Detection of lateral velocity contrasts by crosswell traveltime tomography

Goudswaard, Jeroen; Kroode, Fons P. E. ten; Snieder, Roel; Verdel, Arie

doi:10.1190/1.1444353

Cited by 19 publications

(18 citation statements)

References 8 publications

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“…The majority of effort, as measured by the topics of published and presented work, has concentrated on developing and improving algorithms for estimating the geophysical parameters themselves (Newman, 1995;Lazaratos et al, 1995;Wilt et al, 1995;Nemeth et al, 1997;Goudswaard et al 1998 to list but a few). In most applications where nongeophysical parameters, such as temperature during a steam flood (Lee et al, 1995) or CO 2 saturations during CO 2 flood Wang et al, 1998) are the object of the crosswell survey, correlations between the geophysical parameters, e.g., velocity or electrical conductivity, and the desired reservoir parameter are derived and used to infer the distribution of reservoir parameters from the distribution of the geophysical parameters.…”

Section: Introductionmentioning

confidence: 99%

Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging

et al. 2003

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This paper presents a method for combining seismic and electromagnetic measurements to predict changes in water saturation, pressure, and CO 2 gas/oil ratio in a reservoir undergoing CO 2 flood. Crosswell seismic and electromagnetic data sets taken before and during CO 2 flooding of an oil reservoir are inverted to produce crosswell images of the change in compressional velocity, shear velocity, and electrical conductivity during a CO 2 injection pilot study. A rock properties model is developed using measured log porosity, fluid saturations, pressure, temperature, bulk density, sonic velocity, and electrical conductivity. The parameters of the rock properties model are found by an L1-norm simplex minimization of predicted and observed differences in compressional velocity and density. A separate minimization, using Archie's law, provides parameters for modeling the relations between water saturation, porosity, and the electrical conductivity. The rock-properties model is used to generate relationships between changes in geophysical parameters and changes in reservoir parameters.Electrical conductivity changes are directly mapped to changes in water saturation; estimated changes in water saturation are used along with the observed changes in shear wave velocity to predict changes in reservoir pressure. The estimation of the spatial extent and amount of CO 2 relies on first removing the effects of the water saturation and pressure changes from the observed compressional velocity changes, producing a residual compressional velocity change. This velocity change is then interpreted in terms of increases in the CO 2 /oil ratio. Resulting images of the CO 2 /oil ratio show CO 2 -rich zones that are well correlated to the location of injection perforations, with the size of these zones also correlating to the amount of injected CO 2 . The images produced by this 3 process are better correlated to the location and amount of injected CO 2 than are any of the individual images of change in geophysical parameters.

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Section: Introductionmentioning

confidence: 99%

Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging

et al. 2003

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“…The reflected waves, which generally have less energy, are either muted or considered as noise. These assumptions are commonly made when seismic cross‐well data are processed by tomography to recover the background velocity ( Zhou et al 1995; Goudswaard et al 1998 ). Unfortunately, the cross‐well geometry often leads to multiple (transmission) events in the recorded data as soon as there is a substantial contrast in the background velocity between sources and receivers.…”

Section: Methodsmentioning

confidence: 99%

“…This is generally carried out via a least‐squares approach using a local gradient optimization. Applications of this method to cross‐well data have been given by Luo and Schuster (1991), van Geloven and Herman (1995), Zhou et al (1995) and Goudswaard et al (1998) . These tomography algorithms require manual or semi‐automatic picking, which can be an overwhelming task when the data volume is large.…”

Section: Introductionmentioning

confidence: 99%

Automatic cross‐well tomography by semblance and differential semblance optimization: theory and gradient computation

Plessix

Mulder

Kroode

2000

Geophysical Prospecting

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A technique for automatic cross‐well tomography based on semblance and differential semblance optimization is presented. Given a background velocity, the recorded seismic data traces are back‐propagated towards the source, i.e. shifted towards time zero using the modelled traveltime between the source and the receiver and corrected for the geometrical spreading. Therefore each back‐propagated trace should be a pulse, close to time zero. The mismatches between the back‐propagated traces indicate an error in the velocity model. This error can be measured by stacking the back‐propagated traces (semblance optimization) or by computing the norm of the difference between adjacent traces (differential semblance optimization). It is known from surface seismic reflection tomography that both the semblance and differential semblance functional have good convexity properties, although the differential semblance functional is believed to have a larger basin of attraction (region of convergence) around the true velocity model. In the case of the cross‐well transmission tomography described in this paper, similar properties are found for these functionals. The implementation of this automatic method for cross‐well tomography is based on the high‐frequency approximation to wave propagation. The wavefronts are constructed using a ray‐tracing algorithm. The gradient of the cost function is computed by the adjoint‐state technique, which has the same complexity as the computation of the functional. This provides an efficient algorithm to invert cross‐well data. The method is applied to a synthetic data set to demonstrate its efficacy.

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“…It is also possible to determine the depth to an interface by the inversion of surface gravity measurements (Pedersen 1977; Barbosa, Silva and Medeiros 1997). On the other hand, data from a cross‐borehole seismic experiment may be inverted to find the configuration of a near‐vertical interface, in which case it is assumed to be a function of depth (Goudswaard et al 1998). In any case, most of the authors believe that an interface may be described by a single‐valued function of one or two coordinates, such as Z ( x ), Z ( x,y ) or X ( z ).…”

Section: Introductionmentioning

confidence: 99%

Finding the shape of a local heterogeneity by means of a structural inversion with constraints

Ditmar

2002

Geophysical Prospecting

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Various aspects of structural inversion are considered. The aim of the inversion is limited to finding the shape of an isolated 2D homogeneous body, although the technique may be generalized to the case of interfaces with steep fragments, faults or overhangs. The unknown parameters are shifts of border points. The shift directions can be normal to the initial heterogeneity configuration or to another contour. Medium properties (seismic velocities, densities, etc.) within the heterogeneity are assumed to be known. The optimal shape is determined iteratively, a quadratic objective function being minimized at each iteration with the conjugate‐gradient method. Special attention is paid to preventing self‐intersections, for which purpose each unknown parameter is forced to lie within a certain predetermined interval. In order to achieve this, the classical conjugate‐gradient method has been modified accordingly. Three numerical examples are considered. These illustrate how the developed technique can be applied to different practical problems. The first example is devoted to monitoring an oil/steam interface by gravity gradiometry measurements. In the second example, a cross‐hole seismic experiment is simulated. It is shown that a structural inversion can restore the configuration of a local body much more accurately than traditional seismic tomography. In the third example, the shape of a salt dome is reconstructed by joint inversion of refracted traveltimes and gravity measurements. This example demonstrates how different kinds of data, used simultaneously in a structural inversion, can complement each other.

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Detection of lateral velocity contrasts by crosswell traveltime tomography

Cited by 19 publications

References 8 publications

Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging

Pressure and fluid saturation prediction in a multicomponent reservoir using combined seismic and electromagnetic imaging

Automatic cross‐well tomography by semblance and differential semblance optimization: theory and gradient computation

Finding the shape of a local heterogeneity by means of a structural inversion with constraints

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