AVO Data Research Articles

Bayesian seismic 4D inversion for lithology and fluid prediction

Seismic data acquired at different times over the same area can provide insight into changes in an oil/gas reservoir. Probabilities for pore fluid will typically change while the lithology remains stable over time. This implies significant correlations across the vintages. We have developed a methodology for Bayesian prediction of joint probabilities for discrete lithology-fluid classes (LFCs) for two vintages, simultaneously considering the seismic AVO data of both vintages. By taking into account cross-vintage correlations of elastic and seismic properties, the simultaneous invertion ensures that the individual results for both vintages as well as their differences are consistent and constrained by the seismic data of both vintages. The method relies on prior geological knowledge of stratigraphic layering, possible lithologies and fluids within each layer, and possible cross-vintage changes in lithology and pore fluid. Multiple LFCs can be used to represent different strengths of dynamic cross-vintage changes. We have tested the algorithm on a synthetic data set and on data from the Edvard Grieg field in the central North Sea. Synthetic results demonstrate that the algorithm is able to use dual-vintage data together with a prior model specifying their correlations to calculate joint LFC posterior probabilities for both vintages with a lower degree of uncertainty than independent single-vintage inversions. The Edvard Grieg results indicate that the underlying model is sufficiently general to explain 4D variations in seismic data using a reasonably simple prior model of 4D LFC changes.

Recovery of the main fracturing direction of oil reservoirs from seismic and AVO data

In the present work, we formulate and solve an inverse problem to recover the main fracturing direction of petroleum reservoirs from seismic and AVO data. Equivalent media theory provides a starting point for modeling an equivalent anisotropic medium starting from an isotropic background with fractures. We then represent the equivalent medium in a new coordinate system whose x-axis is parallel to the reservoir's top. Using the classical theory of wave propagation and the Invariant Embedding technique (specially to deal with anisotropic media), we solve exactly the elastodynamic equations of motion to compute the reflection of P, SV, SH waves at the reservoir's top, without the need of using the so-called Zoeppritz approximation. In the inverse problem formulation, we assume that we know a priori the reservoir isotropic background elastic constants as well as the fracture system behavior, by means of its effective compliance. We then apply a metaheuristic to recover the main fracturing direction whose input data are the amplitudes of the reflected waves recorded by the geophones and the output are two Euler-angles representing the fracturing direction. We validate the methodology using noiseless synthetic data and further test its robustness using incomplete and noisy synthetic data.

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.

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.

Seismic reservoir and source-rock analysis using inverse rock-physics modeling: A Norwegian Sea demonstration

Identifying type of rocks and fluids from seismic-amplitude anomalies can be challenging because of seismic nonuniqueness and rock-physics ambiguities. Lithology and fluid predictions based on seismic properties therefore are often associated with uncertainties. On the Norwegian Shelf, clay-rich source rocks and hydrocarbon-filled sandstones often show similar AVO responses. A seismic screening method based on rock physics enables one to better discriminate between these different facies. This technique is demonstrated on seismic AVO data (i.e., acoustic impedance [AI] and VP/VS) from the Norwegian Sea. Rock-physics models for organic-rich shales and gas sandstones are calibrated using nearby well data. Then these models are used for predictions of rock parameters away from well locations. From these predictions, the likelihood of presence of organic-rich shales versus gas sandstones can be evaluated, based on a rock-physics approach. However, there are many uncertainties in the accuracy of the calibrated models and the seismic image of the target area. Hence, predictions should be evaluated along with other geologic and geophysical information before firm conclusions about these anomalies are made.

Bayesian lithology/fluid inversion—comparison of two algorithms

Algorithms for inversion of seismic prestack AVO data into lithology-fluid classes in a vertical profile are evaluated. The inversion is defined in a Bayesian setting where the prior model for the lithology-fluid classes is a Markov chain, and the likelihood model relates seismic data and elastic material properties to these classes. The likelihood model is approximated such that the posterior model can be calculated recursively using the extremely efficient forward–backward algorithm. The impact of the approximation in the likelihood model is evaluated empirically by comparing results from the approximate approach with results generated from the exact posterior model. The exact posterior is assessed by sampling using a sophisticated Markov chain Monte Carlo simulation algorithm. The simulation algorithm is iterative, and it requires considerable computer resources. Seven realistic evaluation models are defined, from which synthetic seismic data are generated. Using identical seismic data, the approximate marginal posterior is calculated and the exact marginal posterior is assessed. It is concluded that the approximate likelihood model preserves 50% to 90% of the information content in the exact likelihood model.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Overburden distortions—implications for seismic AVO analysis and time-lapse seismic

A 3D finite difference (FD) modelling study is used to investigate the effects of various overburden distortions on seismic data. Two types of distortions are considered: shallow diffractions randomly distributed within a 400 m thick layer and shallow lenses. We find that the presence of overburden diffractors leads to considerable local amplitude variation with offset. However, smoothing of the AVO curves reduces the risk of misinterpretation of AVO data. For shallow lenses in the overburden, we find strong impact on the AVO behaviour at top reservoir level, and that these effects (in contrast to random overburden diffractors) are not reduced by simple smoothing of the AVO data. This study suggests that prior to AVO analysis, it is important to map and identify possible overburden distortions, since various types of overburden effects distort the AVO behaviour in different ways.

A FORTRAN program to determine fracture principal axes from multiazimuthal seismic P-wave AVO data

A FORTRAN program to determine fracture principal axes from multiazimuthal seismic P-wave AVO data

The implications of anisotropy for seismic impedance inversion

Seismic inversion is an established technique for deriving acoustic impedance (AI) from seismic data using well log information as a low frequency constraint. However, within anisotropic strata e.g. shale, velocity logs measured in deviated wells will typically exhibit higher velocities than would have been recorded in vertical boreholes (Furre and Brevik 1998; Hornby et al. 1999). If this effect is not corrected for before building a low frequency impedance model for seismic inversion, impedance results will be biased around deviated well trajectories. Since this is quite a subtle effect, the unwary interpreter might assign geological meaning to inversion artefacts. In this paper we demonstrate a simple but effective method for correcting deviated well AI logs for anisotropic effects. We know that others have performed similar but more sophisticated corrections (e.g. Vernik 2001; Vernik and Fisher 2001), but are not aware of this pitfall with deviated wells being widely acknowledged. Further, we consider the implications of the revealed anisotropy for single and simultaneous angle-stack (elastic impedance (EI)) inversions and propose workflows for compensating for anisotropy. Seismic impedance inversion has become an integral part of the reservoir characterisation workflow, since interpretation and quantification of reservoir rock properties is made using AI layer data rather than seismic amplitudes which relate to AI contrasts (van Riel, 2000). Recently seismic AVO data have also been inverted, bringing additional constraints on reservoir properties, since knowledge of the elastic rock properties can improve lithological and/or fluid reservoir characterisation over the use of AI alone. Two main families of inversion methods have developed, which we will classify as deterministic and stochastic. In general, deterministic methods search for a single global optimum, with an objective function being the mismatch between seismic and synthetic data. By their nature, the resulting AI volume will have a high frequency spectrum determined and limited by the seismic data, and a low frequency spectrum derived from the well data. By contrast, the high frequency limit of AIs generated by stochastic methods is raised beyond the seismic spectrum using the high frequency content of well logs coupled with geostatistics. These AI results are therefore non-unique, making them suitable for statistical uncertainty analysis on many high-resolution models. Both deterministically- and stochastically-generated AIs are subsequently converted to models of reservoir properties (porosity, Vshale etc) via upscaled petro-elastic relationships. Final results include 3D models of reservoir properties and associated uncertainties that reflect uncertainties on seismic inversion results and petro-elastic relationships. To date, there has been relatively little consideration of the impact of anisotropy on impedance inversion, despite the increasingly widespread acceptance that sedimentary rocks, and in particular shales, are often quite strongly anisotropic (e.g., Thomsen, 1986). This is presumably due to the fact that until now most inversion work has been focused on zero/near-offset cubes with (near-) vertical wells in relatively calm structural environments, where the impact of anisotropy can largely be factored out in the wavelet calibration step. However, as the community begins to consider AVO and the inversion of far-offset substacks (e.g., Vernik 2001), it will become increasingly important to estimate and account for anisotropy at various stages of the processing. In this paper we first consider the discrepancies between real AI logs from adjacent vertical and deviated wells. We then describe a correction to the deviated logs assuming an anisotropic model dependent on the proportion of shale, and show the effect of this correction on the inversion results. Finally, we consider the implications of anisotropy for EI and simultaneous AVO inversion. Even though we have used the geostatistical inversion method (Haas and Dubrule 1994; Dubrule et al. 1998), all impedance inversion techniques benefit from accounting for anisotropy revealed by deviated well logs.

ABSTRACT: Interactive Seismic Facies Classification of Stack and AVO Data Using Textural Attributes and Neural Networks

ABSTRACT: Interactive Seismic Facies Classification of Stack and AVO Data Using Textural Attributes and Neural Networks

4-D time lapse reservoir monitoring of Nelson Field, Central North Sea: Successful use of an integrated rock physics model to predict and track reservoir production

Nelson Field, in the U.K. sector of the Central North Sea approximately 180 km east of Aberdeen in 280 ft of water, is on the Forties-Montrose ridge and forms a broad, low relief anticline with four-way dip closure (Figure 1). Figure 1. Nelson Field in relation to Forties-Montrose trend and established infrastructure. 4-D seismic data, acquired using conventional towed streamers, have been successfully applied to the Paleocene reservoirs of the North Sea. Importantly, AVO applications enhance the 4-D difference signal in this reservoir. We tested a new multivariate solution to predict shear-wave slowness against field data, and used this to establish an integrated rock physics model of the Forties Sandstone. This formed the basis for full elastic inversion of the AVO data sets. Full elastic inversion results demonstrate that quantifiable seismic differences can be detected and that they are of similar magnitude to those estimated independently from core and log data. Our study found that the predicted, simulated, drainage pattern and the actual time-lapse seismic difference signal are in good agreement. The 4-D is detecting OWC movements as small as 15–20 ft within the main sand channel fairways. 4-D seismic may provide a valuable tool to monitor reservoir drainage patterns for areas characterized by low net-to-gross sand ratios, but the method clearly works best within main sand fairways to target potential upside areas. The field was discovered in 1988. An extensive appraisal campaign by Enterprise and Shell/Esso followed. By 1990 13 appraisal wells had been drilled. The field began producing in February 1994 from eight wells. To date, 27 development wells have been drilled from a single central platform: 23 oil producers and 4 water injectors. In addition, four subsea producers exploit the southern satellite area. Plateau production was maintained during 1994–1996 at 150 000 b/d. Top Reservoir is relatively shallow at …

An introduction to this special section: AVO: The Next Step

It should not be possible to use amplitude variation with offset to detect hydrocarbons. Any physicist can tell you that the mathematical inversion of even the most perfect AVO data is nonunique. I often take pride in my ability to beautifully model prestack gathers for giant gas fields without requiring VP/VS ratios of the kind expected for gas-bearing rocks. In fact, any AVO anomaly can be explained with an appropriate combination of nonreservoir rocks.

The anatomy of AVO crossplots

AVO crossplots are a simple and elegant way of representing AVO data. Offset variations in amplitude for reflecting interfaces are represented as single points on a crossplot of intercept and gradient. The advantage of this type of plot is that a great deal of information can be presented and trends can be observed in the data that would be impossible to see with a standard offset (or angle) versus amplitude plot. The crossplot is an ideal way of examining differences in AVO responses that may be related to lithologic or fluid-type variations. Commonly used techniques for revealing these differences include color-coding samples from the crossplot and using this as an overlay to a seismic display or creating weighted (or “equivalent angle”) stacks (i.e., linear combinations of intercept (R0) and gradient (G). The early literature approached AVO crossplots from the point of view of rock properties. Acentral concept that emerged from this work was the “fluid line,” a hypothetical trend based on a consideration of brine-filled rock properties together with simplifications of the reflectivity equations (Figure 1). If the intercept is plotted on the x axis and the gradient on the y axis, then for consolidated sand/shale rocks the top and base reflections form a trend from the upper left to the lower right quadrant of the crossplot that passes through the origin. When it was realized that data points for equivalent hydrocarbon-filled rocks plot to the left of this line, it became clear that normalizing the data against the fluid line might provide an optimum AVO indicator. Figure 1. AVO classes and the AVO crossplot. The similarity of the fluid-line trend to trends on time-window AVO crossplots generated from seismic was compelling, and many assumed these are the same. In fact, both models and real data examples show that in general this …

ABSTRACT: 3-D seismic imaging and interpretation of thin gas sandstone reservoirs using multi-attribute analysis on post-stack and AVO data, Offshore SE Sumatra Indonesia

ABSTRACT: 3-D seismic imaging and interpretation of thin gas sandstone reservoirs using multi-attribute analysis on post-stack and AVO data, Offshore SE Sumatra Indonesia

An example of 3-D AVO for lithology discrimination in Widuri Field, Asri Basin, Indonesia

Abstract Drilling offshore Southeast Sumatra in the area of the Widuri Field, Asri Basin has demonstrated that the oil producing Upper Gita sandstones within the Lower Miocene Talang Akar formation correlate with bright seismic amplitudes on 3-D stack data. Seismic amplitude maps at the Base Upper Gita horizon are interpreted as either a network of distributary channels or a meandering fluvial system. Channel fill lithologies vary considerably and are difficult to interpret from conventional seismic stack data. Carbonaceous shales, likely deposited during the abandonment phase of channeling, have acoustic impedances similar to sandstones. Dipole sonic log measurements of Poisson's ratio are significantly higher in carbonaceous shales than sandstones. Based on raytraced models, carbonaceous shale has a strongly decreasing AVO while sand has flat AVO response (no change in amplitude with offset). AVO attributes P and G or intercept and gradient inverted from 3-D prestack seismic data are used for detection of carbonaceous shale. Based on mapping, good correlation exists between well data and the AVO product attribute P*G. The stratigraphic model of shale abandonment fill in a meandering fluvial system is interpreted to be supported by the 3-D AVO data. This interpretation will be tested with future drilling.

Bright Spot, AVO Inversion and Synthetic on Grubišno Polje Locality

Bright spot analysis is generally used in exploration for hydrocarbons, especially in the Search for gas. However, many seismic amplitude anomalies have not been caused by gas accumulations, but they are related to lithological changes. AVO methods (amplitude vs. offset methods) can be used to distinguish between the presence of gas and lithological changes. The Grubisno polje field has been defined and mapped by these methods,and a well drilled afterward confirmed the results. After acquiring the well-logging data a Synthetic was computed. The correlation of the Synthetic with the reprocessed seismic sections and the AVO data enabled the more precise vertical and horizontal definition of the gas accumulation. This method and results contribute to the more rational exploration and developmenl of a gas field.

Genetic inversion of AVO

Dan Hampson’s article AVO inversion, theory and practice (June 1991 TLE) describes some recent developments, particularly linearized inversion techniques and Monte Carlo methods that can be applied to the inversion of AVO data to determine rock properties. He also described the problem of nonuniqueness associated with the inversion process and pointed out that we have to think about inversion as a process that gives many reasonable answers.