High‐resolution crosswell imaging of a west Texas carbonate reservoir: Part 4—Reflection imaging

Lazaratos, Spyros; Harris, Jerry M.; Rector, James W.; Schaack, Mark Van

doi:10.1190/1.1443809

Cited by 41 publications

(15 citation statements)

References 10 publications

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“…Following this, several groups pursued migration-like approaches for crosshole data (Miller et al, 1987;Zhu and McMechan, 1988;Rowbotham and Goulty, 1995). Initial successes in the application of this approach to real data were achieved by Findlay et al (1991); further demonstrations have been provided by Lazaratos et al (1995), who used a CMP mapping approach. Unfortunately, seismic crosshole data in strongly layered media or strongly attenuating media often fail to yield clear reflections and migration is of less utility in such cases.…”

Section: Introductionmentioning

confidence: 97%

Seismic waveform inversion in the frequency domain, Part 2: Fault delineation in sediments using crosshole data

1999

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A crosshole experiment was carried out in a layered sedimentary environment in which a normal fault is known to cut through the section. Initial traveltime inversions produced stable but low-resolution images from which the fault could be only vaguely inferred. To image the fault, wavefield inversion was used to produce a velocity model consistent with the detailed phase and amplitude of the data at a number of frequencies. Our wavefield inversion scheme uses a classical, descent-type algorithm for decreasing the data misfit by iteratively computing the gradient of this misfit by repeated forward and backward propagations. Our propagator is a full-wave equation, frequency-domain, acoustic, finite-difference method. The use of the frequencyspace domain yields computational advantages for multisource data and allows an easy incorporation of viscous effects.By running wavefield inversion on the field data, a quantitative velocity image was produced that yielded

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Section: Introductionmentioning

confidence: 97%

Seismic waveform inversion in the frequency domain, Part 2: Fault delineation in sediments using crosshole data

1999

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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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“…The wavefield-separated reflections were subsequently imaged in depth using the VSP-CDP mapping algorithm described by Lazaratos et al (1995). This algorithm can be viewed as a special case of a Kirchhoff depth migration whereby each point in the time domain maps to a single point in the depth domain.…”

Section: Rehection Imagingmentioning

confidence: 99%

17. Imaging of a Stratigraphically Complex Carbonate Reservoir with Crosswell Seismic Data

Langan¹,

Lazaratos²,

Harris³

et al. 1997

Carbonate Seismology

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A. A. VassiliouWe conducted a crosswell survey to evaluate the stratigraphy and structure in and near a stratigraphically complex limestone reservoir in the Permian Basin of West Texas. Although the reservoir was detected with surface seismic data and was successfully drilled, the surface data had insufficient resolution to image its internal structure and to determine its relationship with nearby sedimentary rocks. This is a common problem in surveys of carbonate strata, where large seismic velocities result in long characteristic wavelengths. The thickness of the reservoir in our example is -75 m, whereas the shortest wavelength in the surface seismic data is -60 m. The high-resolution crosswell seismic data, which contain frequencies more than 10 times greater than those of the surface data, clearly delineate the lateral and vertical extent of the reservoir unit between the wells. The data also reveal the inner stratification of the reservoir and show the stratigraphy of units adjacent to, above, and below the reservoir. Anisotropy in the compressional velocities obtained from the crosswell data is an indicator of the facies present. Our analysis of the P-wave direct arrivals suggests that the reservoir itself has little velocity anisotropy but that a thinly interbedded facies of mixed siliciclastics and carbonates has a horizontal velocity of -5% lower than its vertical velocity. The shale units have an intermediate degree of anisotropy of -3%.

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High‐resolution crosswell imaging of a west Texas carbonate reservoir: Part 4—Reflection imaging

Cited by 41 publications

References 10 publications

Seismic waveform inversion in the frequency domain, Part 2: Fault delineation in sediments using crosshole data

Seismic waveform inversion in the frequency domain, Part 2: Fault delineation in sediments using crosshole data

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

17. Imaging of a Stratigraphically Complex Carbonate Reservoir with Crosswell Seismic Data

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