Kyle Spikes

Probabilistic seismic inversion based on rock-physics models

A site-specific rock-physics transform from porosity, mineralogy, and pore fluid to elastic-wave velocities is used to invert seismic amplitude data for clay content, total porosity, and saturation. The implementation is Bayesian and produces probabilistic values of the reservoir properties from seismic measurements and well data. This method focuses on an exploration setting where minimal data exist. Two key assumptions reduce the problem and keep the prior information as noncommittal as possible. First, a prior interpretation of the seismic data is required that provides a geobody on which to perform the inversion. Second, the reservoir thickness is assumed to be constant, as are the rock properties within the reservoir. The prior distributions of the reservoir properties are assumed to be uncorrelated and independent, although this is not an essential assumption. Central to theinversion is the generation of a complete set of earth models derived from the prior distribution. A site-specific rock-physics...

Multichannel analysis of surface wave method with the autojuggie

Abstract The shear (S)-wave velocity of near-surface materials and its effect on seismic-wave propagation are of fundamental interest in many engineering, environmental, and groundwater studies. The multichannel analysis of surface wave (MASW) method provides a robust, efficient, and accurate tool to observe near-surface S-wave velocity. A recently developed device used to place large numbers of closely spaced geophones simultaneously and automatically (the ‘autojuggie’) is shown here to be applicable to the collection of MASW data. In order to demonstrate the use of the autojuggie in the MASW method, we compared high-frequency surface-wave data acquired from conventionally planted geophones (control line) to data collected in parallel with the automatically planted geophones attached to steel bars (test line). The results demonstrate that the autojuggie can be applied in the MASW method. Implementation of the autojuggie in very shallow MASW surveys could drastically reduce the time required and costs incurred in such surveys.

Useful resorting in surface‐wave method with the Autojuggie

The multichannel analysis of surface wave (MASW) method (Park et al., 1999; Xia et al., 1999, 2002a,b) is a relatively new technique. This technique consists (1) acquiring wide-band (∼2 to ∼100 Hz), high-frequency ground roll using a multichannel recording system; (2) creating efficient and accurate algorithms designed to extract and analyze 1D multimodal Rayleigh-wave dispersion curves from ground roll using a basic, robust, and pseudoautomated processing sequence; (3) developing stable and efficient algorithms (Xia et al., 1999) incorporating the minimum number of assumptions necessary to obtain 1D near-surface S-wave velocity profiles using the generalized linear inversion (GLI) method (Xia et al., 1999; Tian and Goulty, 1997); and (4) combining a standard common midpoint (CMP) roll-along acquisition format (Mayne, 1962) with surface-wave inversion of each shot gather to generate a cross-section of S-wave velocity (Xia et al., 1998; Miller et al., 1999). Based on published results (Xia et al., 2002a,b), when calculated with high accuracy, the fundamental mode phase velocities generally can provide reliable S-wave velocities with ±15% relative error.

Modeling elastic properties and assessing uncertainty of fracture parameters in the Middle Bakken Siltstone

A statistical rock-physics technique, based on well data that provides estimates and associated uncertainty of fracture density in the Middle Bakken Siltstone, is presented. Geologic and hydrocarbon-charging history of the Middle Bakken indicate multiple sets of fractures that justify treating this unit as elastically isotropic. The generalized n-phase self-consistent model relates the elastic properties to composition, matrix porosity, and fracture porosity, where an assigned aspect ratio and volumetric fraction corresponds to each input. The modeling of bulk density as a function of total porosity supplies deterministic estimates of the composition. Analysis of in situ stress and pore-stiffness calculations provide a range of fracture aspect ratios, corresponding to open fractures. Stochastic simulation of fracture porosity initiates the statistical nature of the technique. This treatment of fracture porosity enables the rock-physics model to be treated statistically through multiple realizations. Model...

A new stochastic inversion workflow for time-lapse data: hybrid starting model and double-difference inversion

Non-uniqueness presents challenges to seismic inverse problems, especially for time-lapse inversion where multiple inversions are needed for different vintages of seismic data. For time-lapse applications, the focus typically is to detect relatively small changes in seismic attributes at limited locations and to relate these differences to changes in the underlying physical properties. We propose a robust inversion workflow where the baseline inversion uses a starting model, which combines a high-frequency fractal component and a low-frequency component from well log data. This starting model provides an estimate of the null space based on fractal statistics of well data. To further focus on the localized changes, the inverted elastic parameters from the baseline model and the difference between two time-lapse data are summed together to produce the virtual time-lapse seismic data. This is known as double-difference inversion, which focuses primarily on the areas where time-lapse changes occur. The misfit function uses both data and model norms so that the ill-posedness of the inverse problem can be regularized. We pre-process the seismic data using a local correlation-based warping algorithm to register the time-lapse datasets. Finally, very fast simulated annealing, a nonlinear global search method, is used to minimize the misfit function. We demonstrate the effectiveness of our method with synthetic data and field data from Cranfield site used for CO2 sequestration studies.

Pore-shape And Composition Effects On Rock-physics Modeling In the Haynesville Shale

In this work, we investigated pore-shape and composition effects on the elastic properties in the Haynesville Shale. A pilot hole of a horizontal well (Well 1) and a vertical well (Well 2) were analyzed. The rock-physics model we applied is the self-consistent model. This model is not limited to specific grain/pore shapes or specific compositions. The ability to characterize the effects of pore shapes makes this model an effective approach to study the elastic properties of gas shale. For Well 1, the relationship between P-wave velocity and porosity with different aspect ratios and compositions were investigated. The result indicated that both pore shape and composition have significant effects on the elastic properties of the Haynesville Shale. For Well 2, the relation between P-wave velocity and density with different porosities, aspect ratios and composition assemblages were studied. The result showed a combined effect of aspect ratio and composition, as well as the effect of different composition assemblages with the same aspect ratio distribution. Overall, the selfconsistent model explained the data sets accurately and provided constraints on the pore shape and the composition. This work provides a more comprehensive understanding of the Haynesville Shale in terms of relating its reservoir properties to elastic parameters.

From seismic traces to reservoir properties: Physics-driven inversion

Forward modeling and inversion are commonly used tools to translate seismic traces into elastic Earth properties. Comparisons of real and multiple iterations of synthetic seismic data, the latter computed from initial and perturbed models of elastic properties, provide this translation. The final Earth model, the one corresponding to the best match between the real and synthetic seismic data, possibly represents the spatial distribution of independent elastic constants such as P-wave impedance (Ip) and Poissons ratio (ν). Although these maps of elastic properties are meaningful to geophysicists, they fail to supply necessary information to engineers who require lithology, fluid, and porosity, i.e., bulk property maps and in-situ conditions.

Gassmann-consistency of velocity-porosity transforms

Rock physics results have shown that the effective elastic moduli, and thus the seismic velocity of rock, depend on the porosity, lithology, and pore fluid. Laboratory experimentation has resulted in several deterministic porosity-velocity transforms or models for siliciclastic rocks. The development of these models led to the interpretation of petrophysical properties, porosity in particular, from seismic velocity data.

Strategy For Estimation of Lithology And Reservoir Properties From Seismic Velocities And Density

Summary The use of seismic data for estimation of lithology and reservoir properties is important in seismic exploration and reservoir characterization. We present a technique for the estimation of porosity, mineral fraction (lithology) and fluid properties from seismic parameters and density. The method includes a resampling of rock physics constraints, made from some rock physics theory, which result in direct relations between the various rock properties to be estimated and each data parameter. The final estimation is made by comparing the relations obtained for all the data parameters. The method is flexible to the type of rock model considered for linking the rock properties and seismic parameters. It also reveals the non-uniqueness of the rock property solutions in case the problem is

Reservoir And Elastic Property Prediction Away From Well Control

The ultimate goal of this effort is to quantify lithology, fluid, and porosity from seismic data. We approach this goal by generating synthetic seismograms and comparing them to real seismograms, with the expectation that a similarity in the seismic response indicates a similarity in the underlying rock properties, specifically, lithology, fluid, and porosity (LFP). The synthetic seismic approach also can be used to understand the sensitivity of real data to LFP. The starting point for synthetic seismic generation is rock physics analysis and geologic interpretation of well data. Then a geological model of the subsurface with geobodies and expected depositional and diagenetic patterns outlined. Next, geobodies are populated with clay content and the corresponding porosity and hydrocarbon saturation at the well log scale. These rock properties are translated into the Pand S-wave velocity and bulk density by using a rock physics model. Then synthetic seismograms are produced from the elastic properties. We illustrate this methodology using well data from a fluvial environment in which the clay content is related directly to the total porosity by the dispersed clay model and to oil content via irreducible water saturation. These properties are related to the velocity and density by the Raymer-Greenberg-Castagna model, which is appropriate for mature sand/shale sequences. We use standard schemes of depositional models to construct a vertical geological section of grain size distributions, with a fluvial channel that includes the well. We examine two – the massive and gradational – depositional sequences within the channel. In the case under examination, synthetic seismic appears to be sensitive to lithology, fluid, and porosity. This result indicates that LFP information can be extracted from real seismic. Introduction The depositional environment, diagenesis, burial, and compaction can vary substantially within the same basin or within the same stratigraphic facies. These factors may directly affect lithology, fluid, and porosity (LFP). LFP, in turn, affects the elastic properties of the rock and, subsequently, the seismic response. The main question of exploration is how to translate seismic data into LFP. Our approach to this problem is synthetic seismogram generation and comparison thereof to real seismic data. If the real and synthetic data are similar, we assume a similarity in the underlying LFP. Of course, this approach carries nonuniqueness because multiple combinations of reservoir properties and geometries can produce the same seismic signature. However, the power of the synthetic seismic approach is that responses can be generated easily, rapidly, and massively for various scenarios of lithology, porosity, and fluid distributions in the subsurface. By so doing, the geophysicist can create a site-specific catalogue of seismic signatures to assess various possibilities and probabilities contained in the field data. Also, synthetic seismic data helps to understand the sensitivity of real data to LFP. Well data represent the rock properties along a onedimensional spatial trajectory. Therefore, information contained in the well data lacks lateral continuity and cannot be used alone for reliable predictions of rock properties where lateral geologic variation is significant. Seismic amplitude data augments well data by providing information about the subsurface away from the well but at a much larger scale than well data. One powerful approach to mapping seismic-scale lithofacies away from well control is statistical rock physics. In this approach lithofacies are identified at a well, their seismic signatures, such as AVO are modeled, and synthetic seismic attributes, such as the intercept and gradient, are created and mapped into the seismic attribute space. A rock physics model then transforms the seismic amplitude or impedance into rock properties based on the seismic facies. Ing another current example neural networks are used to calibrate the elastic to reservoir properties at a well and then apply these statistically derived transforms to seismic inversion volumes. We build upon this approach by deterministically generating pseudo-wells and the corresponding synthetic seismic data for site-specific geological scenarios, not necessarily reflected in available well data. The first step is to establish a rock physics model at the calibration well that quantitatively explains the data. The second step involves building a geological model of the subsurface. This model can be based on a geological interpretation of the seismic amplitude volume or simply on the knowledge of the depositional environment around a well and assumptions about the shape and interval architecture. Once the expected depositional and diagenetic trends are identified, the geobodies are populated with clay content at the well-log scale according to standard and predictable depositional patterns. In this particular case, clay content is equated to shale content (or grain-size distribution). In general, shale content does not equal clay content. However, in this study we equate the two, which is justified by rock physics relations present in the log data. After the geobodies are populated with clay content, total porosity and hydrocarbon saturation values are assigned. In this example, both the total porosity and the hydrocarbon saturation are related directly to the clay content via the dispersed clay model and capillary pressure, respectively. Next, the rock physics model established at the well is used to obtain the Pand S-wave velocity and density from porosity, fluid, and mineralogy. Lastly, synthetic seismograms are generated to assess the effects of LFP, reservoir geometry, and lateral position on seismic signatures. This methodology automatically generates pseudo-wells with an accompanying suite of log curves at any spatial trajectory within the geologic section from clay content only. Using clay content only for rock property and seismic velocity prediction is not a new approach. Furthermore, the concept of pseudo-wells is not new. For example, Monte Carlo simulation is used to generate well data that account for the possible stratigraphic and physical property variability of a target interval. Rock physics relations are used to construct pseudo-log data where the original data were missing due to borehole conditions. We make a next step and combine rock physics with site-specific principles of deposition. We expect this approach to reduce uncertainty further in seismic interpretaReservoir And Elastic Property Prediction Away From Well Control; Spikes and Dvorkin; Stanford Rock Physics Laboratory www.rocksolidimages.com