the influence of fluid sensitive dispersion and attenuation on avo analysis summary analysis of seismic data suggests that hydrocarbon deposits are often associated with higher than usual values of attenuation but this is generally ignored during amplitude versus offset avo analysis the effect can be modelled with equivalent medium theory based on the squirt flow concept but the excess attenuation is associated with strong velocity dispersion consequently when we study reflections from the interface between such an equivalent medium and an elastic overburden we find that the reflection coefficient varies with frequency the impact of this variation depends on the avo behaviour at the interface class i reflections tend to be shifted to higher frequency while class iii reflections have their lower frequencies amplified we calculate synthetic seismograms for typical models using the reflectivity method for materials with frequency dependent velocities and attenuations and find that these effects are predicted to be detectable on stacked data two field data sets show frequency anomalies similar to those predicted by the analysis and we suggest that our modelling provides a plausible explanation of the observations

scale dependent seismic velocity in heterogeneous media the measurable traveltimes of seismic events propagating in heterogeneous media depend on the geologic scale the seismic wavelength and the propagation distance in general the velocity inferred from arrival times is slower when the wavelength is longer than the scale of heterogeneity and faster when the wavelength is shorter for normal incidence propagation in stratified media this is the difference between averaging seismic slownesses in the short wavelength limit and averaging elastic compliances in the long wavelength limit in two and three dimensions there is also the path effect shorter wavelengths tend to find faster paths thus biasing the traveltimes to lower values in the short wavelength limit the slowness inferred from the average traveltime is smaller than the mean slowness of the medium when the propagation distance is much larger than the scale of the heterogeneity the path effect causes the velocity increase from long to short wavelengths to be much larger in two dimensions tha

holocene development and geomorphic processes at omnsbreen southern norway evidence for glacier permafrost interactions omnsbreen is a small 0 5 km2 and degrading glacier situated at the regional lower limit of present day permafrost distribution and glaciation at present the existence of omnsbreen is mainly de

stochastic reservoir characterization using prestack seismic data reservoir characterization must be based on information from various sources well observations seismic reflection times and seismic amplitude versus offset avo attributes are integrated in this study to predict the distribution of the reservoir variables i e facies and fluid filling the prediction problem is cast in a bayesian setting the a priori model includes spatial coupling through markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory including gassmann s relation the likelihood model relating observations to reservoir variables including lithology facies and pore fluids is based on approximations to zoeppritz equations the model assumptions are summarized in a bayesian network illustrating the dependencies between the reservoir variables the posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models this posterior model is not analytically tra

probabilistic petrophysical properties estimation integrating statistical rock physics with seismic inversion a joint estimation of petrophysical properties is proposed that combines statistical rock physics and bayesian seismic inversion because elastic attributes are correlated with petrophysical variables effective porosity clay content and water saturation and this physical link is associated with uncertainties the petrophysical properties estimation from seismic data can be seen as a bayesian inversion problem the purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho fluid classes from seismics estimation of reservoir properties and the associated uncertainty was performed in three steps linearized seismic inversion to estimate the probabilities of elastic parameters probabilistic upscaling to include the scale changes effect and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho fluid classes rock physics equations provide the linkbetween reservoir properties and velocities and lin

hierarchical bayesian lithology fluid prediction a north sea case study seismic 3d amplitude variation with offset avo data from the alvheim field in the north sea are inverted into lithology fluid classes elastic properties and porosity lithology fluid maps over hydrocarbon prospects provide more reliable estimates of gas oil volumes and improve the decision concerning further reservoir assessments the alvheim field is of turbidite origin with complex sand lobe geometry and appears without clear fluid contacts across the field the inversion is phrased in a bayesian setting the likelihood model contains a convolutional linearized seismic model and a rock physics model that capture vertical trends due to increased sand compaction and possible cementation the likelihood model contains several global model parameters that are considered to be stochastic to adapt the model to the field under study and to include model uncertainty in the uncertainty assessments the prior model on the lithology fluid classes is a markov random field that captures local vertical horizonta

mapping lithofacies and pore fluid probabilities in a north sea reservoir seismic inversions and statistical rock physics reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization in this study we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids one of the innovations was to use a seismic impedance attribute related to the vp vs ratio that incorporates far offset data but at the same time can be practically obtained using normal incidence inversion algorithms the methods were applied to a north sea turbidite system we incorporated well log measurements with calibration from core data to estimate the near offset and far offset reflectivity and impedance attributes multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations a training data was set up using monte carlo simulations based on the well log derived probability distributions fluid substitution by ga

seismic inversion combining rock physics and multiple point geostatistics a novel inversion technique combines rock physics and multiple point geostatistics the technique is based on the formulation of the inverse problem as an inference problem and incorporates multiple point geostatistics and conditional rock physics to characterize previously known geologic information the proposed implementation combines elements of sampling from conditional probabilities and elements of optimization the technique provides multiple solutions all consistent with the expected geology well log data seismic data and the local rock physics transformations a pattern based algorithm was selected as the multiple point geostatistics component rock physics principles are incorporated at the beginning of the process defining the links between reservoir properties e g lithology saturation and physical quantities e g compressibility density making it possible to predict situations not sampled by log data results for seismic lithofacies inversion on a synthetic test and a real data application demonstrate the validity and applicability of the proposed inversion technique

predicting vshale and porosity using cascaded seismic and rock physics inversion in characterizing hydrocarbon reservoirs estimating reserves and developing models for how to best extract the hydrocarbons it is useful to know the lithology and associated porosity of the rocks in the target interval it is particularly important to have accurate estimates of volumes and flow characteristics early in the development of deepwater reservoirs as sizing the facilities to the resource is a significant component of an economically successful project accurate characterization of the reservoir can be a challenge in this situation as well control is typically very limited

vertical propagation of low frequency waves in finely layered media multiple scattering in finely layered sediments is important for interpreting stratigraphic data matching well log data with seismic data and seismic modeling two methods have been used to treat this problem in seismic applications the o doherty anstey approximation and backus averaging the o doherty anstey approximation describes the stratigraphic filtering effects while backus averaging defines the elastic properties for an effective medium from the stack of the layers it is very important to know when the layered medium can be considered as an effective medium in this paper we only investigate vertical propagation therefore no anisotropy effect is taken into consideration using the matrix propagator method we derive equations for transmission and reflection responses from the stack of horizontal layers from the transmission response we compute the phase velocity and compare the zero frequency limit with the effective medium velocity from backus averaging we also investigate how the transition from timeaverage medium to effective medium depends on contrast i e strength of the reflection coefficient series using numerical examples we show that a transition zone exists between the effective medium low frequency limit and the time average medium high frequency limit and that the width of this zone depends on the strength of the reflectioncoefficient series

uncertainty in seismic based pay volume estimation analysis using rock physics and bayesian statistics pay volume pv is a measure of the amount of hydrocarbon volume or pay in the overall reservoir and is used to appraise reservoir quality and the economics associated with reservoir development as seismic based reservoir characterization technology is advancing lithology and porosity information derived from seismic inversion is often used to derive an estimate of pv in general pv estimation from seismic data is based on some sort of rock physics transformation and seismic inversion algorithm and both may be nonunique as pv estimates are often used for economic decision making it is important to associate expected risk or confidence associated with the prediction this article presents a workflow to compute quantitative estimates of pv along with associated uncertainties from well log calibrated prestack seismic inversion attributes the main tools used in this workflow are seismic avo inversion rock physics and bayesian statistics the pv is estimated from the seismically derived rock prope

foraging spots of streaked shearwaters in relation to ocean surface currents as identified using their drift movements ocean currents are in continuous motion and strongly influence oceanic ecosystems in situ observation of currents is of primary importance for understanding how marine animals respond to ocean surface currents at various scales and for realizing effective ecosystem based management and realistic oceanographic modelling we developed a new method for obtaining in situ current measurements by using seabirds as lagrangian current sensors akin to drifting buoys we deployed high resolution global positioning system gps loggers on streaked shearwaters calonectris leucomelas foraging in the oyashio tsugaru warm current confluence in japan which is one of the most productive oceans in the world the seabirds repeatedly performed foraging trips including searching for prey and resting on the sea surface over several hundred kilometres the seabirds spent half of their time resting on the water surface and tended to be passive drifters we inferred that the drift movements of c leucomelas provided a direct and detailed description of the ocean surface currents because currents deduced from their drift movements were in good agreement with ocean surface currents derived from in situ and satellite data in addition we extracted details of shearwaters intense searching flights associated with feeding i e foraging spots from gps tracks c leucomelas did not forage at the core of anticyclonic eddies rather they used the boundary areas between eddies and the edge of eddies where primary productivity and prey density are thought to be high our study demonstrated that animal borne gps data can provide a detailed and cost efficient tool for observing ocean surface currents and can reveal the ways in which marine animals respond to these currents at a fine scale

sequential backus averaging upscaling well logs to seismic wavelengths the measure of geologic knowledge spans a tremendously wide geometric scale in the vertical dimension alone the range is from microscopic clay particle morphology and pore throat geometry to basinal megatrends many kilometers thick of a more limited scope and inherent to building synthetic seismograms is the process of relating logging tool resolution measurements to seismic wavelengths a tens of centimeters to tens of meters problem this upscaling can be achieved by sequential backus averaging

trace based and geostatistical inversion of 3 d seismic data for thin sand delineation an application in san jorge basin argentina argentina s san jorge basin which straddles the southern provinces of chubut and santa cruz in the heart of patagonia has been producing hydrocarbons since 1907 the region currently accounts for 32 of the country s production the existence and early geologic evolution of the basin is due to the same rift process responsible for the opening of the atlantic ocean in early jurassic times extensive direct faulting and local erosion accompanied the rift evolution thereby facilitating the accumulation of chiefly nonmarine terrigenous sediments well into the early cretaceous at this point andean tectonism became a major source of pyroclastic concentration in the sedimentary column it was also responsible for pervasive batholitic intrusions clastic deposition in the hydrocarbon producing zone is characterized by thick shale laminations of lacustrine and floodplain origin interspersed with much thinner and laterally sparse sand bodies the hydrocarbon reservoirs

seismic inversion for reservoir properties combining statistical rock physics and geostatistics a review there are various approaches for quantitative estimation of reservoir properties from seismic inversion a general bayesian formulation for the inverse problem can be implemented in two different work flows in the sequential approach first seismic data are inverted deterministically or stochastically into elastic properties then rock physics models transform those elastic properties to the reservoir property of interest the joint or simultaneous work flow accounts for the elastic parameters and the reservoir properties often in a bayesian formulation guaranteeing consistency between the elastic and reservoir properties rock physics plays the important role of linking elastic parameters such as impedances and velocities to reservoir properties of interest such as lithologies porosity and pore fluids geostatistical methods help add constraints of spatial correlation conditioning to different kinds of data and incorporating subseismic scales of heterogeneities

petrophysical seismic inversion conditioned to well log data methods and application to a gas reservoir hydrocarbon reservoirs are characterized by seismic welllog and petrophysical information which is dissimilar in spatial distribution scale and relationship to reservoir properties we combine this diverse information in a unified inverse problem formulation using a multiproperty multiscale model linking properties statistically by petrophysical relationships and conditioningthemtowell logdata twoapproacheshelpus 1markov chainmontecarlosampling whichgeneratesmanyreservoir realizationsforestimatingmediumpropertiesandposteriormarginalprobabilities and2optimizationwithaleast squaresiterativetechniquetoobtainthemostprobablemodelconfiguration our petrophysical model applied to near vertical anglestacked seismicdataandwell logdatafromagasreservoir includesadeterministic component based on a combination of wyllie and wood relationships calibrated with the well log data and a random component based on the statistical characterization of the deviations of well log data from the petrophysical transform at the petrophysical level the effects of porosity and saturation on acoustic impedance are coupled conditioning the inversion to well log data helps resolve this ambiguity the combination of well logs petrophysics and seismic inversion builds on the correspondingstrengthsofeachtypeofinformation jointlyimproving 1 cross resolution of reservoir properties 2 vertical resolution of property fields 3 compliance to the smooth trend of property fields and 4 agreement with well log data at well positions

impact of rock physics depth trends and markov random fields on hierarchical bayesian lithology fluid prediction early assessments of petroleum reservoirs are usually based on seismic data and observations in a small number of wells decision making concerning the reservoir will be improved if these data can be integrated and converted into a lithology fluid map of the reservoir we analyze lithology fluid prediction in a bayesian setting based on prestack seismic data and well observations the likelihood model contains a convolved linearized zoeppritz relation and rock physics models with depth trends caused by compaction and cementation well observations are assumed to be exact the likelihood model contains several global parameters such as depth trend wavelets and error parameters the inference of these is an integral part of the study the prior model is based on a profile markov random field parameterized to capture different continuity directions for lithologies and fluids the posterior model captures prediction and model parameter uncertainty and is assessed by markov chain monte carlo simulation base

bayesian lithology fluid prediction and simulation on the basis of a markov chain prior model a technique for lithology fluid lf prediction and simulation from prestack seismic data is developed in a bayesian framework the objective is to determine the lf classes along 1d profiles through a reservoir target zone a stationary markov chain prior model is used to model vertical continuity of lf classes along the profile the likelihood relates the lf classes to the elastic properties and to the seismic data and it introduces vertical correlation because the seismic data are band limited an approximation of the likelihood model provides an approximate posterior model that is a markov chain the approximate posterior can be assessed by an exact and efficient recursive algorithm the lf inversion approach is evaluated on a synthetic 1d profile that is inspired by a north sea sandstone reservoir with a realistic wavelet colored noise model and a s n ratio of three in the seismic data the results are reliable the lf classes and the interfaces between zones are largely correct the prediction unce

a monte carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes inversion of seismic data and quantification of reservoir properties such as porosity lithology or fluid saturation are commonly executed in two consecutive steps a geophysical inversion to estimate the elastic parameters and a petrophysical inversion to estimate the reservoir properties we combine within an integrated formulation the geophysical and petrophysical components of the problem to estimate the elastic and reservoir properties jointly we solve the inverse problem following a monte carlo sampling approach which allows us to quantify the uncertainties of the reservoir estimates accounting for the combination of geophysical data uncertainties the deviations of the elastic properties from the calibrated petrophysical transform and the nonlinearity of the geophysical and petrophysical relations we implement this method for the inference of the total porosity and the acoustic impedance in a reservoir area combining petrophysical and seismic information in our formulation the porosity and impedance are related with a statistical model based on the wyllie transform calibrated to well log data we simulate the seismic data using a convolutional model and evaluate the geophysical likelihood of the joint porosity impedance models applying the monte carlo sampling method we generate a large number of realizations that jointly explain the seismic observations and honor the petrophysical information this approach allows the calculation of marginal probabilities of the model parameters including medium porosity impedance and seismic source wavelet we show a synthetic validation of the technique and apply the method to data from an eastern venezuelan hydrocarbon reservoir satisfactorily predicting the medium stratification and adequate correlation between the seismic inversion and well log estimates for total porosity and acoustic impedance

velocity dispersion and upscaling in a laboratory simulated vsp a laboratory and numerical study was conducted to investigate the impact of scale dependent seismic wave propagation in randomly layered media as applied to sonic logs surface seismic and vertical seismic profiles vsps analysis of the laboratory results 1 confirmed the wavelength dependence of velocities inferred from traveltimes 2 indicated that scale effects can introduce traveltime errors when upscaling from logs to surface seismic and vsps and 3 illustrated that erroneous vsp interval velocities can result when layer thicknesses are smaller than about one tenth of the wavelength a simple approximate recipe is presented for estimating these traveltimes by successively filtering the medium using a running backus average and ray theory the scale dependent dispersion was also predicted well using a more rigorous invariant imbedding formulation the predicted traveltimes using the approximate recipe compare well with the times observed in the laboratory stack of steel and plastic layers and in numerical studies of stratified media the dispersion curves predicted by the approximate method also show the overall behavior computed with the more rigorous invariant imbedding formulation

porosity from seismic data a geostatistical approach using a geostatistical technique called cokriging the areal distribution of porosity is estimated first in a numerically simulated reservoir model then in an oil bearing channel sand of alberta canada the cokriging method consistently integrates 3 d reflection seismic data with well measurements of the porosity and provides error qualified linear mean square estimates of this parameter in contrast to traditional seismically assisted porosity mapping techniques that treat the data as spatially independent observations the geostatistical approach uses spatial autocorrelation and crosscorrelation functions to model the lateral variations of the reservoir properties in the simulated model the experimental root mean square porosity error with cokriging is 50 percent smaller than the error in predictions relying on a least squares regression of porosity on seismically derived transit time in the reservoir interval in the alberta reservoir a cross validation study at the wells demonstrates that the co

improved resolution in bayesian lithology fluid inversion from prestack seismic data and well observations part 1 methodology the focus of our study is lithology fluid inversion with spatial coupling from prestack seismic amplitude variation with offset avo data and well observations the inversion is defined in a bayesian setting where the complete solution is the posterior model the prior model for the lithology fluid lf characteristics is defined as a profile markov random field model with lateral continuity each vertical profile is further given as an inhomogeneous markov chain model upward through the reservoir the likelihood model is defined by profile and it relates the lf characteristics to the seismic data via a set of elastic material parameters and a convolution model the likelihood model is approximated the resulting approximate posterior model is explored using an efficient block gibbs simulation algorithm the inversion approach is evaluated on a synthetic realistic 2d reservoir seismic avo data and well observations are integrated in a consistent manner to obtain predictions of the lf characteristics wi