Richard Uden

The challenge of scale in seismic mapping of hydrate and solutions

Rock physics transforms are based on data generated in the laboratory in the scale of centimeters or in the well in the scale of meters. We aspire to use them at the seismic scale in tens or hundreds of meters. The vast disparity between these scales may lead to erroneous results during direct application of rock physics to seismic data.

Novel Use of P- and S-Wave Seismic Attenuation for Deep Natural Gas Exploration and Development

The well selected for the application of our attenuation theory and extraction of attenuation attributes from seismic data is the Texaco well (API 177104132700) in Block 313 of Eugene Island in the Gulf of Mexico (Well 2700). The rock physics diagnostics indicates that the rock can be described by the uncemented (soft-sand) model. This model is used to predict the S-wave velocity that was missing in the original well data. The Pand S-wave inverse quality factors are computed according to our theoretical model. The ratio of these inverse quality factors (P-to-S) is small (on the order of one) in wet rock and large in the gas zone. The seismically-measured attenuation ratio may serve, therefore, as an indicator of hydrocarbons. The synthetic seismic traces computed using the well data and the ray-tracer with attenuation, specifically developed for this project, indicate that attenuation affects the seismic response and, therefore, can be extracted from real seismic data, including the P-to-P and P-to-S reflection amplitude. Rock Physics Diagnostics – Model for Velocity The gas saturation in the well was calculated from the resistivity curve while the clay content was estimated by linearly scaling the gamma-ray curve between its minimum and maximum values. It was assumed that the formation water has the bulk modulus 2.85 GPa and density 1.01 g/cc while the gas has the bulk modulus 0.14 GPa and density 0.26 g/cc. The total porosity was calculated from the bulk density by assuming that the density tool samples the virgin formation with gas saturation as calculated from resistivity. The measured impedance and P-wave velocity are compared to the curves due to the uncemented (soft-sand) model. The proximity of the data and model (Figure 1) indicates that this model is appropriate for the well under examination. This model was then use to predict the S-wave velocity (absent in the measured data) from the P-wave velocity. The in-situ impedance is plotted versus the total porosity and Poisson’s ratio (PR) in Figure 2 where the data are color-coded by gamma-ray and by water saturation. Similar cross-plots are shown in Figure 3 but for wet conditions where the elastic properties and density were calculated using the P-wave-only fluid substitution. The soft-sand model curves for water-saturated rock are superimposed upon the wet-condition data to further emphasize the relevance of this model. The curves are produced for varying porosity and each for fixed clay content. The latter variable changes from one to zero with step 0.2. These model curves fully encompass the well log data.

Rock physics diagnostic in a sand/shale sequence

In this paper we will review the use of Rock Physics Diagnostics applied to log data that illustrates a relational model between porosity, clay and saturation. We use these relations to estimate porosity from elastic impedance attributes. Using statistical fits may work locally around the property values experienced by a well for example but away from the well you need to employ some systematic approach to improve the confidence and reduce the risk associated with such estimations. Such a systematic approach is Rock Physics Diagnostics and we believe that this methodology is essential for extracting rock properties from seismic data.

Seismic Attribute Analysis In Hydrothermal Dolomite, Devonian Slave Point Formation, Northeast British Columbia, Canada

Recent advances in visualization technology and seismic attribute analysis are beginning to revolutionize the landscape of 3-D seismic interpretation. This presentation focuses on the interpretive use of post-stack seismic attributes for seismic reservoir characterization. Multiple seismic attributes facilitate structural interpretation and recognition of seismic stratigraphy, but as importantly, they may offer clues to lithology typing and estimation of fluid content from seismic data. Potential benefits include reduction of stratigraphic and structural drilling risks, seismic reservoir characterization in exploration settings, and value increase of new and vintage 3D seismic data. Immediate improvements in drilling risk reduction can be obtained by using multiple seismic attributes. This enhancement occurs because each seismic attribute computation resembles a non-linear filter that decomposes reflection data into its constituents, and, as a consequence, use of multiple seismic attributes restores much of the discriminating information retained in the originally recorded wavefield (Barnes, 2001; Taner, 2001). Thus, each seismic attribute, for instance, amplitude, inadvertently contains only a subset of the total information recorded, since a single seismic attribute represents only one numerical property of a propagating seismic wavefield. In this presentation, we advocate the use of geometric attributes in conjunction with relative acoustic impedance and frequency-derived seismic attributes. In the past, use of geometric attributes was mostly limited to edge detection, where edges in the seismic data commonly represent faults or stratigraphic terminations (seismic facies changes). In this Devonian Slave Point Formation case study, we use post-stack seismic attributes to

Lithology substitution in fluvial sand

Dichotomy in geophysical remote sensing is both relative and absolute: while the seismic reflection relates to the impedance contrast, the reservoir properties, such as porosity, relate to the absolute value of the impedance. One way of interpreting the relative in terms of the absolute is to perturb the absolute and calculate the corresponding relative. To accomplish this task in geophysics, the velocity and density curves from an existing prototype well are perturbed according to a likely situation at a different location. A new pseudowell thus created is used to generate synthetic full-waveform seismograms which are then compared to real data at the location, with the expectation that the similarity in the seismic response reflects the similarity in the reservoir properties.

Monte Carlo AVO Analysis For Lithofacies Classification

The objective of this work is to use AVO intercept and gradient, in conjunction with well-log petrophysics analysis, to discriminate and classify lithofacies in a shaly sand reservoir. Careful log and core analysis, and rock physics modeling was used to identify the important seismic litho-classes. Monte Carlo AVO simulations based on statistical rock physics were used to set up the classconditioned probability distributions (pdfs) of intercept and gradient. The effect of thin-layer anisotropy on the probability distributions of AVO intercept and gradient was considered by simulating various realizations of sand-shale thin layers. Monte Carlo simulations, by taking into account distributions of values instead of single average values, help to avoid the flaw of averages (Mukerji and Mavko, 2005). Monte Carlo simulations also give us confidence intervals and other measures of uncertainty. Computations using averages and average trends alone do not give any indication of the uncertainty due to the variability in the properties. The pdfs were then used to classify the seismic AVO intercept and gradient cubes to estimate the most-likely facies and obtain lithofacies probability cubes.

Practical exploration applications of dynamic immersive environments

Human scale immersive environments are becoming more and more prevalent throughout our industry. The hardware to generate these impressive data visualizations is fast becoming a commodity. But what of the software necessary to take advantage of these environments? What are the best ways to use these environments in real world exploration? How must we change our workflow to leverage these new tools? This paper presents a few responses to these questions based on several man-years of pioneering work in the field. Tools and practices will be described in the context of real world examples.