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73rd EAGE Conference and Exhibition - Workshops 2011
- Conference date: 23 May 2011 - 27 May 2011
- Location: Vienna, Austria
- ISBN: 978-90-73834-13-2
- Published: 27 May 2011
121 - 129 of 129 results
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Integrated Probabilistic Seismic Reservoir Characterization Workflow
The application of a multi-step process of reservoir characterization conditioned with seismic-derived attributes and associated uncertainty is presented. The workflow consists of: a log-facies classification integrating petrophysical properties derived from formation evaluation analysis and elastic properties computed through a rock physics model; a bayesian linearized seismic inversion; a probabilistic estimation of petrophysical properties; and a seismic facies classification. This methodology introduces some improvements with respect to traditional workflows: log-facies are discriminated and classified in a petro-elastic space, both in depth and time domain, handling scale changes for the elastic logs and the discrete log-facies; the rock properties distribution is described by a Gaussian Mixture Model, rather than a Gaussian Model; the conditional probabilities of elastic properties are estimated at coarse scale taking into account the uncertainty associated to the scale change. This conditional probability is combined with the probability of elastic properties from the Bayesian inversion to obtain the posterior probability of petrophysical properties. Then, litho-fluid classes are identified based on petrophysical properties probabilities and on log-facies classification. Since log-facies are coherent both in the geological and geophysical domain, probability volumes of petrophysical properties and reservoir log-facies are easily integrated in the hierarchical reservoir modelling workflow.
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Petro-Elastic and Lithology-Fluid Inversion from Seismic Data, a Geophysical Perspective
More LessPost-stack and pre-stack seismic inversion techniques are now widely used in the oil and gas industry for reservoir characterization. Nevertheless, significant challenges remain for a truly quantitative use of inversion results in constraining detailed reservoir models: seismic inversion is non-unique and usually unable to recover the fine vertical details required in geological models; the link between inverted elastic properties and petrophysical properties is also non-unique. Recent advances in seismic reservoir characterization combine rock physics and geostatistics in order to better constrain seismic and rock property inversions and quantify uncertainty. In this presentation, we first review seismicbased stochastic reservoir characterization workflows with examples from a giant, on-shore carbonate reservoir and from a deep water turbidite field offshore West Africa. In the carbonate example, we show how post-stack stochastic inversion is combined with stochastic porosity modeling to characterize the uncertainty in the spatial distribution of thin, low porosity intra-reservoir layers, which adversely affect the field water flood performance. In the turbidite example, we combine prestack stochastic inversion with Bayesian classification to derive detailed lithofacies-probability cubes and study the uncertainty in sand volume and well connectivity. In the second part of the presentation, we introduce the concept of direct petrophysical inversion, which involves a direct inversion of seismic amplitudes for rock properties such as porosity and saturation. We illustrate this technique using 3-D and 4-D inversion examples from the North Sea. Whether direct or cascaded inversion for rock properties is performed, Petro Elastic Models (PEM) play an increasingly important role in linking seismic and reservoir properties. In the presentation, we emphasize the role of statistical rock physics for incorporating uncertainty analysis with PEM transforms.
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Importance of Geological and Rock Physics Prior Information for Lithology and Pore Fluid Estimation from Inverted Seismic Data: Exploration in a Turbidite Reservoir Case Study
Authors Ezequiel F. Gonzalez, Stephane Gesbert and Ronny HofmannUsing inverted seismic data from a turbidite depositional environment, we show that accounting only for rock types sampled by wells can lead to biased predictions of the reservoir’s fluids. As it is common in an exploration setting, information from a single well (well logs and petrological analysis) was used to define a set of initial facies that combine lithology and fluids in a single reservoir property. Based on our understanding of the depositional environment, we augmented our model with expected lithofacies and associated elastic properties, which were not sampled by the well (here different types/proportions of sand-shale mixtures). Given a geologically consistent, spatially variant, prior probability of facies occurrence, Bayesian estimation of each facies probability was computed at every sample of the inverted seismic data. In this study, we used deterministic seismic inversion to produce the input data for our analysis, which is customary in similar field studies. Accounting for the augmented geological prior we were able to generate a scenario consistent with all available data, which supports further development of the field. In contrast, the purely data driven Bayesian classification (well log and seismic) would lead to downgrading of the prospectivity of the field in our case. Based on our findings, we argue that lack of data in Quantitative Interpretation needs to be counterweighted by robust geological prior information In order to risk geological scenarios without bias in exploration settings. In this work, using inverted seismic data from a turbidite depositional environment, we show that accounting only for rock types sampled at the wells can lead to biased predictions of the reservoir fluids. We propose two critical improvements on the purely data-driven approach: first we extend the rock physics model with facies expected in the depositional environment but not sampled by wells, and second we impose a spatially variant prior probability density of lithologies and fluids. Accounting for the augmented geological prior in this way, we were able to generate a scenario consistent with all available data that supports further development of the field. In contrast, the purely data-driven Bayesian classification (well log and seismic) would lead to downgrading the prospectivity of this field.
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Some Newer Algorithms in Joint Categorical and Continuous Inversion Problems around Seismic Data
Authors James Gunning and Michel KemperTo the statistically minded, the subsurface presents a complex, spatially correlated ”mixture” distribution of rock properties to our remote sensing tools, where the mixture originates from different rock types. The information we collect from seismic, EM and production data is often dominated by the geometrical boundaries separating lithologies in the subsurface, yet many standard geophysical inversion tools use purely continuous optimization techniques that model rock properties as if they come from some common population. Newer hierarchical Bayesian approaches that embed a discrete aspect via discrete Markov random fields, coupled with conditional prior distributions that embed rock–physics relationships, offer a more convincing way to represent the categorical aspects of geology. Some published studies on these models, using seismic data, indicate the posterior distribution can be modestly sharp, though the sampling MCMC algorithms are naturally challenging. This renews interest in the maximum aposteriori model, and this talk focuses on some of the newer algorithms for seeking maximum aposteriori models in joint discrete/continuous problems. In particular, recent algorithmic work in computer vision and graph cutting techniques has made these MRF-based estimation problems much more computationally feasible. We discuss some of these ideas in the context of joint lithology/elastic inversion.
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Geological Constraints in Model-based Seismic Inversion
Authors Jaap Leguijt and Paul GelderblomGeologically Constrained Inversion is an extension to the Shell proprietary Promise stochastic inversion engine that takes lateral continuity and well constraints into account. The new algorithm produces an ensemble of reservoir-size (as opposed to single-position) models that match the seismic data, the geological constraints and the well data. The results are geologically more realistic than those of the original single-position inversion. The algorithm is parallelized so that it can be applied to real-world size datasets.
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A Reservoir Engineering Perspective on Litho-Fluid Inversion from Seismic Data
More LessIn this talk, we focus on the possible use of saturations from seismic inversion to populate reservoir simulators. While the inversion methods based on prior geological knowledge and well observations reduce the ill-posedness of the results, the results are generally still inconsistent with physical laws governing flow and transport of fluids. The incorporation of those physical constraints would result in models that are more useful to reservoir engineers.
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Solving for Facies and Fluids directly from Geostatistical Inversion
Authors Mark Sams and Denis SaussusBuilding a facies model ensures that the distribution of reservoir properties can be constrained to be consistent with geological expectation. It is also desirable that facies and property models are consistent with seismic data and rock physics understanding. This implies that the ideal modelling procedure must integrate various types of scale-dependent data within a variety of constraints. Geostatistical inversion offers a framework that is flexible enough to incorporate a wide range of data and constraints while also quantifying the associated uncertainty. In particular the ability to apply all constraints simultaneously, including facies and property distributions, leads to a more tightly integrated and thus more robust and consistent reservoir model. As facies are a key component of any reservoir model, it is essential that the definition of facies for modelling be adapted to be meaningful across all the domains of data being integrated.
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Reservoir Property Estimation in Carbonates
More LessWe present some key challenges in rock and fluid property prediction in carbonates. Most traditional rock physics models have been developed for siliciclastic rocks. Carbonates, which have a completely different mineralogy, pore structure and geometry, pose new challenges to us. We look into the applicability of traditional rock physics models on carbonate reservoirs. We analyze the effect of porosity, mineralogy, clay content, and fluids on the bulk and shear stiffness of carbonate rocks in a middle-eastern deep reservoir, composed predominantly of limestone with varying proportions of anhydrite, dolomite, and clay. We also analyze the effect of in-situ stresses on the elastic properties, with particular emphasis on pore-pressure. We demonstrate the ability to model the elastic properties (bulk and shear moduli) as functions of mineralogy, clay content, porosity, and pore-pressure. We use a stochastic rock physics framework to build a probabilistic forward model, based on and calibrated to well-log and drilling data. Using the joint probability distribution functions, we use a Bayesian inversion technique to estimate reservoir properties (porosity, clay content, and pore-pressure) from seismic inversion data (acoustic impedance, shear impedance, and density) along with the associated uncertainties.
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Application of Geofluids Systems Analysis to Successful Geologic CO2 Storage
By K. Udo WeyerTraditionally pressure gradients and buoyancy forces play a central role in considering hydrocarbon migration and carbon sequestration, be it in the determination of flow directions for both hydrocarbons and CO2, or the determination of the height of breakthrough columns for CO2. This paper deals with the application of physically correct force fields (Hubbert, 1940, 1953) to subsurface flow. The methodology shown applies to both CO2 sequestration and hydrocarbon accumulations. Its consequences are shown on the CO2 sequestration as an example.
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