AVO Inversion Research Articles

Model-data-driven AVO inversion method based on multiple objective functions

Model-data-driven AVO inversion method based on multiple objective functions

Effective pressure prediction from 4D seismic AVO data during CO2-EOR and storage

The use of time-lapse seismic difference analysis methods, time-lapse AVO forward and inversion technology to study and distinguish the changes in saturation and pressure caused by CO2 injection into reservoirs is one of the difficult issues in the current technology research for monitoring CO2 geological storage. Based on the time-lapse seismic AVO inversion method proposed by Landrø, this paper distinguishes the fluid saturation and pressure effect after CO2 injection. By establishing a petrophysical model with simultaneous changes in CO2 saturation and pressure, and performing an AVO forward and inversion simulation of two-layer media, we compare the accuracy of Landrø’s simplified formula with Smith and Gidlow's AVO simplified equation that before Landrø simplified in inverting the saturation differences (ΔS) and pressure differences (ΔP), and conclude that Smith and Gidlow's simplified formula has better accuracy. And then we get the relationship between the saturation differences (ΔS), pressure differences (ΔP) and the time-lapse AVO intercept differences (ΔR0), gradient differences (ΔG). The relationship is further applied to the actual time-lapse seismic AVO inversion to quantitatively analyze the saturation and pressure changes after CO2 injection. The effective pressure difference value obtained by inversion has a good match with the measured wellhead pressure difference value, which can well explain the pressure sweep range and the pressure difference change caused by CO2 injection in the reservoir, which provides a basis for monitoring the underground occurrence state and storage safety of CO2.

Prediction of sweet spots in tight sandstone reservoirs based on anisotropic frequency-dependent AVO inversion

Abstract The frequency-dependent amplitude-versus-offset (FAVO) method has become a practical method for fluid detection in sand reservoirs. At present, most FAVO inversions are based on the assumption that reservoirs are isotropy, but the application effect is not satisfactory for fractured reservoirs. Hence, we analyse the frequency variation characteristics of anisotropy parameters in tight sandstone reservoirs based on a new petrophysical model, and propose a stepwise anisotropic FAVO inversion method to extract frequency-dependent attributes from prestack seismic field data. First, we combine the improved Brie's law with the fine-fracture model to analyse frequency-dependent characteristics of velocities and Thomsen anisotropy parameters at different gas saturations and fracture densities. Then, we derive an anisotropic FAVO inversion algorithm based on Rüger's approximation formula and propose a stepwise anisotropic FAVO inversion method to obtain the dispersions of anisotropy parameters. Finally, we propose a method that combines the inversion spectral decomposition with the stepwise anisotropy FAVO inversion and apply it to tight sand reservoirs in the Xinchang area. We use P-wave velocity dispersion and anisotropy parameter ε dispersion to optimise favourable areas. Numerical analysis results show that velocity dispersion of the P-wave is sensitive to fracture density, which can be used for fracture prediction in fractured reservoirs. In contrast, anisotropic parameter dispersion is sensitive to gas saturation and can be used for fluid detection. The seismic data inversion results show that velocity dispersion of the P-wave and anisotropic parameter dispersion are sensitive to fractured reservoirs in the second member of Xujiahe Group, which is consistent with logging interpretation results.

Fluid identification and effective fracture prediction based on frequency-dependent AVOAz inversion for fractured reservoirs

Fluid and effective fracture identification in reservoirs is a crucial part of reservoir prediction. The frequency-dependent AVO inversion algorithms have proven to be effective for identifying fluid through its dispersion property. However, the conventional frequency-dependent AVO inversion algorithms based on Smith & Gidlow and Aki & Richards approximations do not consider the acquisition azimuth of seismic data and neglect the effect of seismic anisotropic dispersion in the actual medium. The aligned fractures in the subsurface medium induce anisotropy. The seismic anisotropy should be considered while accounting for the seismic dispersion properties through fluid-saturated fractured reservoirs. Anisotropy in such reservoirs is frequency-related due to wave-induced fluid-flow (WIFF) between interconnected fractures and pores. It can be used to identify fluid and effective fractures (fluid-saturated) by using azimuthal seismic data via anisotropic dispersion properties. In this paper, based on Rüger's equation, we derived an analytical expression in the frequency domain for the frequency-dependent AVOAz inversion in terms of fracture orientation, dispersion gradient of isotropic background rock, anisotropic dispersion gradient, and the dispersion at a normal incident angle. The frequency-dependent AVOAz equation utilizes azimuthal seismic data and considers the effect of both isotropic and anisotropic dispersion. Reassigned Gabor Transform (RGT) is used to achieve high-resolution frequency division data. We then propose the frequency-dependent AVOAz inversion method to identify fluid and characterize effective fractures in fractured porous reservoirs. Through application to high-qualified seismic data of dolomite and carbonate reservoirs, the results show that the method is useful for identifying fluid and effective fractures in fluid-saturated fractured rocks.

Fluid discrimination incorporating viscoelasticity and frequency-dependent amplitude variation with offsets inversion

Frequency-dependent amplitude versus offset (FAVO) inversion is a popular method to estimate the frequency-dependent elastic parameters by using amplitude and frequency information of pre-stack seismic data to guide fluid identification. Current frequency-dependent AVO inversion methods are mainly based on elastic theory without the consideration of the viscoelasticity of oil/gas. A fluid discrimination approach is proposed in this study by incorporating the viscoelasticity and relevant FAVO inversion. Based on viscoelastic and rock physics theories, a frequency-dependent viscoelastic solid-liquid decoupling fluid factor is initially constructed, and its sensitivity in fluid discrimination is compared with other conventional fluid factors. Furthermore, a novel reflectivity equation is derived in terms of the viscoelastic solid-liquid decoupling fluid factor. Due to the introduction of viscoelastic theory, the proposed reflectivity is related to frequency, which is more widely applicable than the traditional elastic reflectivity equation directly derived from the elastic reflectivity equation on frequency. Finally, a pragmatic frequency-dependent inversion method is introduced to verify the feasibility of the equation for frequency-dependent viscoelastic solid-liquid decoupling fluid factor prediction. Synthetic and field data examples demonstrate the feasibility and stability of the proposed approach in fluid discrimination.

Anisotropic frequency-dependent characteristics of PP- and PS-waves in partially saturated double-porosity rocks

AbstractMost frequency-dependent AVO inversions are currently based on an approximate equation derived using an isotropic medium. However, actual reservoirs usually show anisotropy, such as shale reservoirs, tight sandstone reservoirs and fractured reservoirs. We propose a joint frequency-dependent AVO (JFAVO) inversion in an anisotropic medium based on a periodic layered double-porosity medium. This JFAVO will allow us to quantitatively study the influence of fluids on the dispersion of PP- and PS-wave velocities and anisotropic parameters. First, we used a double-porosity medium to analyse the frequency-dependent characteristics of velocities and anisotropy parameters. We found that the anisotropic parameters show obvious dispersions, similar to those of velocities. Then, we derived the JFAVO inversion based on Rüger's equation to extract the dispersion of velocities and anisotropic parameters. Finally, we analysed the stability and applicability of the inversion algorithm, and used three sets of models to analyse the sensitivity of dispersion properties to fluids. The numerical analysis results show that PP-wave velocity dispersion and anisotropic parameter δ dispersion are sensitive to fluids, whereas, the velocity dispersion of the PS-wave is not. When saturation exceeds 80%, the velocity dispersion and anisotropic parameter dispersion properties are not sensitive to fluids.

Rock Physical Modeling and Seismic Dispersion Attribute Inversion for the Characterization of a Tight Gas Sandstone Reservoir

Gas identification using seismic data is challenging for tight gas reservoirs with low porosity and permeability due to the complicated poroelastic behaviors of tight sandstone. In this study, the Chapman theory was used to simulate the dispersion and attenuation caused by the squirt flow of fluids in the complex pore spaces, which are assumed to consist of high aspect-ratio pores (stiff pores) and low aspect-ratio microcracks (soft pores). The rock physics modeling revealed that as the gas saturation varies, P-wave velocity dispersion and attenuation occurs at seismic frequencies, and it tends to move to high frequencies as the gas saturation increases. The velocity dispersion of the tight gas sandstone causes a frequency-dependent contrast in the P-wave impedance between the tight sandstone and the overlying mudstone, which consequently leads to frequency-dependent incidence reflection coefficients across the interface. In the synthetic seismic AVO modeling conducted by integrating the rock physics model and the propagator matrix method, the variations in the amplitudes and phases of the PP reflections can be observed for various gas saturations. The tests of the frequency-dependent AVO inversion of these synthetic data revealed that the magnitude of the inverted P-wave dispersion attribute can be used to indicate gas saturation in tight sandstone reservoirs. The applications of the frequency-dependent AVO inversion to the field pre-stacked seismic data revealed that the obtained P-wave dispersion attribute is positively correlated with the gas production from the pay zone at the well locations. Thus, the methods of the rock physics modeling and the frequency-dependent AVO inversion conducted in this study have good potential for the evaluation of the gas saturation in tight gas sandstone reservoirs.

Effective Pressure Prediction from 4D Seismic AVO Data During CO2-EOR and Storage

The use of the time-lapse seismic difference analysis methods, time-lapse AVO forward and inverse technology to study and distinguish the changes in saturation and pressure caused by CO2 injection into reservoirs is one of the difficult issues in the current technology research for monitoring CO2 geological storage. Based on the time-lapse seismic AVO inversion method proposed by Landro, this paper distinguishes the fluid saturation and pressure effect after CO2 injection. First, establish a petrophysical model with simultaneous changes in CO2 saturation and pressure, and then use the Smith & Gidlow’s AVO simplified equation based on the Landro method to derive the relationship between the saturation differences (ΔS), pressure differences (ΔP) and the time-lapse AVO intercept differences (ΔR0), gradient differences (ΔG). The relationship is further applied to the actual time-lapse seismic AVO inversion to quantitatively analyze the saturation and pressure changes after CO2 injection. The effective pressure difference value obtained by inversion has a good match with the measured wellhead pressure difference value, which can well explain the pressure sweep range and the pressure difference change caused by CO2 injection in the reservoir, which provides a basis for monitoring the underground occurrence state and storage safety of CO2.

Study of seismic attributes recognition method of high quality igneous rock reservoir

Aimed target area is deeply buried, complex lithology, dual media, reservoir development degree is controlled by a variety of factors, meanwhile, lateral thickness and lithofacies change rapidly, and strata formation is poor. Therefore, igneous rock reservoir has difficulty in predicting, since seismic is complicated to track trace, reservoir attribute analysis is hard to determine the time window, and inversion modeling requires sophisticated. By analyzing, the basalt in the target research area accounts for the principal component of the igneous rock, however, the igneous rocks with relatively developed reservoirs are mostly distributed in the trachyte breccia which has good productivity. The results of petrophysical study indicate that frequency-dependent AVO inversion method is an important means to identify fluid and reservoir prediction, notwithstanding it is difficult to distinguish high-quality reservoirs barely by P-wave impedance. Consequently, AVOF inversion method is appropriately proposed to identify igneous rock reservoir. Foremost, eliminating the effects of algorithm,frequency, spectrum balancing and other factors, then put the improved three-term Aki&Richards frequency-dependent AVO inversion method applying to distinguish igneous reservoir fluid and lithology, for the purpose of carrying out the identification of high-quality reservoirs.

P-Wave Reflection Approximation of a Thin Bed and Its Application

“Thin-bed” reservoirs have become important targets of seismic exploration and exploitation. However, traditional amplitude versus offset/amplitude versus angle (AVO/AVA) technologies, for example, those based on Zoeppritz equations and their approximations for a single interface, are not sufficiently accurate for thin-bed stratigraphy. Analytic solutions of thin-bed reflectivity may become practical for thin-bed AVO analysis and inversion. Therefore, a linear analytic approximation of thin-bed P-wave reflectivity is developed under small-incidence and thin-bed assumptions. Numerical simulations show that the amplitude approximation errors are usually smaller than 10% for incidence angles less than 20 degrees, and the thin-bed thicknesses are less than one-tenth of the P-wave wavelength. Based on the least-squares approach, the inversion strategy is proposed using the approximate formula. A synthetic data test shows that the proposed inversion method can produce more accurate thin-bed properties than that based on the Zoeppritz equations, which reveals the potential of the inversion method based on the linear analytic approximate formula in the fine characterization of thin reservoirs.

Prestack AVO inversion based on the vectorised reflectivity method with blockiness constraint

Many oil fields in the world are continental clastic facies lake basins, which develop many thin interbedded reservoirs. Conventional AVO inversion methods based on Zoeppritz equation are not suitable for the identification and characterisation of such reservoirs. In this study, we propose a new inversion method based on the vectorised reflectivity method with blockiness constraint in order to obtain the inversion results with high accuracy and resolution. The vectorised reflectivity method can provide the 1D analytical solution of elastic wave equation, and simulate full-wave field. Therefore, it overcomes the problems caused by internal multiples, converted waves, transmission loss, and reservoir thickness in the thin interbedded reservoirs. Applications of the synthetic data and field data demonstrate that, compared with the conventional method based on Zoeppritz equation, the proposed method shows advantages in estimating the elastic parameters of the thin interbedded reservoirs. In addition, the inversion of blocky model and logging data demonstrate that the differential Laplace constraint gives higher resolution and better numerical stability than Gaussian and Cauchy constraints. The computational efficiency and high accuracy that the proposed approach provides are expected to be exploited in characterising thin interbedded reservoirs in many fields.

Prestack AVO inversion for brittleness index of shale based on BI_Zoeppritz equation and NSGA II

As one of the evaluation characteristics of shale sweet spots, the brittleness index (BI) of shale formations is of great significance in predicting the range of sweet spots, and guiding hydraulic fracturing. Based on the three elastic parameters of P-wave velocity (VP), S-wave velocity (VS) and density obtained by conventional prestack AVO inversion, BI can be calculated indirectly using the Rickman formula. However, the conventional AVO inversion based on Zoeppritz approximation assumes that incident angle is small and elastic parameters change slowly, which affects the inversion accuracy of the three elastic parameters. Additionally, using these three elastic parameters to obtain BI indirectly also leads to cumulative errors of the inversion results. Therefore, we propose an inversion method based on BI_Zoeppritz equation to directly estimate VP, VS and BI. The BI_Zoeppritz equation is an exact Zoeppritz equation for BI, which is used as the forward operator for the proposed method. The multi-objective function of the inversion method is optimized by a fast nondominated sorting genetic algorithm (NSGA II). An initial model and an optimized search window are used to improve the inversion accuracy. The test results of model data and actual data reveal that this method can directly obtain the BI with high precision. In addition, the stability and noise immunity of the proposed method are verified by the seismic data with random noise.

Facies-constrained prestack seismic probabilistic inversion driven by rock physics

Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs. Prestack seismic inversion is an important method for the quantitative characterization of elasticity, physical properties, lithology and fluid properties of subsurface reservoirs. In this paper, a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation (Jacobian and Hessian matrix) of rock moduli is derived. Besides, the contribution of porosity, shale content and fluid saturation to AVO reflectivity is analyzed. The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties. On the basis of this, one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model. The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model. Through the cross parallel of multiple Markov chains, multiple stochastic solutions of the model parameters can be obtained simultaneously, and the posterior probability density distribution of the model parameters can be simulated effectively. The posterior mean is treated as the optimal solution of the model to be inverted. Besides, the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results, so as to realize the simultaneous estimation of reservoir elasticity, physical properties, discrete lithofacies and dry rock skeleton. The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China.

Least-Squares Migration Technique Improves Imaging and Inversion Offshore China

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19285, “Improving Imaging and Inversion Through Least-Squares Q-Kirchhoff APSDM: A Case Study From Offshore China,” by Chenghai Jiao, Jianfeng Yao, and Keat Huat Teng, CGG, et al., prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission. Least-squares migration (LSM) has become an increasingly important imaging tool. Recently, several case studies have shown that LSM provides greatly improved seismic imaging. However, only a few examples reveal its advantages in both imaging and amplitude-vs.-offset (AVO) inversion. In this paper, the authors propose a least-squares Q migration (LSQM) method that combines the benefits of both LSM and Q prestack depth migration (QPSDM) to improve the amplitude fidelity and image resolution of seismic data. Introduction Seismic data can be considered an outcome of a forward modeling experiment with an unknown Earth velocity model and absorption Q model. Several major factors can affect the resolution and amplitude fidelity of the resulting seismic images, namely the acquisition con-figuration, absorption of the anelastic Earth, and the complexity of the subsurface velocity model. With regard to the first factor, seismic images can suffer from limited migration aperture and nonuniform illumination. This illumination issue can be addressed through image-domain single-iteration LSM. With regard to the second and third factors, it is important to include Q absorption in the velocity model-building process and the migration algorithm, and to use least-squares means to control the compensation of energy loss through LSQM. In the South China Sea, complex faulting geology poses a significant challenge to imaging with narrow-azimuth towed streamer data. Over the past decade, Q tomography and Q migration have been developed to tackle the issue with Q absorption. However, because both the Q compensation level and the noise level of seismic data usually increase with travel time and frequency, high-frequency noise and migration swings are often overamplified in Q migration. The issues with amplitude distortion associated with fault shadows and smeared fault images with a limited usable aperture can be coped with better through the use of LSM, and seismic image resolution can be increased by compensating Q effect with travel time and frequency. The high-frequency noise generated from QPSDM can be attenuated better through LSM if Q can be brought into the process of LSM. To fully exploit the merits of LSM and QPSDM, seismic imaging can be improved through LSQM. The authors apply LSQM to a narrow-azimuth marine-towed streamer survey acquired over an area with complicated faulting structures and significant absorption in the South China Sea. Using image-domain single-iteration LSQM and reservoir-oriented processing, the authors demonstrate that the previously mentioned problems can be addressed, with the result of improved AVO inversion at the wells.

THE EFFECT OF THE OVERBURDEN STRUCTURE AND INTERFACES CURVATURE ON AVO INVERSION OF PP-REFLECTIONS

In this paper, we consider an effect of incorrect accounting of the overburden structure and interfaces curvature on results of nonlinear AVO inversion. The research is carried out on synthetic data obtained using ray method for two- and three-layer models with convex and concave interfaces. We show that neglect of the overburden heterogeneity leads to inversion errors about 10–19 % and neglect of the reflector’s curvature to errors about 2 % for convex and 11 % for concave interfaces.

A modified shear-wave velocity estimation method based on well-log data

Shear wave (S-wave) velocity is crucial for AVO inversion and fluid identification. Estimating S-wave velocity based on the rock physical model is commonly necessary for practical reservoir prediction. However, it is difficult to obtain some crucial parameters such as the pore aspect ratio accurately in some conventional estimation methods. Additionally, estimating S-wave velocity usually needs to use some optimization algorithms that depend on the initial values, which may decrease the accuracy of the estimation results. To solve these difficulties, we propose a concise S-wave velocity estimation method based on the notion of the consolidation parameter. We introduce a novel parameter defined as the product of consolidation parameter and porosity for calculating S-wave velocity, and propose a simple equation about the novel parameter after derivation. We solve the simple equation and obtain the novel parameter without needing any optimization algorithm. Then, the S-wave velocity is estimated accurately by using the novel parameter. The calculation of the proposed method is concise, because the optimization algorithm is needless, and the conventional parameters, such as the consolidation parameter and the pore-aspect ratio, can be disregarded. We test the estimation method by using well-log data from an oil field, where the lithology is complex. In the data test, the estimation error of the proposed method decreases compared with the error of the conventional method.

Fluid Discrimination Based on Frequency-Dependent AVO Inversion with the Elastic Parameter Sensitivity Analysis

Fluid discrimination is an extremely important part of seismic data interpretation. It plays an important role in the refined description of hydrocarbon-bearing reservoirs. The conventional AVO inversion based on Zoeppritz’s equation shows potential in lithology prediction and fluid discrimination; however, the dispersion and attenuation induced by pore fluid are not fully considered. The relationship between dispersion terms in different frequency-dependent AVO equations has not yet been discussed. Following the arguments of Chapman, the influence of pore fluid on elastic parameters is analyzed in detail. We find that the dispersion and attenuation of Russell fluid factor, Lamé parameter, and bulk modulus are more pronounced than those of P-wave modulus. The Russell fluid factor is most prominent among them. Based on frequency-dependent AVO inversion, the uniform expression of different dispersion terms of these parameters is derived. Then, incorporating the P-wave difference with the dispersion terms, we obtain new P-wave difference dispersion factors which can identify the gas-bearing reservoir location better compared with the dispersion terms. Field data application also shows that the dispersion term of Russell fluid factor is optimal in identifying fluid. However, the dispersion term of Russell fluid factor could be unsatisfactory, if the value of the weighting parameter associated with dry rock is improper. Then, this parameter is studied to propose a reasonable setting range. The results given by this paper are helpful for the fluid discrimination in hydrocarbon-bearing rocks.

Novel wide-angle AVO attributes using rational function

Conventional AVO inversion employs Zoeppritz equations and various approximations to them to obtain the reflection coefficients of plane-waves, which are confined to a certain (small) angle range (mostly below $$40^{\circ }$$). However, near the critical angles (wide-angle), reflections at the post-critical angles provide much more potential for velocity and density inversion because of the large amplitudes and phases-shifted waveforms, while the Zoeppritz equations are not applicable anymore. Hence, there is a strong demand for the research into wide-angle AVO. With reflection coefficients at wide-angle corresponding to the features of rational function, we try to approximate the seismic data with vector fitting which is used to obtain the rational zero-pole and residual properties of wide-angle AVO. We apply this technique to classify AVO type and recognize the lithology. Our experiment shows that extending our research into wide-angle AVO is very promising in gathering richer data for a more accurate seismic analysis.

Using recursive inversion as input for gross-rock volume extraction from lithology prediction volumes: How bad can it be?

SummaryReserves and Resources can be directly estimated by using seismic lithology prediction volumes generated from AVO inversion, calibrated to wells, to estimate sand rock volumes within a stratigraphic interval. However, high quality AVO inversion data and studies are not always available. This case study utilises recursive inversion to generate prediction cubes for input to geobody based gross rock volume estimates.Here, relative recursive inversions of the near and far seismic stacks are generated. EEI rotation theory was applied to calculate relative AI and Vp/Vs volumes. These have then been converted to band-limited absolute inversion volumes by adding a low frequency model built using seismic horizons, well logs and rock physics trends. Finally, probability density functions calibrated to wells were estimated to calculate lithology prediction volumes. A sand probability volume is then calculated using these probability density functions. A relative approach has not been applied due to poor separability of different lithologies in cross-plot space.This method is applied to an area where a single multiazimuth PSDM seismic survey covers two gas fields and several deep exploration prospects. However, previous inversion studies were limited to the individual fields (each inverted separately), incorporated fluid contact information and did not cover the deeper exploration, so were therefore considered sub-optimal.Although there is potential for results from this method to be “bad”, this case study was successful. The inversion volumes generated as part of this study enabled a wholistic view of the fields and exploration prospectivity, which had not been previously possible with the available QI volumes. The seismic data used for input was exceptionally good, and there was abundant well control to provide control and for use in blind testing. The amount of validation and quality-control applied to this project cannot be under-stated. It is critically important to be mindful of the limitations and broad assumptions that are applied as part of this work-flow. These include the addition of the low-frequency model, wavelet affects not being taken into account and depth decay of the lithology predictions due to the application of a single PDF.

Prospect Identification using AVO Inversion and Lithology Prediction

Identifying prospects within a large study area is traditionally a time consuming process. Schlumberger Reservoir Seismic Services have produced an efficient prospect screening tool in the form of a well log calibrated gas sand probability cube derived from AVO inversion attributes. Pre-stack simultaneous AVO inversion was performed on a 5048 sq/km multi-client seismic survey to compute seismic elastic attributes (acoustic impedance, Poisson?s ratio and density) which can be used to generate lithology and fluid properties. The survey was acquired in 2008 and is located offshore between the giant Io/Jansz and Scarborough gas fields on Australia?s NW shelf, with potential gas reservoir?s situated at multiple levels within a complex geological setting. Well log data from within the survey area were calibrated to the seismic, from which wavelets were estimated for each angle stack using the angle-dependant reflectivity logs. Low frequency models were derived using log data from wells both inside and outside the survey extents, in conjunction with seismic velocities and guided by interpreted horizons. Elastic attribute logs were generated from the wells and used to produce non-Gaussian lithology probability density functions (PDF), which in turn are utilised to generate a 3D gas sand probability cube from acoustic impedance and Poisson's ratio volumes. The gas sand probability cube can be loaded into a 3D visualisation software package where techniques such as volume rendering and opacity are applied to quickly and efficiently screen for potential prospects from which gas sand geobodies can be extracted for volumetric analysis and further reservoir characterisation.