used in total porosity estimation, S-wave velocity prediction and rock-physics template establishment. The applicability of the improved rock-physics model is verified by a theoretical quartz-water model test and a real data total porosity estimation compared with the traditional Xu–White model and the density method. Then, a rock-physics template is generated by the improved rock-physics model for porosity and gas saturation prediction using seismic data. This template is carefully calibrated and validated by well-log data at both the well-log scale and seismic scale. Finally, the feasibility of the established rock-physics template for porosity and gas saturation prediction is validated by a deep-buried sandstone reservoir application in the East China Sea.