Amplitude Variation With Offset Research Articles

Frequency-dependent nonlinear AVO inversion for fluid mobility in viscoelastic media

Reservoir fluid mobility is defined as the ratio of rock permeability to fluid viscosity, which is an important attribute to guide the prediction of high-quality oil and gas reservoirs. Frequency-dependent amplitude variation with offset (AVO) inversion, based on viscoelastic theory, is a useful tool for fluid identification. In conventional pre-stack inversion, linear approximations are usually used to calculate the reflection coefficients, while nonlinear inversions are only occasionally carried out. However, the nonlinear equation for the reflection coefficient, derived from the exact Zoeppritz equation, has higher accuracy and fewer assumptions than the linear approximations. Therefore, we derive a nonlinear frequency-dependent reflection coefficient equation of the PP-wave in terms of P-wave velocity reflectivity, S-wave velocity reflectivity and fluid mobility, based on the relationship among fluid mobility, incident angle, and seismic wave frequency. We consider viscoelastic media and frequency-dependent responses to improve the authenticity of the reflection coefficients. Additionally, we develop a split-step inversion method for fluid mobility based on the Artificial Neural Network Inversion (ANNI) algorithm. Synthetic and field data examples demonstrate that the proposed split-step inversion method outperforms traditional inversion methods that rely on approximate equations for predicting underground reservoirs, improving the stability of fluid mobility parameter inversion.

Automatic Conditioning of Marine Seismic Angle Stack Data With a Convolutional Neural Network

Misalignment of reflections is a common problem in migrated prestack seismic data, which occurs due to moveout correction with incomplete knowledge of the velocity model. This issue persists in partial angle stack data often used for amplitude versus offset (AVO) analyses, and is traditionally treated with residual moveout and trim static corrections. However, these methods require time-consuming velocity selection and parameter tuning, and commonly lead to spurious alignment of reflections with noise. Therefore, we train a convolutional neural network (CNN) on synthetic examples to automatically perform conditioning and alignment of angle stack data. First, two-dimensional synthetic common depth point (CDP) gathers are created using a convolutional modeling method, and then made into input-target pairs where the inputs are poorly moveout-corrected (using inaccurate normal moveout velocities) and the targets are accurately moveout-corrected. These input-target examples are split into angle stacks for the near, mid, and far offsets, and then used to train a CNN to transform the misaligned angle stacks into their more-aligned counterparts. In testing, the trained network increases the alignment of unseen synthetic data, improving the correlation with the target far offset trace from 0.76 to 0.85 on average. The network is applied on two field examples from offshore Norway with Class 3 and Class 2P AVO responses, respectively. In both cases, the angle stack data predicted by the network shows improved alignment, greater resolution in the far offset stacked section, and increased correspondence to a well-tie synthetic seismogram, all while retaining the AVO responses. The CNN predictions are compared to angle stack data following traditional conditioning (residual moveout and trim static corrections), with the results being of similar or greater quality. This method enables high quality conditioning of angle stack data at a fraction of the time required for conventional methods, and is easily adaptable to different datasets.

A new expression for fluid factor using AVO intercept and gradient: Theory and application on gas sand reservoirs from offshore Australia

Numerous amplitude variation with offset (AVO) fluid indicators have proven to be sensitive to the presence of hydrocarbons. Mathematically, these fluid indicators measure the deviation of seismic responses of hydrocarbon reservoirs from their background in a specific domain. We develop a new expression for the fluid factor commonly used in AVO analysis and interpretation. The expression is a function of the common AVO intercept and gradient, along with a weighting coefficient that suppresses the contribution of lithology and other factors unrelated to fluid content. The coefficient also assists in calibrating the fluid factor to the local geology. We compare the performance of our fluid factor with existing fluid factors. All the fluid factors are tested on a worldwide collection of P- and S-wave velocity and density measurements. The testing is followed by application on synthetic data before applying the attribute on real data from the Northwest Shelf of Australia. In the latter application on the real data, we use two wells for calibration purposes, and the third as a blind well. Our fluid factor demonstrated high capability in detecting the targeted gas zones at the three well locations and identifying new prospective zones.

Poisson’s ratio-LambdaRho rock-physics templates and a study on sensitivity of different fluid indicators

Seismic fluid indicators are highly accurate when calibrated, but when the data is scarce, an interpreter must choose between different indicators. We investigate the data from multiple basins to understand the most effective fluid indicators for use. Unlike previous studies, the data comprises a large, equal, and statistically meaningful number of hydrocarbon and brine sands from different basins. We show that Poisson’s ratio-LambdaRho templates can be used to separate different facies in synthetic and real data and can be used as a useful crossplot interpretation tool. We develop a fluid indicator, as LambdaRho Poisson’s ratio multiplication [Formula: see text], which is highly sensitive to saturation and can be potentially used as a fizz-gas discriminator. We compare the sensitivity of different fluid indicators to hydrocarbon saturation by using Bhattacharyya distance, which provides a quantitative metric for how well different indicators separate hydrocarbon and brine sands, and it is a nonparametric measure unlike other fluid indicator scores developed in similar studies. Absolute properties derived from seismic by inversion are rarely available in regional studies, whereas relative elastic properties can be easily obtained and used. Following the concepts of elastic reflectivity vectors and geometry of intercept-gradient crossplots, we show how the reflectivity of different fluid indicators can be approximated from amplitude variation with offset (AVO) parameters at various chi ([Formula: see text]) angles, enabling an interpreter to use them even when inversion products are not available. Finally, we compare the effectiveness of relative elastic parameters on different AVO classes and show that no single attribute works best across all classes but for general screening purposes fluid factor and [Formula: see text] can be good choices. The findings of this study can help better characterization of fluids in exploration, appraisal, and development of hydrocarbons, and in other areas where monitoring produced and injected fluids is important like 4D seismic or carbon capture storage.

Characterizing hydrocarbon discoveries and prospects in the Tay Sandstone using relative elastic inversion: Greater Pilot area, Central North Sea

The Pilot oil accumulation and associated discoveries of Blocks 21/27 and 21/28 have been stranded assets for three decades. The principal cause of delay in developing the oil fields is because these are heavy oils with low gas–oil ratio and, in some cases, significant gas caps. The oil in the Tay Sandstone is trapped in structural and stratigraphic closures at a depth of 800–1300 m. Determining pay thickness and delimiting oil-in-place in some existing discoveries and especially in undrilled prospects has been problematic. This difficulty has been addressed by conducting an innovative amplitude versus offset (AVO) inversion of the seismic data which has been successfully blind tested against the wells and prognosed closures. The method, which is a band-limited form of extended elastic impedance (EEI), has proved robust for discriminating gas-, oil- and brine-filled sands from one another. Oil and gas trapped further down-dip in the discoveries Fyne, Crinan and Dandy are developed in Tay reservoirs showing generally lower net-to-gross. The sensitivity of the rEEI attribute to the presence of thicker intervals of oil pay, in this case, is used to indicate where higher net-to-gross is present above the oil–water contact, a characteristic of direct relevance to the planning of optimally located development wells.

Seismic shear wave noise suppression and application to well tie

Abstract Seismic shear waves have been employed in oil and gas exploration for decades. A 2D seismic shear wave inline was acquired in Sanhu area, located in the Qaidam Basin in western China. Although the acquired shear wave data exhibits high resolution and comparable bandwidth to the compressional wave, it is contaminated by various types of noise, including linear noise, single-frequency noise, and especially internal multiples. Internal multiples seriously compromise the primary reflections at both near-offset and far-offset and are difficult to be suppressed. This paper presents a case study of noise attenuation for the seismic shear wave. Firstly, single-frequency noise and linear noise are attenuated through filtering methods. Then, two methods (the Radon transform and the frequency-wavenumber (F-K) filtering) are evaluated for their effectiveness in multiple suppression and amplitude preservation. The results indicate that both methods successfully reduce long-period multiples at far-offset, enhancing the signal-to-noise ratio. We show that F-K filtering retains the characteristic ‘strong-weak-strong’ amplitude variation in the SV-SV wave data gather, making it preferable for subsequent AVO (amplitude variation with offset) analysis and inversion. Finally, a wave-equation-based MSI (multiple suppression inversion) method is used to suppress near-offset internal multiples. This involves iteratively predicting internal multiples and adaptively subtracting them from the original data. Stacked sections of different offsets are compared to demonstrate de-multiple result, and the result is also validated by the improvement of well calibration with the seismic shear wave data.

Intelligent seismic AVO inversion method for brittleness index of shale oil reservoirs

The brittleness index (BI) is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development. Seismic amplitude variation with offset (AVO) inversion is commonly used to obtain the BI. Traditionally, velocity, density, and other parameters are firstly inverted, and the BI is then calculated, which often leads to accumulated errors. Moreover, due to the limited of well-log data in field work areas, AVO inversion typically faces the challenge of limited information, resulting in not high accuracy of BI derived by existing AVO inversion methods. To address these issues, we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients. Based on this, an intelligent AVO inversion method, which combines the advantages of traditional and intelligent approaches, for directly obtaining the BI is proposed. A TransU-net model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI. By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples, the challenge of limited samples is effectively addressed, and the direct inversion of the BI is stably achieved. Tests on model data and applications on field data demonstrate the feasibility, advancement, and practicality of the proposed method.

Reflection, transmission, and AVO response of inhomogeneous plane waves in thermoporoelastic media with two-temperature equations of heat conduction

We develop a modified fluid-saturated thermoporoelastic model by introducing two temperature equations to account for the temperature differences between the solid skeleton and the pore filling. The modified two-temperature generalized thermoporoelastic (TTG) equation is an extension of the classical single-temperature Lord-Shulman (LS), Green-Lindsay, and generalized LS theories. It predicts four compressional waves and one shear wave based on the analysis of inhomogeneous plane waves. We study the exact reflection and transmission (R/T) coefficients at the interface separating two thermoporoelastic half-spaces and develop an amplitude-variation-with-offset (AVO) approximation. Comparison with the Biot poroelastic case of the water/oil contact shows that the TTG model reproduces the exact R/T results. The AVO response of oil, gas, and real CO2 geosequestration reservoirs illustrates the practical applicability of our model and provides the theoretical basis for the exploration of high-temperature resources.

Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas

The Arbuckle Group in southern Kansas has been investigated for carbon geosequestration-related studies. In this study, we evaluated the seismic-scale petrophysically defined facies variability of the Arbuckle Group at the Wellington Field, Kansas, using quantitative seismic interpretation and a supervised Random Forest classification approach. We first defined three petrophysics-based rock types (petrofacies) from core-derived porosity and permeability measurements using the flow-zone indicator approach. Then, using the artificial neural network, we classified these petrofacies in noncored intervals. We observed that petrofacies 1 corresponds to medium and coarse-grained dolomitic packstone, wackestone, and dolomitic breccia with up to 8% porosity and D-scale permeability values. Whereas petrofacies 2 and 3 correspond to argillaceous and fine-grained micritic dolomites and dolomitic mudstones with lower permeability values for a given porosity, with respect to petrofacies 1. Using the common reflection-point gathers, we performed prestack seismic inversion and calculated various amplitude-variation-with-offset (AVO) attribute volumes. We used these elastic properties and AVO attribute volumes as input for estimating supervised seismic-scale 3D petrofacies and petrofacies probability volumes using the Random Forest algorithm. Results reveal the complex distribution of the petrofacies in the candidate injection and baffle zones in the study area, where petrofacies 1 is mainly prevalent within the lower and upper portions of the Arbuckle group, whereas petrofacies 2 and 3 are mainly present in the middle Arbuckle interval. The workflow we present through this study provides the spatial variability of facies distribution that is reflective of the actual lithology and petrophysical properties of the Arbuckle group in the study area with limited well control.

Anisotropy in shale and its impact on AVO modelling and prospect de-risk: a case study of a Central North Sea field

The inherent anisotropy in shale is an attribute associated with its clay minerals platelets alignment and layering and very common with transversely isotropic (TI) medium. This implies that velocity is fastest along the horizontal axis and slowest along the vertical axis, which could affect velocity signature from sonic tools measurements in deviated wells. This may lead to wrong amplitude variation with offset (AVO) signature, inaccurate depth conversions, seismic-to-well tie difficulty etc. Nineteen (19) wells in Forties field andthirteen (13) in Huntington field were investigated and interpreted for the presence of anisotropy effect within the caprock over the reservoir Forties sands of the Sele (shales) Formation. In order to analyse the Thomsen weak anisotropy parameters (ϵ, γ and δ) per field, wells with closer Sele depth range were considered. The well inclinations ( θ ), average compressional (α) and shear (β) velocities per well were utilised for forward and inverse modelling of Thomsen anisotropy equations. The estimated anisotropy (VTI) parameters and the P-wave ( α o ) and S-wave velocities (βo ) at zero inclinations in Sele Formation for the studied wells are: α o = 2448 ± 15.9 m/s, β o = 1003 ± 34.8 m/s, ϵ = 0.15 ± 0.02, γ = 0.33 ± 0.10, δ = 0.030 ± 0.015 in the Forties field Sele, while α o = 2775 ± 23.6 m/s, β o = 1306 ± 47.9 m/s, ϵ = 0.22 ± 0.02, γ = 0.43 ± 0.14, δ = 0.022 ± 0.018 in the Huntington field. AVO responses were compared, assuming both isotropic and anisotropic P–P reflectivity models. Result shows an increase in AVO gradient for all anisotropic case in both fields. AVO signatures were either completely or averagely masked in both fields using the isotropic assumptions. There is also an increase in AVO gradient for all anisotropic cases in both fields. Velocity decreases of about 1.2% and 3.5% were observed in the Sele Formation for the Forties and Huntington fields respectively for anisotropy. The corrected two-way traveltime (TWT) should in principle allow for a better seismic-to-well tie estimation. Similarly, using the faster deviated velocity (i.e. uncorrected velocity) in any depth conversion would result in under estimation of the actual depth across the field.

GeosPIn: A MATLAB library for geostatistical petrophysical inversion

The petrophysical inversion of seismic data is one of the key components of seismic reservoir characterization. The goal of petrophysical inversion is to estimate the petrophysical properties of reservoir rocks, such as porosity, volumes of minerals or lithologies, and water and hydrocarbon saturations, from seismic data. This process can be performed by combining seismic amplitude-variation-with-offset (AVO) modeling and rock-physics relations with deterministic or probabilistic inverse theory methods, either in a two-step approach based on seismic AVO inversion and rock-physics inverse mapping or in a single-loop step that includes both. We present a comprehensive MATLAB library named Geostatistical Petrophysical Inversion. The novelty of the library lies in a single-loop geostatistical inversion method based on probability field simulation and ensemble smoother multiple data assimilation, as well as its implementation for applications to 3D data sets. The library also includes a Bayesian petrophysical inversion based on the two-step approach for comparison. We illustrate the main algorithms, explain the structure of the source codes, and demonstrate the library applications through 1D and 3D examples.

Bayesian joint residual moveout and amplitude-variation-with-offset inversion

Residual moveout (RMO) can have a severe impact on seismic amplitude-variation-with-offset (AVO) analysis. It is, therefore, common practice to quality control and correct the processed seismic data for RMO before AVO analysis. However, a complicating factor is that AVO effects and tuning may result in up- or down-dipping events that are easily mistaken for events with RMO, e.g., AVO Class 2p response. Flattening these events will lead to an incorrect AVO estimation. We present a new Bayesian joint RMO and AVO inversion to estimate the RMO time shifts and the AVO intercept and gradient. The joint inversion corrects the seismic data based on the RMO and AVO prior models rather than explicitly assuming that the data should be flattened. The prior models will typically guide toward flat gathers but will also allow for up- and down-dipping events when these are possible within the prior model. The method is illustrated on synthetic and real seismic data. For cases wherein flat events are correct, the results are similar to sequential methods with RMO correction before AVO analysis, but in situations wherein dipping seismic reflectors may be misinterpreted as events with RMO, the joint inversion provides better results by evaluating RMO and AVO simultaneously. The inversion results include the posterior covariance matrix, which represents uncertainties for the AVO intercept and gradient, the RMO time shifts, and the correlations between these at all samples. The uncertainty of the RMO time shift varies within seismic gathers and depends on the seismic quality and how clear and well determined the seismic events are. The uncertainty of the RMO is lower for clear and continuous events but increases in the zones with weaker and noisy events. The uncertainty of the RMO time shifts has a low impact on the uncertainty of the AVO intercept but increases the uncertainty of the AVO gradient significantly.

Stress-dependent reflection and transmission of elastic waves under confining, uniaxial, and pure shear prestresses

Insights into the reflection and transmission (R/T) of waves at a prestressed interface are important in geophysical applications, such as evaluating the angle-dependent elastic properties for monitoring geopressure and tectonic stress using sonic logging data or seismic data. Although many studies deal with wave propagation in prestressed media, the angle-dependent R/T of waves at an interface subject to different prestress loading modes remains largely unaddressed. We address this issue by applying the theory of acoustoelasticity with third-order acoustoelastic constants to study the R/T coefficients at the interface between two prestressed media. Stress-induced elastic deformations are assumed to be locally homogeneous without boundary dislocations caused by stress concentration so that the static boundary conditions can be applied. We consider three typical prestress modes (confining, uniaxial, and pure shear), each of which takes into account the incidence of upgoing and downgoing P and S waves. The Knott equations under different types of prestresses are derived, followed by the estimation of angle-dependent R/T coefficients. The energy conservation at the interface and the acoustoelastic finite-difference simulation of predicted P and S modes verify the correctness of the angle-dependent R/T coefficients under confining prestress. Comparisons with the elastic case (prestress [Formula: see text]) indicate the important influence of prestresses on the energy distribution of reflected and transmitted waves, including stress-dependent critical angles, converted waves, and R/T energy ratios. Such acoustoelastic effects mainly occur around/after the critical angle. For small-angle incidence, prestresses mainly affect the gradient of R/T coefficients. The type and magnitude of prestress are closely related to the angle-dependent R/T coefficients and must be considered for amplitude-variation-with-offset analysis in prestressed media.

Seismic data AVO analysis in time frequency domain using synchroextracting transform

Time-frequency analysis is a crucial tool for processing and interpreting seismic data in hydrocarbon exploration. Accurately determining the temporal and spectral characteristics is essential for applications such as reservoir characterization and fluid identification. High-resolution representation of seismic data time-frequency properties improves reliability in these applications. Recent advancements in post-processing techniques aim to enhance conventional methods. The synchroextracting transform (SET) is an innovative approach that combines a post-processing procedure with the short-time Fourier transform. It retains time-frequency information associated with spectral components while eliminating excessive energy dispersed in the time-frequency domain. This study evaluates the effectiveness of SET for seismic data time-frequency analysis, comparing it with contemporary post-processing techniques using synthetic signals and field seismic data. Results show that SET provides higher resolution for distinct signal components and effectively reduces energy dispersion. SET exhibits advantages such as robustness against random noise, reasonable computational cost, and offering more interpretable time-frequency information regarding attenuated amplitudes. Building on synthetic experiments, we demonstrate the capability of SET to generate high-resolution single frequency sections of seismic data, facilitating accurate analysis of amplitude variation with offset (AVO) in the time-frequency domain.

Adaptive fault enhancement in OVT domain based on anisotropy theory

As a result of the combined influence of multi-stage and multi-directional tectonic activities, the subsurface medium in complex structural areas can easily develop intricate fault systems with varying scales and directions. Post-stack seismic and azimuth-stack attributes have limited precision in recognizing such complex fault systems. In this paper, we propose an adaptive fault enhancement workflow in offset vector tile (OVT) domain based on anisotropy theory to improve the recognition ability of seismic data for multi-directional faults and minor faults. The vertical directions of fault strikes and tectonic strikes estimated by the gradient structure tensor (GST) can be used as sensitive observation azimuths of faults. At the same time, according to the anisotropic forward modeling and real data amplitude variation with offset (AVO) analysis of minor faults in marine shale of Wufeng-Longmaxi formations in Sichuan Basin, China, the incident angles with the largest difference between isotropic and anisotropic reflection coefficients can be used as sensitive incident angles of faults. A new fault enhancement seismic data can be reconstructed from the OVT gathers by traversing the sensitive azimuths and incident angles adaptively. The 3D model and real data indicate that the faults enhanced by the workflow we proposed are clearer in shape, smaller in scale, and richer in details, which is consistent with geology and well.

Three-Dimensional Amplitude versus Offset Analysis for Gas Hydrate Identification at Woolsey Mound: Gulf of Mexico

The Gulf of Mexico Hydrates Research Consortium selected the Mississippi Canyon Lease Block 118 (MC118) as a multi-sensor, multi-discipline seafloor observatory for gas hydrate research with geochemical, geophysical, and biological methods. Woolsey Mound is a one-kilometer diameter hydrate complex where gas hydrates outcrop at the sea floor. The hydrate mound is connected to an underlying salt diapir through a network of shallow crestal faults. This research aims to identify the base of the hydrate stability zone without regionally extensive bottom simulating reflectors (BSRs). This study analyzes two collocated 3D seismic datasets collected four years apart. To identify the base of the hydrate stability zone in the absence of BSRs, shallow discontinuous bright spots were targeted. These bright spots may mark the base of the hydrate stability field in the study area. These bright spots are hypothesized to produce an amplitude versus offset (AVO) response due to the trapping of free gas beneath the gas hydrate. AVO analyses were conducted on pre-stacked 3D volume and decreasing amplitude values with an increasing offset, i.e., Class 4 AVO anomalies were observed. A comparison of a time-lapse analysis and the AVO analysis was conducted to investigate the changes in the strength of the AVO curve over time. The changes in the strength are correlated with the decrease in hydrate concentrations over time.

Amplitude Variation with Offset (AVO) Inversion for Reservoir Visualization: A Case Study of Taje Field, Niger Delta, Nigeria

Amplitude Variation with Offset (AVO) inversion analysis was performed on pre-stack seismic data and well information gathered from the shallow offshore area of the Niger Delta. This analysis aimed to improve reservoir visualization and employed the Hampson Russell Geoview, AVO, and STRATA software tools. The seismic data were provided in Seg-Y format, covering an in-line range from 4503 to 5569, an x line range from 1434 to 2026, and an angle of incidence range of 0 to 45°. The study centered on the Taje well_026. Within the subsurface, the authors identified five distinct reservoirs, labeled A to E, located at various depths ranging from 3057.50 to 3115.00 m, 3115.00 to 3157.50 m, 3157.50 to 3190.00 m, 3190.00 to 3200.00 m, and 3200.00 to 3239.00 m, respectively. These reservoirs exhibited different fluid compositions. Reservoir A, primarily composed of sandstone, contained brine, whereas Reservoirs B and D, dominated by shale, contained gas. On the other hand, Reservoirs C and E, both comprised of sandstone, held oil. Reservoir C is distinguished by its clean sandstone unit. The inversion results revealed that both Reservoirs C and E consisted of low impedance sand layers surrounded by higher impedance shale layers. The gas migrated from the reservoir and was trapped within the shale units due to deformation of the lithological units, likely induced by stress accumulation. This migration process was facilitated by the shale’s inability to undergo smearing, possibly as a result of faulting mechanisms.

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

Bayesian rock-physics inversion using a localized ensemble-based approach — With an application to the Alvheim field

We describe, implement, and show the results of a localized ensemble-based approach for seismic amplitude-variation-with-offset (AVO) inversion with uncertainty quantification. Ensembles are simulated from prior probability distributions for fluid saturations and clay content. Starting with continuous saturations and clay content variables, we use depth-varying models for cementation and grain contact theory, Gassmann fluid substitution with mixed saturations, and approximations to the Zoeppritz equations for the AVO attributes at the top-reservoir. The local conditioning to seismic AVO observations relies on (1) the misfit between ensemble simulated seismic AVO data and the field observations in a local partition of the grid/local patch, of inlines/crosslines around the locations where we aim to predict, (2) correlations between the simulated reservoir properties and the data in local patches, and (3) local assessment to avoid unrealistic updates based on spurious correlations in the ensembles. Data from the Alvheim field in the North Sea are used to demonstrate the approach. The influence of the prior information from the well logs in combination with the seismic reflection data indicates the presence of higher oil and gas saturation in the lobe structures of the field and increased clay content at their edges.

Second-Order Approximate Reflection Coefficient of Thin Interbeds with Vertical Fractures

The horizontal fractures in the strata will close in the compaction effect of overlying strata, while the vertical cracks are widely developed, which can be equivalent to HTI (transverse isotropy with a horizontal axis of symmetry) medium. When an S-wave propagates into HTI media, the shear wave will divide into two types of waves: a fast S-wave and slow S-wave. When the strata of HTI are thin and overlapping, called the thin interbeds model, the wave field exhibits complex primary reflections, converted waves, and multiples. We introduce a new second-order approximation of the total reflection coefficient, with the incidence angle lower than the critical angle in thin-interbed HTI media using a recursive algorithm. We verify the effectiveness of the second-order approximation by analyzing the energy of multiples. Comparing the second-order approximate solution that degenerates the HTI medium into isotropic and Kennett’s exact solution, we find that our solution has an accuracy of over 99.9% in any azimuth, with the incidence angle lower than the critical angle under P-wave incidence. However, our solution of the SP wave field is suitable for incidence azimuth angles between 0–75° and 120–180°, with the lowest accuracy occurring at an incidence angle of 25° and a relative error of 6.4%. The approximate solution in the SS wave field has the same applicable range as the SP wave, with the maximum error of 6.3% occurring at the incident angle of 1°. This new second-order approximate formula for the total reflection coefficient of thin interbeds composed of HTI helps us to understand the reflection characteristics of complex thin interbeds. It also lays a theoretical foundation for the development of AVO (Amplitude Versus Offset) analysis and inversion techniques for lithological and stratigraphic oil and gas reservoirs.