Simultaneous Inversion of the Ladybug prospect and derivation of a lithotype volume

Fowler, J. L.; Bogaards, Mark; Jenkins, G. W.

doi:10.1190/1.1815696

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Practical applications of prestack inversion indicate that S-wave velocity and bulk density are more difficult to estimate than P-wave velocity. Consequently, additional terms are often included in the objective function to enforce soft nonlinear relationships between elastic properties (Fowler et al 2000). Deterministic inversion procedures do not honor well logs in a direct manner, and, hence, the frequency bandwidth of the inverted products is the same as that of the recorded seismic amplitudes.…”

Section: Introductionmentioning

confidence: 99%

Detection and Spatial Delineation of Thin-Sand Sedimentary Sequences With Joint Stochastic Inversion of Well Logs and 3D Prestack Seismic Amplitude Data

Merletti

Torres‐Verdín

2009

SPE Reservoir Evaluation &Amp; Engineering

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We describe the successful application of a new prestack stochastic inversion algorithm to the spatial delineation of thin reservoir units otherwise poorly defined with deterministic inversion procedures. The inversion algorithm effectively combines the high vertical resolution of wireline logs with the relatively dense horizontal coverage of 3D prestack seismic amplitude data. Multiple partialangle stacks of seismic amplitude data provide the degrees of freedom necessary to estimate spatial distributions of lithotype and compressional-wave (P-wave) and shear-wave (S-wave) velocities in a high-resolution stratigraphic/sedimentary grid. In turn, the estimated volumes of P-and S-wave velocity permit the statistical cosimulation of lithotype-dependent spatial distributions of porosity and permeability. The new stochastic inversion algorithm maximizes a Bayesian selection criterion to populate values of lithotype and P-and Swave velocities in the 3D simulation grid between wells. Property values are accepted by the Bayesian selection criterion only when they increase the statistical correlation between the simulated and recorded seismic amplitudes of all partial-angle stacks. Furthermore, inversion results are conditioned by the predefined measures of spatial correlation (variograms) of the unknown properties, their statistical cross correlation, and the assumed global lithotype proportions. Using field data acquired in a fluvial-deltaic sedimentary-rock sequence, we show that deterministic prestack seismic-inversion techniques fail to delineate thin reservoir units (10-15 m) penetrated by wells because of insufficient vertical resolution and low contrast of elastic properties. By comparison, the new stochastic inversion yields spatial distributions of lithotype and elastic properties with a vertical resolution between 10-15 m that accurately describe spatial trends of clinoform sedimentary sequences and their associated reservoir units. Blind-well tests and cross validation of inversion results confirm the reliability of the estimated distributions of lithotype and P-and S-wave velocities. Inversion results provide new insight to the spatial and petrophysical character of existing flow units and enable the efficient planning of primary and secondary hydrocarbon recovery operations.

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Section: Introductionmentioning

confidence: 99%

Detection and Spatial Delineation of Thin-Sand Sedimentary Sequences With Joint Stochastic Inversion of Well Logs and 3D Prestack Seismic Amplitude Data

Merletti

Torres‐Verdín

2009

SPE Reservoir Evaluation &Amp; Engineering

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Processing, inversion, and interpretation of a 2D seismic data set from the North Viking Graben, North Sea

Madiba

McMechan

2003

GEOPHYSICS

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Simultaneous elastic impedance inversion is performed on the 2D North Viking Graben seismic data set used at the 1994 SEG workshop on amplitude variation with offset and inversion. P‐velocity (Vp), S‐velocity (Vs), density logs, and seismic data are input to the inversion. The inverted P‐impedance and S‐impedance sections are used to generate an approximate compressional‐to‐shear velocity ratio (Vp/Vs) section which, in turn, is used along with water‐filled porosity (Swv) derived from the logs from two wells, to generate fluid estimate sections. This is possible as the reservoir sands have fairly constant total porosity of approximately 28 ± 4%, so the hydrocarbon filled porosity is the total porosity minus the water‐filled porosity. To enhance the separation of lithologies and of fluid content, we map Vp/Vs into Swv using an empirical crossplot‐derived relation. This mapping expands the dynamic range of the low end of the Vp/Vs values. The different lithologies and fluids are generally well separated in the Vp/Vs–Swv domain. Potential hydrocarbon reservoirs (as calibrated by the well data) are identified throughout the seismic section and are consistent with the fluid content estimations obtained from alternative computations. The Vp/Vs–Swv plane still does not produce unique interpretation in many situations. However, the critical distinction, which is between hydrocarbon‐bearing sands and all other geologic/reservoir configurations, is defined. Swv ≤ 0.17 and Vp/Vs ≤ 1.8 are the criteria that delineate potential reservoirs in this area, with decreasing Swv indicating a higher gas/oil ratio, and decreasing Vp/Vs indicating a higher sand/shale ratio. As these criteria are locally calibrated, they appear to be valid locally; they should not be applied to other data sets, which may exhibit significantly different relationships. However, the overall procedure should be generally applicable.

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Velocity determination for pore-pressure prediction

Chopra

Huffman²

2006

The Leading Edge

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Simultaneous Inversion of the Ladybug prospect and derivation of a lithotype volume

Cited by 4 publications

References 2 publications

Detection and Spatial Delineation of Thin-Sand Sedimentary Sequences With Joint Stochastic Inversion of Well Logs and 3D Prestack Seismic Amplitude Data

Detection and Spatial Delineation of Thin-Sand Sedimentary Sequences With Joint Stochastic Inversion of Well Logs and 3D Prestack Seismic Amplitude Data

Processing, inversion, and interpretation of a 2D seismic data set from the North Viking Graben, North Sea

Velocity determination for pore-pressure prediction

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