Gary Mavko

The Rock Physics Handbook: Contents

Responding to the latest developments in rock physics research, this popular reference book has been thoroughly updated while retaining its comprehensive coverage of the fundamental theory, concepts, and laboratory results. It brings together the vast literature from the field to address the relationships between geophysical observations and the underlying physical properties of Earth materials - including water, hydrocarbons, gases, minerals, rocks, ice, magma and methane hydrates. This third edition includes expanded coverage of topics such as effective medium models, viscoelasticity, attenuation, anisotropy, electrical-elastic cross relations, and highlights applications in unconventional reservoirs. Appendices have been enhanced with new materials and properties, while worked examples (supplemented by online datasets and MATLAB® codes) enable readers to implement the workflows and models in practice. This significantly revised edition will continue to be the go-to reference for students and researchers interested in rock physics, near-surface geophysics, seismology, and professionals in the oil and gas industries.

Quantitative Seismic Interpretation: Common techniques for quantitative seismic interpretation

There are no facts, only interpretations. Friedrich Nietzsche Introduction Conventional seismic interpretation implies picking and tracking laterally consistent seismic reflectors for the purpose of mapping geologic structures, stratigraphy and reservoir architecture. The ultimate goal is to detect hydrocarbon accumulations, delineate their extent, and calculate their volumes. Conventional seismic interpretation is an art that requires skill and thorough experience in geology and geophysics. Traditionally, seismic interpretation has been essentially qualitative. The geometrical expression of seismic reflectors is thoroughly mapped in space and traveltime, but little emphasis is put on the physical understanding of seismic amplitude variations. In the last few decades, however, seismic interpreters have put increasing emphasis on more quantitative techniques for seismic interpretation, as these can validate hydrocarbon anomalies and give additional information during prospect evaluation and reservoir characterization. The most important of these techniques include post-stack amplitude analysis (bright-spot and dim-spot analysis), offset-dependent amplitude analysis (AVO analysis), acoustic and elastic impedance inversion, and forward seismic modeling. These techniques, if used properly, open up new doors for the seismic interpreter. The seismic amplitudes, representing primarily contrasts in elastic properties between individual layers, contain information about lithology, porosity, pore-fluid type and saturation, as well as pore pressure – information that cannot be gained from conventional seismic interpretation. Qualitative seismic amplitude interpretation Until a few decades ago, it would be common for seismic interpreters to roll out their several-meters-long paper sections with seismic data down the hallway, go down on their knees, and use their colored pencils to interpret the horizons of interest in order to map geologic bodies.

Quantitative Seismic Interpretation: Introduction to rock physics

Make your theory as simple as possible, but no simpler. Albert Einstein Introduction The sensitivity of seismic velocities to critical reservoir parameters, such as porosity, lithofacies, pore fluid type, saturation, and pore pressure, has been recognized for many years. However, the practical need to quantify seismic-to-rock-property transforms and their uncertainties has become most critical over the past decade, with the enormous improvement in seismic acquisition and processing and the need to interpret amplitudes for hydrocarbon detection, reservoir characterization, and reservoir monitoring. Discovering and understanding the seismic-to-reservoir relations has been the focus of rock physics research. One of our favorite examples of the need for rock physics is shown in Plate 1.1. It is a seismic P–P reflectivity map over a submarine fan, or turbidite system. We can begin to interpret the image without using much rock physics, because of the striking and recognizable shape of the feature. A sedimentologist would tell us that the main feeder channel (indicated by the high amplitude) on the left third of the image is likely to be massive, clean, well-sorted sand – good reservoir rock. It is likely to be cutting through shale, shown by the low amplitudes. So we might propose that high amplitudes correspond to good sands, while the low amplitudes are shales. Downflow in the lobe environment, however, the story changes. Well control tells us that on the right side of the image, the low amplitudes correspond to both shale and clean sand – the sands are transparent.

Advanced Technologies for Monitoring CO2 Saturation and Pore Pressure in Geologic Formations: Linking the Chemical and Physical Effects to Elastic and Transport Properties

Ultrasonic P- and S-wave velocities were measured over a range of confining pressures while injecting CO2 and brine into the samples. Pore fluid pressure was also varied and monitored together with porosity during injection. Effective medium models were developed to understand the mechanisms and impact of observed changes and to provide the means for implementation of the interpretation methodologies in the field. Ultrasonic P- and S-wave velocities in carbonate rocks show as much as 20-50% decrease after injection of the reactive CO2-brine mixture; the changes were caused by permanent changes to the rock elastic frame associated with dissolution of mineral. Velocity decreases were observed under both dry and fluid-saturated conditions, and the amount of change was correlated with the initial pore fabrics. Scanning Electron Microscope images of carbonate rock microstructures were taken before and after injection of CO2-rich water. The images reveal enlargement of the pores, dissolution of micrite (micron-scale calcite crystals), and pitting of grain surfaces caused by the fluid- solid chemical reactivity. The magnitude of the changes correlates with the rock microtexture – tight, high surface area samples showed the largest changes in permeability and smallest changes in porosity and elastic stiffness compared to those in rocks withmorexa0» looser texture and larger intergranular pore space. Changes to the pore space also occurred from flow of fine particles with the injected fluid. Carbonates with grain-coating materials, such as residual oil, experienced very little permanent change during injection. In the tight micrite/spar cement component, dissolution is controlled by diffusion: the mass transfer of products and reactants is thus slow and the fluid is expected to be close to thermodynamical equilibrium with the calcite, leading to very little dissolution, or even precipitation. In the microporous rounded micrite and macropores, dissolution is controlled by advection: because of an efficient mass transfer of reactants and products, the fluid remains acidic, far from thermodynamical equilibrium and the dissolution of calcite is important. These conclusions are consistent with the lab observations. Sandstones from the Tuscaloosa formation in Mississippi were also subjected to injection under representative in situ stress and pore pressure conditions. Again, both P- and S-wave velocities decreased with injection. Time-lapse SEM images indicated permanent changes induced in the sandstone microstructure by chamosite dissolution upon injection of CO2-rich brine. After injection, the sandstone showed an overall cleaner microstructure. Two main changes are involved: (a) clay dissolution between grains and at the grain contact and (b) rearrangement of grains due to compaction under pressure Theoretical and empirical models were developed to quantify the elastic changes associated with injection. Permanent changes to the rock frame resulted in seismic velocity-porosity trends that mimic natural diagenetic changes. Hence, when laboratory measurments are not available for a candidate site, these trends can be estimated from depth trends in well logs. New theoretical equations were developed to predict the changes in elastic moduli upon substitution of pore-filling material. These equations reduce to Gassmann’s equations for the case of constant frame properties, low seismic frequencies, and fluid changes in the pore space. The new models also predict the change dissolution or precipitation of mineral, which cannot be described with the conventional Gassmann theory.«xa0less

Rock Physics of Geologic Carbon Sequestration/Storage

This report covers the results of developing the rock physics theory of the effects of CO{sub 2} injection and storage in a host reservoir on the rock�s elastic properties and the resulting seismic signatures (reflections) observed during sequestration and storage. Specific topics addressed are: (a) how the elastic properties and attenuation vary versus CO{sub 2} saturation in the reservoir during injection and subsequent distribution of CO{sub 2} in the reservoir; (b) what are the combined effects of saturation and pore pressure on the elastic properties; and (c) what are the combined effects of saturation and rock fabric alteration on the elastic properties. The main new results are (a) development and application of the capillary pressure equilibrium theory to forecasting the elastic properties as a function of CO{sub 2} saturation; (b) a new method of applying this theory to well data; and (c) combining this theory with other effects of CO{sub 2} injection on the rock frame, including the effects of pore pressure and rock fabric alteration. An important result is translating these elastic changes into synthetic seismic responses, specifically, the amplitude-versus-offset (AVO) response depending on saturation as well as reservoir and seal type. As planned, three graduate students participated inmorexa0» this work and, as a result, received scientific and technical training required should they choose to work in the area of monitoring and quantifying CO{sub 2} sequestration.«xa0less

Quantitative Seismic Interpretation: Case studies: Lithology and pore-fluid prediction from seismic data

The path of precept is long, that of example short and effectual. Seneca The case study examples in this chapter make use of the techniques described in the previous chapters, to estimate the uncertainty and map the probability of occurrence of different facies and fluids away from the well locations by combining attributes from seismic analyses with statistical rock physics. The first case study uses pre-stack seismic amplitude analyses to delineate reservoir zones in the North Sea. In the second study, again in the North Sea, use is made of seismic impedance inversions, statistical rock physics, and geostatistics to characterize the reservoir by mapping probabilities of occurrence of facies and fluids. In the third case study we show how we can combine statistical rock physics, lithofacies interpretation, and AVO analysis to discriminate between lithologies and thereby improve detectability of hydrocarbons from seismic amplitudes in Grane field, North Sea. The fourth study, from West Africa, shows an example of using seismic amplitude analyses and depth trends in rock properties to classify hydrocarbon zones at different depths. The fifth case study is an example of the full workflow of rock physics template (RPT) analysis, starting with the selection of the most appropriate RPT using well-log cross-plot analysis followed by rock physics interpretation of elastic inversion results using the selected template. The example is from the Grane field in the North Sea, the same field as for case study 3.

Quantitative Seismic Interpretation: Rock physics interpretation of texture, lithology and compaction

How does it feel To be without a home Like a complete unknown Like a rolling stone? Bob Dylan Introduction The main goal of conventional, qualitative seismic interpretation is to recognize and map geologic elements and/or stratigraphic patterns from seismic reflection data. Often hydrocarbon prospects are defined and drilled entirely on the basis of this qualitative information. Today, however, quantitative seismic interpretation techniques have become common tools for the oil industry in prospect evaluation and reservoir characterization. Most of these techniques, which are discussed in Chapter 4, seek to extract extra information about the subsurface rocks and their pore fluids from the reflection amplitudes. The seismic reflections are physically explained by contrasts in elastic properties, and rock physics models allow us to link seismic properties to geologic properties. Hence, the application of rock physics models can guide and improve on the qualitative interpretation. Figure 2.1 shows a schematic depiction of the relationship between geology, rock physics properties and seismic response. In Chapter 1 we summarized how seismic properties are controlled by a wide range of different factors, including porosity, lithology, pore fluids and pressure. As of today, the application of rock physics in seismic interpretation has mainly been on prediction of porosity and discrimination of different fluid and pressure scenarios. Little work has been done on quantitative prediction of geologic parameters from seismic amplitudes, like sorting, cement volume, clay content, sand–shale ratio, and lithofacies.

Quantitative Seismic Interpretation: Workflows and guidelines

Damn the torpedoes, full speed ahead! Admiral David Glasgow Farragut In this chapter we provide a summarizing workflow, or road map, explaining the major steps of the methodologies for seismic reservoir prediction and characterization presented in this book. In the description of the workflows we consider the term AVO to represent all offset reflectivity-dependent seismic attributes . These are not limited to the classical intercept–gradient attributes but also include other elastic parameters extracted from pre-stack data such as near- and far-offset impedances, elastic Lame parameters, converted wave impedance, P-wave and S-wave impedances, and density. The workflows are general and are applicable to any quantitative seismic attribute that can be linked to rock properties. A complete workflow of quantitative seismic interpretation should also include some necessary qualitative steps, including AVO reconnaissance, semi-quantitative feasibility studies based on well-log analysis, and qualitative interpretations of the inversion results. Below, we list our recommended techniques to be included in a combined qualitative and quantitative seismic interpretation workflow: AVO reconnaissance and seismic anomaly hunting (performed together with conventional seismic interpretation, not afterwards!). Well-log-based rock physics and AVO feasibility study (rock physics templates (RPTs) and cross-plot analysis, lithology and fluid substitution, forward seismic modeling). RPT interpretation of elastic inversion results. AVO lithology and fluid classification constrained by rock physics depth trends. AVO analysis and classification constrained by statistical rock physics and facies analysis of well logs. Elastic inversions and classification constrained by statistical rock physics and facies analysis of well logs. Quantifying the uncertainty associated in the interpretations in terms of prob-abilities. All the methodologies listed above are complementary to each other, yet they should be applied at different stages during exploration and production of an oil field.

Quantitative Seismic Interpretation: Statistical rock physics: Combining rock physics, information theory, and statistics to reduce uncertainty

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 Phillips Feynman Introduction This chapter introduces the concepts of statistical rock physics for seismic reservoir characterization. We will see how we can quantify uncertainties in reservoir exploration and management by combining rock physics models with statistical pattern recognition techniques to interpret seismic attributes. Plate 3.1 shows an example of results from a statistical rock physics study. Seismic impedances from near and far-offset inversions were interpreted using well logs and rock physics to estimate the probabilities of oil sands. The figure shows the iso-probability surface for 75% probability of oil-sand occurrence. Statistical rock physics is also useful for identifying additional information that may help to reduce the interpretation uncertainties. Seismic imaging brings indirect, but nevertheless spatially extensive information about reservoir properties that are not available from well data alone. Rock physics allows us to establish the links between seismic response and reservoir properties, and to extend the available data to generate training data for the classification system. Classification and estimation methods based on computational statistical techniques such as nonparametric Bayesian classification, bootstrap, and neural networks help to quantitatively measure the interpretation uncertainty and the misclassification risk at each spatial location.

Integrating seismic lithofacies prediction and depositional geometry analysis for reservoir delineation in a North Sea turbidite field

Summary In this study we delineate reservoir sands in a North Sea turbidite field by combining lithofacies prediction from pre-stack seismic amplitudes and quantitative analysis of seismic scale depositional geometries. This Late Paleocene field has been problematic because of complex sand distribution and non-reservoir seismic anomalies. Two of the three most recent exploration wells failed to encounter reservoir sands. Our goal is to improve our ability to forecast reservoir sands in this and similar turbidite fields in the North Sea. First we recognize and classify different lithofacies from well-log data, including sandstone, shale, marl, limestone and tuff. Since the Vp/Vs ratio together with impedance better discriminate lithology than does impedance alone, we conduct AVO analysis to predict seismic lithofacies from seismic data. We derive probability density functions (pdfs) for each of the facies in terms of zero offset reflectivity (R(0)) and AVO gradient (G). R(0) and G from inversion of real seismic data is used to predict the most likely facies distribution along selected seismic lines. Subsequently, we analyze and quantify the seismic scale depositional geometries in the area. Where reservoir sands have been identified from seismic interpretation, there is a good correlation between reservoir sand thickness and a thicker, better defined Late Paleocene seismic interval. We take advantage of this correlation and predict thickness of reservoir sands given the Late Paleocene interval thickness. These results are integrated with the sand predictions from the AVO inversion. A blind test is conducted on a well drilled at a location where post-stack seismic amplitudes indicated reservoir sands, but where only shales, tuffs and some carbonates were encountered. Our lithofacies prediction results indicate that the most likely facies at that well location in terms of AVO response is tuff, whereas the geometry analysis indicate only local presence of a thin sand unit around the well. We conclude that the seismic anomaly around the well is caused by an intra-Late Paleocene tuff unit, and this unit likely explains the local thickening of the Late Paleocene interval .