Amplitude Variation With Offset Analysis Research Articles

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.

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.

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.

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.

Quantifying amplitude-variation-with-offset uncertainties related to calcite-cemented beds using a Monte Carlo simulation

Calcite cement often occurs locally, forming thin layers of calcite-cemented sandstone characterized by high seismic velocities and densities. Because of their strong impedance contrast with the surrounding rock, calcite-cemented intervals produce detectable seismic reflection signals that may interfere with target reflections at the top of a reservoir. In this case, the amplitude-variation-with-offset (AVO) of the effective seismic signature will be altered and may even create a false hydrocarbon indication. From the Monte Carlo simulation, we find that the presence of thin calcite-cemented beds increases the uncertainty of the Bayesian pore-fluid classification based on the AVO attributes intercept and gradient. In the case example of a North Sea turbiditic oil and gas field, the probability of a false-positive hydrocarbon indication increases from 3%–5% to 18%–21% assuming an equal probability of the occurrence of brine, oil, and gas. The results confirm that calcite-cemented beds can create a pitfall in AVO analysis. Realistic estimates of the AVO uncertainty are crucial for the risk assessment of well placement decisions.

Quantitative evaluation of gas hydrate reservoir by AVO attributes analysis based on the Brekhovskikh equation

AVO (Amplitude variation with offset) technology is widely used in gas hydrate research. BSR (Bottom simulating reflector), caused by the huge difference in wave impedance between the hydrate reservoir and the underlying free gas reservoir, is the bottom boundary mark of the hydrate reservoir. Analyzing the AVO attributes of BSR can evaluate hydrate reservoirs. However, the Zoeppritz equation which is the theoretical basis of conventional AVO technology has inherent problems: the Zoeppritz equation does not consider the influence of thin layer thickness on reflection coefficients; the approximation of the Zoeppritz equation assumes that the difference of wave impedance between the two sides of the interface is small. These assumptions are not consistent with the occurrence characteristics of natural gas hydrate. The Brekhovskikh equation, which is more suitable for thin-layer reflection coefficient calculation, is used as the theoretical basis for AVO analysis. The reflection coefficients calculated by the Brekhovskikh equation are complex numbers with phase angles. Therefore, attributes of the reflection coefficient and its phase angle changing with offset are used to analyze the hydrate reservoir's porosity, saturation, and thickness. Finally, the random forest algorithm is used to predict the reservoir porosity, hydrate saturation, and thickness of the hydrate reservoir. In the synthetic data, the inversion results based on the four attributes of the Brekhovskikh equation are better than the conventional inversion results based on the two attributes of Zoeppritz, and the thickness can be accurately predicted. The proposed method also achieves good results in the application of Blake Ridge data. According to the method proposed in this paper, the hydrate reservoir in the area has a high porosity (more than 50%), and a medium saturation (between 10% and 20%). The thickness is mainly between 200m and 300m. It is consistent with the previous results obtained by velocity analysis.

First-order approximate analytical expressions of oblique incident elastic wave at an interface of porous media saturated with a non-viscous fluid

The analysis technology of Amplitude Variation with Offset (AVO) is one of the important methods for oil and gas reservoir prediction. Zoeppritz equation and its approximations are the theoretical basis of AVO analysis, which assumes that the upper and lower media of a horizontal interface are single-phase media. Limited by this assumption, AVO analysis has limited prediction and identification accuracy for complex porous reservoirs. In view of this, the first-order approximate analytical expressions of oblique elastic wave at an interface of porous media are derived. Firstly, the incident and scattering characteristics of various waves at the interface of porous media are analyzed, and the displacement vectors generated by these elastic waves are described by exponential function. Secondly, the kinematic and dynamic boundary conditions at the interface of porous media are discussed. Thirdly, by substituting the displacement vectors of incident and scattered waves into boundary conditions, the exact analytical equation is derived. Then, considering the symmetry of scattering matrix in the equation, the exact analytical expressions of each scattered wave are obtained. Furthermore, under the assumptions of small incident angle, weak elasticity at an interface of porous media, and ignoring the second- and higher-order terms, the first-order approximate analytical expressions are derived. Establishing a model of sandstone porous media with different porosity in upper and lower media, the correctness of the approximate analytical expressions is verified, and the elastic wave response characteristics of lithology and pore fluids are analyzed.

GPR antennas geometry and its impact over detection of near-surface water contamination through AVO Data analysis at laboratory site

Polarization is an important technique for ground penetrating radar (GPR) surveys, particularly when determining the shape, orientation, size, and precise location of any buried material. A linearly-polarized incident GPR wave experiences polarization change upon scattering from the host medium, which highly depends upon the technique and antenna orientation used for the acquisition of the GPR data. This paper focuses on the polarization of GPR data acquired at a laboratory test site. We kept the GPR antenna orientation and geometry in mind to understand how they impact the results while detecting subsurface contamination. Because of its direct indication ability, amplitude variation with offset (AVO) has been widely used to detect subsurface contamination; however, a technique to evaluate the distribution of subsurface contamination is not yet available; thus, in this study, we will also discuss the distribution of contamination using polarization of GPR data. Two major phenomena have been studied: the polarity of the data acquired and slice view with and without contamination in the testing tank, as well as trace, frequency, and amplitude plot during data analysis to determine the difference of plot with and without contamination. Our findings suggest that using the polarization geometry technique to obtain an accurate view of the subsurface contamination will not only improve the GPR AVO analysis but will also provide a clear distribution of contamination in the subsurface.

Research on Prediction Method of Volcanic Rock Shear Wave Velocity Based on Improved Xu–White Model

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs.

Seismic characterization of source rocks: Why should it be assessed on a site-by-site basis?

Seismic characterization of source rocks (SRs) became widely used in exploration risk assessment, partly driven by the evaluation of petroleum systems conditioned by the presence of organic-rich SRs. Generally, rock physics combined with seismic amplitude variation with offset (AVO) analysis and inversion of seismic data are used to detect SR presence and to assess SR lateral and vertical variations measured in total organic carbon (TOC) content (in weight percent [wt%]). Despite its great potential, this method suffers from a range of pitfalls and uncertainties. In this study, based on several data sets, we highlight the variability of seismic responses of SRs. From well data, rock property studies of SRs show that the relation between acoustic impedance, which is the product of density and P-wave velocity, and TOC turns out not to be representative in SRs with TOC contents less than approximately 4–5 wt%. In the screening phase of rock-physics data, SR also reveals a large range of Poisson's ratio values, which relates to P-wave and S-wave velocities and has a direct impact on AVO. Moreover, from real seismic data, AVO analysis gave support for this complex behavior, highlighting AVO class I, III, and IV anomalies. Therefore, the expectation that the top of SR intervals would feature a “clear dimming with offset” (AVO class IV) should not be generalized for SR identification, especially in frontier areas lacking nearby well calibration, as suggested by the results of this study.

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.

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.

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.

Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response

The velocity of near-surface materials is one of the most important for Ground-Penetrating Radar (GPR). In the study, we evaluate the options for determining the GPR velocity to measure the accuracy of velocity approximations from the acquired GPR data at an experimental site in Hangzhou, China. A vertical profile of interval velocities can be estimated from each common mid-point (CMP) gather using velocity spectrum analysis. Firstly, GPR data are acquired and analyzed using the popular method of hyperbola fitting which generated surprisingly high subsurface signal velocity estimates while, for the same profile, the Amplitude variation with offset (AVO) analysis of the GPR data (using the same hyperbola fitting method) generate a more reasonable subsurface signal velocity estimate. Several necessary processing steps are applied both for CMP and AVO analysis. Furthermore, experimental analysis is conducted on the same test site to get velocities of samples based on dielectric constant measurement during the drilling process. Synthetic velocities generated by AVO analysis are validated by the experimental velocities which confirmed the suitability of velocity interpretations.

Identification of gas hydrates and bottom-simulating reflectors in far-offset seismic images

The presence of marine gas hydrates is routinely inferred based on the identification of bottom simulating reflectors (BSRs) in common-depth-point (CDP) seismic images. Additional seismic studies such as amplitude variation with offset (AVO) analysis can be applied for corroboration. Although confirmation is needed by drilling and sampling, seismic analysis has proven to be a cost-effective approach to identify the presence of marine gas hydrates. Single-channel far-offset seismic images are investigated for what appears to be a more reliable and cost-effective indicator for the presence of BSRs than traditional CDP processing or AVO analysis. A nontraditional approach for processing seismic data is taken to be more relevant to imaging the gas/gas hydrate contact. Instead of applying the traditional CDP seismic processing workflows from the oil industry, we more carefully review the significant amount of information existing in the data to explore how the character of the data changes as offset angle increases. Three cases from different environments are selected for detailed analysis. These include (1) stratigraphy running parallel with the ocean bottom, (2) a potential BSR, running parallel to the ocean bottom, and cross-cutting dipping reflections, and (3) a suspected thermal intrusion without a recognizable BSR. This investigation considers recently collected multichannel seismic data from the deep waters of the central Aleutian Basin beneath the Bering Sea, the preprocessing of the data sets, and the methodology for processing and display to generate single-channel seismic images. Descriptions are provided for the single-channel near- and far-offset seismic images for the example cases. Results indicate that BSRs related to marine gas hydrates, and originating due to the presence of free gas, are more easily and uniquely identifiable from single-channel displays of far-offset seismic images than from traditional CDP displays.

Machine learning derived AVO analysis on marine 3D seismic data over gas reservoirs near South Korea

Amplitude variation with offset (AVO) analysis has become a crucial step in determining gas well locations. However, large amounts of time and effort are required to confirm AVO anomalies, and interpretations can be inconsistent. To avoid the need for the long processes involved in conventional manual analysis, we developed an automatic AVO analysis method for common midpoint (CMP) gathers through using machine learning (ML) with a convolutional neural network (CNN). To deal with complicated seismic data, the network was constructed based on VGG16 network architecture, which includes 16 layers. The resulting CNN-based algorithm was applied to two sets of three-dimensional (3D) seismic data acquired off the east coast of South Korea. One dataset, which was obtained over gas reservoirs and confirmed to show multiple AVO class III anomalies by comparison with geophysical well logging data, was used for training and evaluation of the proposed CNN-based algorithm. The resulting trained model was tested using the second dataset, which was obtained over an area near the gas reservoirs with a different depositional environment. To demonstrate the applicability of the model to raw and final migrated CMP gathers, AVO class III anomalies predicted by the ML-derived analysis were confirmed by manual AVO analysis of the test data.

AVO Analysis Combined With Teager–Kaiser Energy Methods for Hydrocarbon Detection

An improved amplitude variation with offset (AVO) attribute analysis method combined with the Teager–Kaiser energy methods for hydrocarbon detection is proposed in this letter. It has a stronger ability to reveal the subtle amplitude anomaly changes caused by hydrocarbons than the traditional AVO analysis method. The Teager–Kaiser energy operator (TKEO) algorithm is used for enhancing the hydrocarbons’ characteristics of the prestack gathers. The cross Teager–Kaiser energy operator (CTKEO) is further employed to efficiently highlight the AVO feature of the gathers by calculating the energy interaction between the first trace and the other traces. Finally, the intercept and gradient product (PG) parameters are obtained through AVO analysis for hydrocarbon detection. The model test and the field data applications show that the proposed method can effectively target the gas reservoirs. This letter presents a complementary approach to current AVO analysis methods.

Prediction of abnormal pressure from AVO velocities over “Safety” field, onshore Niger delta, Nigeria

Abnormal pressure prediction was undertaken in ‘‘Safety’’ field, onshore Niger Delta, Nigeria using amplitude variation with offset (AVO) velocities information. Each of the methods used form an integral part of a process that produces AVO Analysis, AVO velocity inversion, extraction of seismic velocity from AVO velocities inversion results and pore pressure prediction. Pore pressure predicted from the seismic velocity has a better resolution than the pressure predicted from the interval transit time. The pore pressure within the field ranges from 14.7psi to 3916psi. Overpressured and underpressured zones were delineated on the field from the pressure predicted from the seismic velocities. Within the field, the over-pressured zones were delineated at depth 6855 ft–7802 ft. Over pressure top was delineated at a depth of 6855 ft with a pore pressure of 3053psi and a corresponding hydrostatic pressure of 2722psi. The under-pressured zones were also delineated at depth 7883 ft–9288 ft. The under pressure top was delineated at a depth of 7883 ft with a pressure of 1093psi and a corresponding hydrostatic pressure of 3122psi. Porosity values within the over pressure zone ranges from 23% to 53% which could be considered as relatively high. This could be as a result of the fact that the pore fluid cannot be expelled rapidly thereby causing the pore fluid to increase rapidly since they are no longer compacted; thus leading to overpressure. As a result of overpressure top which is directly above the reservoir top within the shale zone, drilling this reservoir vertically could not be suggested so as to avoid possible blow out. It was also observed that the primary cause or mechanism of overpressure within this field could be disequilibrium compaction.

Amplitude variation with offset inversion using acoustic-elastic local solver

Conventional amplitude variation with offset (AVO) inversion analysis uses the Zoeppritz equations, which are based on a plane-wave approximation. However, because real seismic data are created by point sources, wave reflections are better modeled by spherical waves than by plane waves. Indeed, spherical reflection coefficients deviate from planar reflection coefficients near the critical and postcritical angles, which implies that the Zoeppritz equations are not applicable for angles close to critical reflection in AVO analysis. Elastic finite-difference simulations provide a solution to the limitations of the Zoeppritz approximation because they can handle near- and postcritical reflections. We have used a coupled acoustic-elastic local solver that approximates the wavefield with high accuracy within a locally perturbed elastic subdomain of the acoustic full domain. Using this acoustic-elastic local solver, the local wavefield generation and inversion are much faster than performing a full-domain elastic inversion. We use this technique to model wavefields and to demonstrate that the amplitude from within the local domain can be used as a constraint in the inversion to recover elastic material properties. Then, we focus on understanding how much the amplitude and phase contribute to the reconstruction accuracy of the elastic material parameters ([Formula: see text], [Formula: see text], and [Formula: see text]). Our results suggest that the combination of amplitude and phase in the inversion helps with the convergence. Finally, we analyze elastic parameter trade-offs in AVO inversion, from which we find that to recover accurate P-wave velocities we should invert for [Formula: see text] and [Formula: see text] simultaneously with fixed density.

A physical modelling study of the reservoir scale effect on amplitude variation with incidence angle

Abstract The 3D pre-stack seismic data from a physical modeling experiment were employed to investigate the effect of reservoir scales on AVO (amplitude variation with offset or incidence angle). Eight cuboid samples simulating cavernous reservoirs with different widths and the same thickness and elastic parameters were set within a 3D model. 3D seismic data acquisition and processing were conducted. To get the AVO responses of the samples with different widths, trough amplitudes corresponding to the sample tops at different incidence angles were extracted from the pre-stack angle gathers. Amplitude calibration for transducer radiation patterns was conducted on the extracted amplitudes at different angles. AVO analysis was conducted to quantitatively demonstrate the effect of sample scales on AVO characteristics. The effect of sample width was weak when the width was less than 60 m, which was 3/5 of the wavelength. When the width was larger than 60 m, both AVO intercept and gradient gradually increased with the sample width. The AVO gradient peaked at 150 m, which was 1.5 times the wavelength. Cross-plot analysis of AVO intercept and gradient showed the samples were aligned in a straight line when the sample width was less than twice the seismic wavelength. The result in this study partially verified the conclusions of reservoir scale effect on AVO responses drawn from previous numerical modelling studies. For a heterogeneous rectangular reservoir, the effect of reservoir scales on AVO responses could potentially be used to quantitatively estimate reservoir scale.