An Application of Structural Filter and Gabor Deconvolution for Avo Processing

Only available on StudyMode
  • Topic: Data, Reflection seismology, The Target
  • Pages : 5 (1488 words )
  • Download(s) : 91
  • Published : January 4, 2013
Open Document
Text Preview
An Application of Structural Filter and Gabor Deconvolution for AVO Processing Jiangbo Yu* and Dong Liu, University of Houston Summary
This paper discusses two seismic data conditioning processes – Structural Filter (SF) and Gabor deconvolution. We found that these two techniques can improve AVO analysis in CMP and CRP gathers. Structural Filter greatly enhances the S/N of gathers by removing abnormal single high amplitude events, outliers and strong random noise. At the meantime, the relative amplitude is well preserved. A smoother AVO anomaly is observed in the gathers with enhanced S/N. The gather was measured for AVO fit using a 2-term Aki-Richards equation. The AVO gradient analysis shows an amplitude change discontinuity at far offsets. We speculated that this is caused by tuning effect. Gabor deconvolution is used to increase the resolution of the data and remove the Q effect. The gradient analysis showed a much more reasonable class 2 AVO anomaly with a fit of 0.85 for top of the layer after Gabor deconvolution.

Target zone

AVO analysis contributes significantly in reservoir characterization and seismic interpretation. But AVO suffers from a wide variety of complications and problems. Castagna (1993) showed the myriad of factors affecting seismic amplitudes. According to Sherwood et al (1983) “The simple interface reflection coefficient is only a starting point toward understanding offset-dependent-reflectivity. More often we deal with reflections from layers, transition zones, or complex layered sequences.’’ A robust data processing scheme for AVO analysis needs to be carefully selected so that noise could be suppressed without biasing or corrupting the reflectivity variation with offset. Two specific AVO processing techniques that we will discuss in this paper are structural filter and Gabor deconvolution.

Figure 1: VSP data from well Wardner 175. Horizon of interest is F11 at ~1.58s. (El-Mowafy and Marfurt, 2008).

Gather Conditioning Methods
Structural Filter (SF):

Structural Filter is a dip oriented smoothing technique used for pre-stack noise suppression. This method follows the same principle of EPS and SPS described by Luo, et al (2002) and Singleton (2008). The main feature of Structural Filter is that this pre-stack noise suppression technique can remove strong random noise in both flat and dipping events. Spikes or single high amplitude events will be removed without corrupting the edges (fault/fracture) and lateral Amplitude Variation with The data we used is a 3-D seismic and well log data set from Offset (AVO). Stratton Field, Nueces and Kleberg County, Texas, USA. It covers a 7.6-square-mile area and a large number of wells were Structural Filter works on pre-stack gathers (CMP or CRP drilled and used in making a geologic analysis of the Fro gathers). At the beginning of the filtering process a Running reservoirs. The seismic interpretation at Stratton Field was Window is defined by NtNx= (50, 5), where Nt= time in ms particularly challenging because of the thin and closely stacked and Nx= Number of trace. Inside the window the algorithm reservoirs. Most of the Frio reservoirs are less than 15 feet scans for a local dip, and then follows the dip orientation as a pilot, calculates the correlation factor of each trace to the target thick and they were separated only 10-15 feet vertically. trace (defined as the middle trace in the running window) and As a major hydrocarbon producer in the U.S. Gulf Coast, the selects the result that best aligns traces according to the true middle Frio Formation consists of multiple vertically-stacked structure. The smoothing process is done by using Median reservoir sequences. The target for this study was the basal part Filter after positioning traces with their true dip. Median of the middle Frio Formation represented by the F11 horizons filtering assigns the median values of each data row (defined by (Figure 1). The structural setting of...
tracking img