Event-based low-frequency impedance modeling using well logs and seismic attributes

Abstract

Acoustic impedance is a rock property that, under specific conditions, can be derived from seismic data and can provide important insights into reservoir parameters-such as porosity, lithology, fluid content, etc. Direct measurements of acoustic impedance are available from sonic and density well logs. Seismic inversion, a process of converting seismic data into relative impedance, provides estimates of relative acoustic impedance away from the well locations. Because absolute acoustic impedance can be related to other rock properties, the inverted relative seismic impedance could be used to predict these properties away from the wells if the missing low frequencies could be reliably calculated and compensated for. In chalk, seismic inversion finds its most significant application in porosity prediction. Compared to the well acoustic impedance, inverted relative acoustic impedance has a limited bandwidth, which is restricted on low and high ends of the frequency spectrum. Band-limited impedance (in this article referred to as relative impedance) is a useful seismic attribute for a better qualitative understanding of reservoir properties, and it often can be used for quantitative estimation of other reservoir properties, especially in clastic reservoirs. Since chalk consists of homogeneous lithofacies with clean matrix character with its porosity as the main variable, it is a perfect medium for qualitative prediction of this specific attribute. However, quantitative reservoir characterization in chalk is severely limited by the lack of low-frequency information because the bulk of the strong correlation between impedance and porosity is carried by the low-frequency trend. The missing low-frequency part of the inverted impedance data can be modeled by lateral interpolation of impedance logs between well locations. Conventionally, this interpolation has been driven by distance between the wells, which often leads to artifacts and generation of nongeologic solutions. Distance-based, well-log interpolation can be significantly improved by using seismic velocities to guide the well-log interpolation. Although the use of velocities is a major improvement, it is limited by seismic resolution and accuracy that generally deteriorates with depth. Velocity data only partially provide the missing information in the lowest frequency range. In the present study, event-based validated multivariate interpolation was successfully applied to recover lowfrequency acoustic impedance using well data, velocity data, and seismic attributes. The examples given in this article originate from the Danish sector in the North Sea. Using seismic estimates of interval velocities, layer depths, formation thicknesses, and reflection amplitudes, it was possible to significantly improve the accuracy of the predicted low-frequency response and therefore the porosity estimates as compared to conventional methods. Impedance information in the frequency interval between 0 and somewhat higher than 8 Hz was estimated, achieving an improvement especially ...

Kriging interpolation in seismic attribute space applied to the South Arne Field, North Sea

“…Further, the estimate of uncertainty obtained by the kriging system relates only to the residuals, and is not affected by the trend model. Therefore we suggest to avoid calculation of the trend model as part of the kriging system and instead use and alternative method ͑e.g., Russell et al, 1997;Hampson et al, 2001;Russell et al, 2002;Hansen et al, 2008;Pedersen-Tatalovic et al, 2008͒. Given such a trend model, we suggest using the presented method to improve the trend model by modeling the variations away from the trend.…”

An Amplitude-Based Modeling Method and its Application on the Impedance Inversion in Heterogeneous Paleokarst Carbonate Reservoirs

“…Thus, the final inverted result of predicted sediment properties is actually a set of elastic volumes that deviate as little as possible from the initial predictions, while at the same time modeling the real geology as closely as possible (Hampson et al, 2005;Huck et al, 2010). The traditional LFM building method by lateral interpolation and extrapolation of impedance logs between well locations with the guiding of interpreted seismic horizons through a variety of mathematical methods such as weighted inverse-distance, spline, and kirging etc., is driven by distance and often leads to non-geologic solutions (Cerney & Bartel, 2007;Pedersen-Tatalovic et al, 2008;Zou et al, 2013;Grant 2013). Although seismic velocities can be combined to partially provide the missed low frequency information, it is limited by the low resolution and accuracy which generally deteriorate with the increase of the burial depth.…”

“…Seismic data are routinely inverted into the relative impedance to provide quantitative predictions of reservoir properties-such as porosity, lithology fluid content etc., under specific conditions (Cerney & Bartel, 2007, Pedersen-Tatalovic et al, 2008. However, the seismically inverted relative impedance only has a limited bandwidth depending on the band-limited input seismic data, comparing with the measured well logging data or in a laboratory.…”

“…Although seismic velocities can be combined to partially provide the missed low frequency information, it is limited by the low resolution and accuracy which generally deteriorate with the increase of the burial depth. Meanwhile, seismic attributes are gradually employed into the construction of LFMs to improve the inversion reliabilities (Schultz et al, 1994;Liu et al, 2010;Pedersen-Tatalovic et al, 2008;Zou et al, 2013), but the conventional full-stack data and corresponding attributes are reported to be contaminated by the AVO (amplitude versus offset) effects no matter how much signal to noise ratio (SNR) degree can be enhanced via stacking .…”

“…However, the seismically inverted relative impedance only has a limited bandwidth depending on the band-limited input seismic data, comparing with the measured well logging data or in a laboratory. It has to be further transformed into the true absolute impedance for reservoir properties predictions away from well control, via merging reliably low-frequency information of the constrained low frequency model (LFM) (Cerney & Bartel, 2007;Pedersen-Tatalovic et al, 2008;Zou et al, 2013;Grant, 2013). In essence, the constrained LFM represents the initial prediction to construct the inversion and helps to result in a better resolvability or link between the seismic data and the actual geology.…”