Per Avseth

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...

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 Gassmanns 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...

Rock physics diagnostic of north sea sands: Link between microstructure and seismic properties

Velocity in high porosity sands strongly depends on the position and amount of intergranular material. Velocity is high if the original grains are cemented at their contacts. It is low if the pore-filling material is placed away from the contacts. The amount and type of intergranular material can be theoretically determined from velocity and porosity data by adjusting an effective-medium theoretical model curve to a trend in the data and then assuming that the microstructure of the sediment is such as used in the model. We apply this diagnostic method to clean sands in the Heimdal formation (the North Sea). The results show that the reservoir-zone sand in one well under examination has slight contact cementation, while it is completely uncemented and friable in another well. This conclusion is supported by microscopic grain images. The rock physics diagnostic is important for establishing a velocity-porosity trend consistent with local geology. Such trends can improve the accuracy of porosity prediction from seismic data.

Rock physics modeling of unconsolidated sands: Accounting for nonuniform contacts and heterogeneous stress fields in the effective media approximation with applications to hydrocarbon exploration

By treating contact stiffness as a variable, one can extend the effective medium approximation used to obtain elastic stiffness of a random pack of spherical grains. More specifically, we suggest calibrating effective media approximation based on contact mechanics by incorporating nonuniform contact models. The simple extension of the theory provides a better fit for many laboratory and field experiments and can provide insight into the micromechanical bonds associated with unconsolidated sediments. This approach is motivated by repeated observations of shear-wave measurements in unconsolidated sands where observed shear-wave velocities are lower than predicted by the Hertz-Mindlin contact theory. We present the calibration process for well-log data from a North Sea well penetrating a shallow-gas discovery and a deepwater well in the Gulf of Mexico. Finally, we demonstrate the benefit of using this model for amplitude variation with angle (AVA) analysis of shallow sand targets in exploration and reservoir studies.

Statistical rock physics Combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization

“Any physical theory is a kind of guesswork. There are good guesses and bad guesses. The language of probability allows us to speak quantitatively about some situation which may be highly variable, but which does have some consistent average behavior…. Our most precise description of nature must be in terms of probabilities.” —Richard Feynman This paper presents snapshots of current and emerging trends in applied statistical rock physics for reservoir characterization. By integrating fundamental concepts and models of rock physics, statistical pattern recognition, and information theory with seismic inversion and geostatistics, we can quantify and reduce uncertainties in reservoir management. Rock physics allows us to link seismic response and reservoir properties and to extend the available data to generate training data for the classification system. Seismic imaging brings indirect, but nevertheless spatially exhaustive, lateral and vertical information about reservoir properties that are not available from pinpoint well data. Classification and estimation methods based on computational statistical techniques such as nonparametric Bayesian classification, bootstrap, and neural networks help quantitatively measure interpretation uncertainty and the mis-classification risk at each spatial location. Geostatistical stochastic simulations add spatial correlation and small-scale variability which is hard to identify from seismic only because of the limits of resolution. Combining deterministic physical models with statistical techniques leads to new methods for interpretation and estimation of reservoir rock properties from seismic data. These formulations identify the most likely interpretation, the uncertainty of the interpretation, and guide quantitative decision analysis. Subsurface heterogeneity delineation is a key factor in reliable reservoir characterization. These heterogeneities occur at various scales and can include variations in lithology, pore fluids, clay content, porosity, pressure, and temperature. Some methods used in seismic reservoir characterization are purely statistical. Others are deterministic, based on physical models (theoretical, laboratory). Each group of techniques can have some …

Seismic reservoir mapping from 3-D AVO in a North Sea turbidite system

We present a methodology for estimating uncertainties and mapping probabilities of occurrence of different lithofacies and pore fluids from seismic amplitudes, and apply it to a North Sea turbidite system. The methodology combines well log facies analysis, statistical rock physics, and prestack seismic inversion. The probability maps can be used as input data in exploration risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic‐scale sedimentary units which we refer to as seismic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick‐bedded clean sands with water, plane laminated thick‐bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the VP/VS ratio helps resolve these ambiguities. We establish a statistically representative training database by ident...

Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks — A review of selected models and suggested work flows

Rock physics has evolved to become a key tool of reservoir geophysics and an integral part of quantitative seismic interpretation. Rock-physics models adapted to site-specific deposition and compaction help extrapolate rock properties away from existing wells and, by so doing, facilitate early exploration and appraisal. Many rock-physics models are available, each having benefits and limitations. During early exploration or in frontier areas, direct use of empirical site-specific models may not help because such models have been created for areas with possibly different geologic settings. At the same time, more advanced physics-based models can be too uncertain because of poor constraints on the input parameters without well or laboratory data to adjust these parameters. A hybrid modeling approach has been applied to siliciclastic unconsolidated to moderately consolidated sediments. Specifically in sandstones, a physical-contact theory (such as the Hertz-Mindlin model) combined with theoretical elastic bounds (such as the Hashin-Shtrikman bounds) mimics the elastic signatures of porosity reduction associated with depositional sorting and diagenesis, including mechanical and chemical compaction. For soft shales, the seismic properties are quantified as a function of pore shape and occurrence of cracklike porosity with low aspect ratios. A work flow for upscaling interbedded sands and shales using Backus averaging follows the hybrid modeling of individual homogenous sand and shale layers. Different models can be included in site-specific rock-physics templates and used for quantitative interpretation of lithology, porosity, and pore fluids from well-log and seismic data.

AVO classification of lithology and pore fluids constrained by rock physics depth trends

Elastic properties of rocks are strongly influenced by local geological trends and can change markedly even within a sedimentary basin. Critical geologic factors that control elastic properties are either related to depositional environment or burial history. Knowledge about the expected change in seismic response, as a function of depositional or compactional trends, will increase the ability to predict hydrocarbons, especially in areas with little or no well log information. In other words, by understanding the geologic constraints in an area of exploration, one can reduce the range of expected variability in rock properties and hence reduce the uncertainties in seismic reservoir prediction. Figure 1 depicts this problem, where we assume there is well log control only in the shallow interval on the shelf edge. Before extending the exploration into more deeply buried zones, or to more distal deepwater environments, it is important to understand the rock physics trends in the area.

Rock physics estimation of cement volume, sorting, and net-to-gross in North Sea sandstones

Rock physics represents the link between geologic reservoir parameters (e.g., porosity, clay content, sorting, lithology, saturation) and seismic properties (e.g., VP/VS, density, elastic moduli). Rock physics models can either be used to interpret observed sonic and seismic velocities in terms of reservoir parameters, or they can be used to extrapolate beyond observed range to predict certain “what if” scenarios in terms of fluid or lithology substitution. Rock physics models can also be used to estimate expected seismic properties from observed reservoir properties.

Shale rock physics and implications for AVO analysis : A North Sea demonstration

Shales normally constitute more than 80% of sediments and sedimentary rocks in siliciclastic environments. Shales are important both in controlling the overburden seismic wave propagation as well as the reflectivity contrast between cap rocks and reservoir rocks in prestack seismic data. Therefore, during AVO analysis it is crucial to understand the seismic properties of shales as a function of mineralogy and compaction. In this study, we derive the local shale trend for a North Sea gas-and-oil field by integrating rock physics modeling with well-log and seismic data analysis.