Amplitude Variation With Offset Research Articles

Quantitative seismic characterization for gas hydrate- and free gas-bearing sediments in the Shenhu area, South China sea

During China's first gas hydrate drilling expedition, a gas hydrate-bearing layer (GHBL) of ≈25 m thickness was identified above a bottom-simulating reflection (BSR) at site SH2 in the Shenhu area, South China Sea. This study considers the GHBL and the underlying free gas-bearing layer (FGBL) as a whole and evaluates them using quantitative seismic characterization. The reprocessed seismic data, correlated to well log data, reveal the BSR to be accompanied by newly-identified reflections of positive polarity from the top of the GHBL and the base of the FGBL. Of note are phase reversals along individual reflections that cross the top of the GHBL and the base of the FGBL. Phase reversals at the top of the GHBL are a previously little known seismic feature, and seismic forward modeling shows them to provide a direct indicator for the presence of gas hydrate. Amplitude versus offset (AVO) analysis indicates that the two new reflections have characteristics distinct from those of the BSR. Gas hydrate and free gas saturations derived by AVO analysis and seismic impedance inversion, respectively, are consistent with values estimated from sonic velocities, chloride anomalies and pressure cores. The saturations calculated from acoustic impedance inversion show that both gas hydrate and free gas of high saturation are discontinuous, and that free gas has a clear stratified distribution. The distribution of gas hydrate is generally consistent with that of free gas below the BSR, indicating that gas hydrate near site SH2 is mainly controlled by free gas in the underlying FGBL. The quantitative interpretation presented in this study provides a new and reliable method for the characterization of gas hydrate-bearing sediments in areas with no wells, and for the pre-drilling assessment of free gas hazard.

Reflection and transmission coefficients of spherical waves at an interface separating two dissimilar viscoelastic solids

SUMMARY Spherical wave reflection and transmission (R/T) coefficients at an interface are not only of theoretical significance but also play an important role in the amplitude variation with offset (AVO) analysis of wide-angle reflection seismic data and cross-borehole surveys. For sources close to the interface the resulting wavefields cannot be adequately described in terms of a single incident plane wave. Rather, the spherical waves must be viewed as the superposition of an infinite number of plane waves. Moreover, the R/T coefficients for each individual plane wave in viscoelastic media have proven to be more complicated than expected due to the difficulty in selecting the correct vertical slowness. In such attenuating media the R/T coefficients cannot be properly determined by simply replacing the real elastic parameters with their complex viscoelastic counterparts. In this study, the reflection and transmission coefficients of spherical waves at a plane interface separating two dissimilar viscoelastic solids are rigorously investigated. The difficulty in selecting the vertical slowness is shown to be circumvented if the spherical wavefields are calculated from the plane wavefields using the Sommerfeld integral appropriate for the dissipative materials. However, some resulting phase curves of the complex spherical wave R/T coefficients tend to be of opposite sign to the corresponding phase curves of plane waves due to non-uniqueness of the latter for post-critical wave incidence. In this contribution we propose a new definition of spherical wave R/T coefficients for viscoelastic media which differs from the conventional one. Its advantages are that it is not explicitly expressed as a function of the R/T angles, it is valid for both P and S waves, yet it is consistent with the existing definitions of spherical wave R/T coefficients but is more robust. By way of examples we compute both spherical wave reflection coefficients (SWRC) and spherical wave transmission coefficients (SWTC) for two different viscoelastic models. Unlike plane waves, both the SWRC and the SWTC of converted PS waves are found to be non-zero at vertical incidence and may be drastically affected by the existence of longitudinal PS waves which are confirmed by full waveform calculations for the converted PS waves.

Using laboratory data to understand how pore aspect ratio influences elastic parameters and amplitude variation with offset

Pore geometry is an important parameter in reservoir characterization that affects the permeability of reservoirs and can also be a controlling factor on the impact of pressure and saturation on reservoirs elastic properties. We have used selective laser sintering 3D printing technology to build physical models to experimentally investigate the impacts of pore aspect ratio on compressional (P-) and shear wave (S-wave) velocities and amplitude variation with offset (AVO). We printed six models to study the effects of the pore aspect ratio of prolate and oblate pore structures on elastic properties and AVO signatures. We found that the P-wave velocity is reduced by decreasing the pore aspect ratio (flatter pore structure), whereas the S-wave velocity is less sensitive to the pore aspect ratio. This effect is reduced when the samples are water saturated. We developed new experimental and processing techniques to extract realistic AVO signatures from our experimental data and found that the pore aspect ratio has similar effects on AVO as fluid compressibility. This indicated that not considering the pore aspect ratio in AVO analysis can lead to misleading interpretations. We also found that these effects are reduced in water-saturated samples.

Seismic Work Flow Characterizes Deep, Thin Formation Layers Offshore South China Sea

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30164, “Deep and Thin Overpressure Reservoir Characterization of Paleogene Wenchang Formation in the Enping 17 Sag of the South China Sea,” by Jun Liu and Yingwei Li, CNOOC, and Donghai Liang, Schlumberger, et al. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. The complete paper describes an integrated work flow for 3D seismic reservoir characterization of offshore deep and thin layers in the Eocene Wenchang formation (WCF) without sufficient well data. The work flow includes seismic data reprocessing, well-log-based rock physics analysis, seismic structure interpretation, 3D simultaneous amplitude variation with offset (AVO) inversion, 3D lithology prediction, and geological integrated analysis. Formation Development and Objectives The Pearl River Mouth basin (PRMB) on the northern continental margin of the South China Sea has undergone multistage extensional rifts. The Enping Sag lies in the Zhu1 depression, north of the PRMB, and consists of three subsags. Enping 17 is the largest and deepest sag, with an area of 550 km2, and the thickness of Eocene sediments is greater than 3000 m. The deep middle Eocene WCF, with gray lacustrine mudstones, is hydrocarbon-rich and is the major source rock in the area. A wildcat well had penetrated the WCF at an area with a vertical thickness of 380 m. During drilling, the mud weight was increased to 1.46 g/cm3 from 4570 m total vertical depth (TVD) downward because of increasing pore pressure and higher background gas abnormalities in the WCF. A high pore-pressure gradient of 14.5 KPa/m was measured by drillstem test (DST) at a depth of 4641.5 m TVD. Under the protection of the high pore pressure, the high-quality reservoir layer from 4642 to 4651 m TVD was encountered with 12% average porosity, 3.6-md permeability, and 47.1% saturation. Although a hydrocarbon sample was not obtained during the downhole test because of technical restrictions of DST in 1985, gas surge and a small amount of gas and condensate oil were observed after the test instruments were pulled to surface. With the subsequent development of field techniques, it was likely that current drilling and DST techniques could achieve productive oil flow in this layer.

AVO analysis for high amplitude anomalies using 2D pre-stack seismic data

Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the controlling parameter on the AVO analysis. AVO cross plots from the real pre-stack seismic data reveal AVO class IV (showing a negative intercept decreasing with offset). This result matches our modelled result of fluid substitution for the seismic synthetics. It is concluded that fluid substitution is the controlling parameter on the AVO analysis and therefore, the high amplitude anomaly on the seabed and the target horizon 9 is the result of changing the fluid content and the lithology along the target horizons. While changing the porosity has little effect on the amplitude variation with offset within the AVO cross plot. Finally, results from the wedge models show that a small change of thickness causes a change in the amplitude; however, this change in thickness gives a different AVO characteristic and a mismatch with the AVO result of the real 2D pre-stack seismic data. Therefore, a constant thin layer with changing fluids is more likely to be the cause of the high amplitude anomalies.

Hierarchical Bayesian Probabilistic Seismic AVO Inversion Using Gibbs Sampling With IA2RMS Algorithm

From the observed seismic data, the estimation of elastic parameters and lithofacies of subsurface layers with seismic amplitude variation with offset (AVO) inversion plays an important role in reservoir characterization. The sequential estimation of elastic parameters and lithofacies in AVO inversion usually affect the prediction accuracies of model parameters without considering the prior information related to lithofacies. We propose a novel probabilistic AVO inversion approach in hierarchical Bayesian framework that combines a hierarchical iterative strategy with Gibbs-IA2RMS sampling, to estimate elastic parameters and lithofacies simultaneously. From the exact PP-wave reflection coefficient, we first derive the posterior distribution of model parameters involving a Bayesian hyper-parameter of lithofacies through the hierarchical prior probability distributions and the likelihood distribution of observed data. Based on Monte Carlo Markov chain approximation, we then introduce an iterative Gibbs-IA2RMS sampling to achieve the high sampling stability of the high-dimensional posterior PDF in AVO inversion. The algorithm realizes the adaptive construction of proposal distribution, and has a higher acceptance rate than the generic Metropolis-Hastings algorithm. The numerical results show that the approach effectively simulates the posterior distributions and inversion uncertainties of model parameters, and improves the computational efficiency of probabilistic AVO inversion. The applicability and validity of the proposed approach are also demonstrated with a field data application.

Uncertainty estimation in AVO inversion using Bayesian dropout based deep learning

Amplitude versus offset (AVO) inversion is the process of transforming seismic reflection into elastic properties such as P- and S- impedance to estimate the interval properties and thickness of underlying geology using well log and post- or prestack seismic data. Recent applications of AVO inversion based on deep learning have shown excellent results and practical applicability. However, traditional deep learning methods yield only prediction results without any associated predictive uncertainty. Two types of predictive uncertainty should be considered: aleatoric uncertainty, which occurs when noisy data are included; and epistemic uncertainty, which is caused by a lack of data. To estimate the impedances and their uncertainties, Bayesian approximation using Monte Carlo dropout is applied, which simply approximates a Bayesian neural network. From the proposed method, we can not only predict impedances but also estimate their predictive uncertainties in the seismic survey area and determine whether prediction results are reliable.

Uncertainty Quantification in Classifying Complex Geological Facies Using Bayesian Deep Learning

Understanding geological differences in a proved reservoir requires precise facies classification. Predicting facies from seismic data is frequently seen as an inverse uncertainty quantification problem in seismic reservoir characterization. Typically, the uncertainty in the model parameters that regulate the geographic distributions is being ignored. The target facies and its uncertainty can be determined by calculating the posterior distribution of a Bayesian inverse problem conditioned by the seismic data, in which the model parameters are inferred from the observed seismic data using a Bayesian inference framework. It is believed that such facies classification model has a unique set of model parameters that best fits it. The proposed work is unique in that it quantifies the epistemic uncertainty of the predicted facies in blind well conditioned by Seismic Amplitude Versus Offset (AVO-Seismic) attributes in the Bayesian inference framework. Under this framework, parameter uncertainties of the neural net. weights and biases are calculated using their posterior distributions from the ensamble models generated by Marcov-Chains Monte-Carlo (MCMC) by assuming that the prior values of the weights and biases are uninformative. The proposed approach is also demonstrated on Synthetic Amplitude Versus Offset (AVO-Synthetic) dataset (derived from the well log information) and we have found high relevance in the predicted results. For comparision, a plain Deep Learning and Deep learning with Monte Carlo Dropout are employed and the results indicate that our model performs more efficiently comparing to the others indicating the possibility of the model to be used in real world solution to adequate facies classication.

Bayesian Seismic AVO Inversion Using a Laterally Coupled Multimodal Prior Model

A Bayesian seismic amplitude versus offset (AVO) inversion scheme with a laterally coupled prior model for porosity, water saturation, and volume of clay is proposed. A 2-D section and a 3-D volume of an oil reservoir are studied. The oil reservoir is considered at the initial state, which entails gravitationally induced bimodality in the water saturations along vertical traces. A selection Gaussian random field (S-GRF) prior model, capable of representing this bimodality, is specified for porosity, water saturation, and volume of clay. The S-GRF is specified to have lateral correlation, which may reduce the impact of trace-unique signal errors in the seismic AVO data on the inversion results. The likelihood model is linear and Gaussian, for which the S-GRF prior model is conjugate; hence, the posterior model is also an S-GRF. The form of the posterior distribution is therefore known and its parameter values can be analytically computed. Real seismic AVO data from the 2-D section and the 3-D volume are inverted and the results appear to be reliable along validation wells and represent a geologically plausible reservoir design. Furthermore, a notable variance reduction in the laterally coupled posterior model relative to an alternative posterior model without lateral coupling is achieved.

Hybrid Inversion of Reservoir Parameters Based on Cosimulation and the Gradual Deformation Method

A new hybrid inversion methodology for petrophysical properties (porosity, water saturation, and volume of clay) based on partially stacked seismic angle gathers was presented. The innovation of this method is that it combines the advantages of high efficiency of analytical and semianalytical multistep petrophysical estimation with the advantages of spatial continuity and high resolution of geostatistical direct inversion of reservoir parameters. A trace-by-trace linearized amplitude versus offset (AVO) inversion and semianalytical petrophysical estimation approach could efficiently achieve a preliminary estimation of reservoir parameters under some assumptions and approximations. These laterally unconstrained preliminary estimation results are able to provide effective information for finer-resolution geostatistical simulation and improve the computational efficiency of stochastic optimization. The 3-D fast Fourier transform moving average (FFT-MA) geostatistical cosimulation and the stochastic optimization by gradual deformation method (GDM) improve the resolution and spatial continuity of the preliminary estimation results. At the same time, stochastic inversion overcomes the assumptions in the analytical inversion algorithm that the parameters need to conform to the logarithmic Gaussian distribution and the linear approximation of the Zoeppritz equation. The method was tested on a synthetic case and a real case. The results show that compared with the analytical and semianalytical methods, this hybrid inversion can obtain results with finer resolution and better spatial continuity, and the computational efficiency is significantly improved compared with the geostatistical direct inversion of reservoir parameters. Uncertainty evaluation can be obtained by performing statistical analysis on the multiple realizations of stochastic inversions.

Model-based approach to improve class 1 AVO attributes

We propose a model-based Amplitude Variation with Offset (AVO) approach to address the assumption of weak elastic contrast in the linearized approximations of Aki-Richard equation. Existing approximations, especially for the Class 1 AVO response, which indicates the positive elastic contrast, deviate significantly from the Zoeppritz equation in presence of large elastic contrast. Results of the proposed approach show that it can minimize the deviation from the Zoeppritz equation, improve AVO attributes, and is capable of providing the desired attribute for characterizing a reservoir. The method starts with two matrices. One matrix is of the simulated rock properties termed as the rock-property-matrix and the other of the Zoeppritz AVO responses for those rock properties termed as the response-matrix. A model-based AVO equation or the basis-function-matrix is computed utilizing the rock-property-matrix and the response-matrix. The inverse of the basis-function-matrix is applied to the real data to get the AVO attributes from this approach. The conventional AVO (Aki-Richards) and model-based AVO attributes are compared in a class 1 AVO environment from the offshore east coast of India. The curvature attribute based on model-based AVO shows significant improvement. The synthetic-seismic correlation of the curvature attribute improves from −0.03 to 0.9 while synthetic-seismic correlation of the gradient attribute improves from 0.5 to 0.77. The improved AVO attribute volumes add significant values in the reservoir characterization.

Elastic depth trends for siliciclastic sequences

Elastic depth trends are an integral component of reservoir characterization whether explicitly or implicitly constructed. Applications to seismic data include migration velocity model building, pore-pressure prediction, inversion, and amplitude-variation-with-offset (AVO) analysis. In practice, workflows often involve fitting well logs with smooth curves, without reference to known lithology-dependent templates that could provide realistic constraints. We have constructed porosity-depth trends for shales and sandstones with reference to reliable measurements that span mechanical compaction and chemical diagenesis domains. These porosity trends are then used as inputs to well-established rock-physics models to estimate the median and bounding trends for acoustic and shear impedance. The resulting modeling has shown an important sensitivity to the pore-pressure regime (notably in shales) and the onset of cementation (in sands), among other variables. AVO predictions made from these trends provide a useful reference frame for anomaly prediction with depth, which is critical in frontier exploration.

A successful qualitative and quantitative integrated interpretation of Lower Miocene wells and seismic data in Salina del Istmo Basin, Mexico

The study area is located in middepth to deep waters of the Salina del Istmo Basin where Repsol operates Block 29. The objective of this work is to integrate qualitative and quantitative interpretations of rock and seismic data to predict lithology and fluid of the Early Miocene prospects. The seismic expression of those prospects differs from age-equivalent well-studied analog fields in the U.S. Gulf of Mexico Basin due to the mineralogically complex composition of abundant extrusive volcanic material. Offset well data (i.e., core, logs, and cuttings) were used to discriminate lithology types and to quantify mineralogy. This analysis served as input for developing a new rock-physics framework and performing amplitude variation with offset (AVO) modeling. The results indicate that the combination of intercept and gradient makes it possible to discriminate hydrocarbon-filled (AVO class II and III) versus nonhydrocarbon-filled rocks (AVO class 0 and IV). Different lithologies within hydrocarbon-bearing reservoirs cannot be discriminated as the gradient remains negative for all rock types. However, AVO analysis allows discrimination of three different reservoir rock types in water-bearing cases (AVO class 0, I, and IV). These conclusions were obtained during studies conducted in 2018–2019 and were used in prospect evaluation to select drilling locations leading to two wildcat discoveries, the Polok and Chinwol prospects, drilled in Block 29 in 2020.

Gas hydrate accumulation in shelf break setting: Example from the Qiongdongnan Basin in the northern slope of the South China Sea

Based on interpretation and inversion of high‐resolution seismic data, a previously undocumented occurrence of pore‐filled gas hydrates that are associated with a submarine canyon system was discovered in the shelf break of the north‐east Qiongdongnan Basin (QDNB) in the northern South China Sea (SCS). Continuous bottom simulating reflections (BSRs) were recognized in the submarine striking NW‐SE ridges along the shelf break. The two‐way travel times (TWTs) of the BSRs are about 200–250 ms below the sea floor and the burial depths are mapped at ~180–225 mbsf after time‐depth conversion. The distribution of the enhanced reflections (ERs) and acoustic blanking zone overlying the BSRs indicate possible pore‐filled gas hydrate enrichment. ERs have been commonly observed immediately below the BSRs, indicating the accumulation of free gas, which exhibits distinct amplitude versus offset (AVO) anomalies. The acoustic impedance inversion results show that the gas hydrate saturation is 20–50%, and the thickness of the gas hydrate‐bearing layers is usually ~10–40 m, with a maximum thickness of up to ~90 m in local regions. Large‐scale listric faults originating from the deep sag connect the hydrocarbon source region and the shallow gas hydrate stability zone (GHSZ) and act as migration pathways for biogenic gas and potentially thermogenic gas. In addition, hydrocarbons may also be transported into the GHSZ along the sidewalls of the canyon. Canyon incision is believed to cause the increase in the burial depth of the BSRs on the flank of the ridge toward the base of the channel and thereby to trap the hydrocarbons migrating along the sidewall in permeable beds due to the decrease in permeability caused by the gas hydrate accumulation, resulting in the occurrence of ERs immediately below the BSRs. It is speculated that in the channel‐levee system, the coarse‐grained sediments should be deposited within the GHSZ, and the reservoir's physical properties are conducive to the wide distribution and enrichment of pore‐filled gas hydrates in the shelf break of the northern QDNB.

Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multitrace Bayesian amplitude-variation-with-offset petrophysics inversion in multiporosity reservoirs

The estimation of petrophysical and fluid-filling properties of subsurface reservoirs from seismic data is a crucial component of reservoir characterization. Seismic amplitude-variation-with-offset (AVO) inversion driven by rock physics is an effective approach to characterize reservoir properties. In general, PP-wave reflection coefficients, elastic moduli, and petrophysical parameters are nonlinearly coupled, especially in multiple-type pore-space reservoirs, which makes seismic AVO petrophysics inversion ill-posed. We have developed a new approach that combines Biot-Gassmann’s poroelasticity theory with Russell’s linear AVO approximation, to estimate the reservoir properties including elastic moduli and petrophysical parameters based on multitrace probabilistic AVO inversion algorithm. We first derive a novel PP-wave reflection coefficient formulation in terms of porosity, stiff-pore volume fraction, rock-matrix shear modulus, and fluid bulk modulus to incorporate the effect of pore structures on elastic moduli by considering the soft and stiff pores with different aspect ratios in sandstone reservoirs. Through the analysis of the four types of PP-wave reflection coefficients, the approximation accuracy and inversion feasibility of the derived formulation are verified. Our stochastic inversion method aims to predict the posterior probability density function in a Bayesian setting according to a prior Laplace distribution with vertical correlation and prior Gaussian distribution with lateral correlation of model parameters. A Metropolis-Hastings stochastic sampling algorithm with multiple Markov chains is developed to simulate the posterior models of porosity, stiff-pore volume fraction, rock-matrix shear modulus, and fluid bulk modulus from seismic AVO gathers. The applicability and validity of our inversion method is illustrated with synthetic examples and a real data application.

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

Low porosity-permeability structures and microcracks, where gas is produced, are the main characteristics of tight sandstone gas reservoirs in the Sichuan Basin, China. In this work, an analysis of amplitude variation with offset (AVO) is performed. Based on the experimental and log data, sensitivity analysis is performed to sort out the rock physics attributes sensitive to microcrack and total porosities. The Biot–Rayleigh poroelasticity theory describes the complexity of the rock and yields the seismic properties, such as Poisson’s ratio and P-wave impedance, which are used to build rock-physics templates calibrated with ultrasonic data at varying effective pressures. The templates are then applied to seismic data of the Xujiahe formation to estimate the total and microcrack porosities, indicating that the results are consistent with actual gas production reports.

Lithology, porosity and saturation joint prediction using stochastic rock physics modelling and litho-petro-elastic inversion

In this paper we present the application of a single-loop litho-petro-elastic (LPE) inversion, which is a data assimilation algorithm that uses nonlinear Zoeppritz reflectivity operators with sequential filtering. It integrates rock physics models with seismic amplitude variation with offset (AVO) inversion and Bayesian inversion to define lithology, elastic, and petrophysical properties in a single loop, thus, combining several steps of the conventional reservoir characterization workflow. In the conventional multistep approach, the lithology and petrophysical properties are generated sequentially from elastic properties after AVO inversion is performed, which can add prediction uncertainty at each step and produce results that are not correlated with each other. The LPE inversion ensures that the predicted properties maintain the relationships defined by the rock physics model. The LPE inversion was tested on data from offshore Australia with three wells, for a faulted reservoir zone containing oil with a gas cap. It provided robust predictions for lithology, porosity, and water saturation, which matched acceptably at the three wells. The algorithm also accounted for subsurface uncertainties as it produced prediction probabilities of facies, porosity, and water saturation using multidimensional probability density functions. This approach can be effectively used to classify reservoir properties in a single-loop workflow.

Amplitude-variation-with-offset inversion based on group sparse regularization

Seismic amplitude-variation-with-offset (AVO) inversion from prestack seismic data plays a significant role in estimating elastic parameters and characterizing reservoir properties. In general, sparse regularization is widely used to solve ill-posed inverse problems by reducing the solution space of subsurface parameters, which makes seismic AVO inversion more stable. However, the traditional sparse constraint inversion only focuses on the vector sparsity of reflectivity, instead of the structural sparse characteristics of the estimated parameters. Consequently, various elastic parameters demonstrate different formation structural features in the same location of stratum. In this study, we have developed a novel approach that combines the structural sparsity and the vector sparsity of the model reflectivity to establish the posterior probability density distribution and solve the objective function of the model parameters. Based on the relationship among multiple elastic parameters, we divide the model parameters to be inverted into several groups according to intrinsic structural sparse characteristics of elastic parameters. In this case, all of the model parameters at the same sampling point are classified into the identical group, which ensures that different estimated parameters indicate the same characteristic in terms of stratigraphic structure. From the perspective of Bayesian inference, we use the modified Cauchy probability density function (PDF) to characterize the group sparsity and describe the relationship among model parameters in the same group by Gaussian PDF. Furthermore, we estimate the optimum solution corresponding to the maximum a posteriori probability under Bayesian inference. Synthetic experiments on a Marmousi model prove that the estimated P-velocity, S-velocity, and density are consistent with those of the real models, and the application of field data confirms the availability and feasibility of group sparse inversion.

Numerical modeling of seismic responses from fractured reservoirs in 4D monitoring — Part 1: Seismic responses from fractured reservoirs in carbonate and shale formations

One of the most urgent problems of oil and gas reservoir monitoring is the assessment of fractured reservoir infill type — with fluid-filled, gas-filled, or closed (no-reservoir situation) fractures, which is of significant value for time-lapse seismic technology. We used the grid-characteristic method for numerical modeling of seismic responses from fractured periodic elastoacoustic structures. We consider every single fracture individually (without using the effective medium approach), and we set explicit boundary conditions on fracture surfaces. We assume realistic height-to-thickness ratios — fracture opening (aperture) — equaling three to five orders of magnitude. These techniques make our models as close to real fractured reservoirs as possible. Analyzing the simulated seismic responses, we solve the problem of assessing the fractured reservoir infill type. As a result, previously unknown properties of seismic responses from fractured reservoirs were revealed. We use amplitude variation with offset (AVO) as the main tool for the analysis of the fracture infill type effect on the seismic response in three frequency ranges. Three out of four models exhibit a stable positive AVO gradient regardless of the rock type and frequency range. The analysis of linearized Zoeppritz equations confirms such AVO behavior. We have developed quantitative criteria (indicators) for recognition of a fracture infill type. Amplitude-frequency analysis is shown to expand the capabilities of the infill type recognition. Thus, a method for determining the fractured reservoir infill type is established for carbonate and shale formations, which could become the basis for a new direction in time-lapse technology.

Efficient iterative prestack least-squares reverse time migration through surface-offset gather compression

Extended least-squares inversion is superior to stack-based least-squares inversion for imaging the subsurface because it can better account for amplitude-variation-with-offset (AVO) effects as well as residual moveout (RMO) effects induced by erroneous velocity models. Surface-offset extensions have proven to be a robust alternative to angle gathers as well as subsurface extensions when applied to narrow-azimuth data acquisitions, especially when using erroneous velocity models. As such, least-squares reverse time migration (LSRTM) applied to surface-offset gathers (SOGs) obtains accurate surface-offset-dependent estimates of the reflectivity with better AVO behavior, while respecting the curvatures of the events in the gathers. Nevertheless, the computational expense incurred by SOG demigration generally renders this process unfeasible in many practical situations. We have exploited a compression scheme for SOGs that captures AVO and some RMO effects to improve efficiency of extended LSRTM. The decompression operator commutes with the demigration operator; therefore, gathers compressed in the model domain may be decompressed in the data domain. This obviates the need to demigrate all SOGs, requiring only the demigration of a few compressed gathers. We determine the accuracy of this compression in the model and data domains with a synthetic 2D data set. We then use our model-compression/data-decompression scheme to SOG-extended iterative LSRTM for two field data examples from offshore Brazil. These examples demonstrate that our compression can capture most AVO and some RMO information accurately, while greatly improving efficiency in many practical scenarios.