No abstract

The extent to which seismic amplitude maps can contribute to the analysis of hydrocarbon reservoirs was investigated for clastic and carbonate reservoirs worldwide. By using a petrophysical‐based, forward modeling process called incremental pay thickness (IPT) modeling, five lithology types were quantitatively analyzed for the interplay of seismic amplitude versus lithology, porosity, hydrocarbon pore fluid saturation, bedding geometries, and reservoir thickness. The studies identified three common tuning curve shapes (concave, convex, and bilinear) that were primarily dependent upon the lithology model type and the average net porosity therein. While the reliability of pay and porosity predictions from amplitude maps varied for each model type, all analyses showed a limited thickness range for which amplitude data could successfully predict net porosity thickness or hydrocarbon pore volume. The investigation showed that systematic forward modeling is required before amplitude maps can be properly interpreted.

Better estimates of hydrocarbon pay thickness and reservoir distribution are achieved if forward modeling is combined with crossplot cluster analysis before the seismic amplitude and isochron data are converted into estimates of pay thickness. To facilitate this process, an enhanced convolutional modeling technique that incorporates petrophysical data and equations into the synthetic seismogram generation process was developed. These incremental pay thickness (IPT) forward models provide the pertinent seismic and petrophysical values required for crossplot analysis. The crossplot analyses then define which seismic variables (trough amplitude, peak amplitude, time structure, isochron, etc.) are most uniquely related to a pay thickness parameter (gross thickness, net thickness, net porosity thickness, or hydrocarbons in place). Work to date, mostly in offshore Gulf Coast gas sands, has shown significant variation in the crossplot transforms required to convert seismic data to estimated pay maps. As such, an interactive, model‐based, interpretive approach is recommended as an appropriate means to integrate petrophysical, geologic, and 3-D seismic data in the creation of reservoir pay maps.

The one-dimensional convolution model or synthetic seismogram provides more information about the seismic waveform expression of hydrocarbon reservoirs when petrophysical data (porosity, shale volume, water saturation, etc.) are systematically integrated into the seismogram generation process. Use of this modeling technique, herein called Incremental Pay Thickness (IPT) modeling, has provided valuable insights concerning the seismic response of several offshore Gulf of Mexico amplitude anomalies. Through integration of the petrophysical data, comparisons between seismic waveform response and expected reservoir pay thickness are extended to include estimates of gross pay thickness, net pay thickness, net porosity feet of pay, and hydrocarbons in place. These 1-D synthetic data easily convert to 2-D displays that often show exceptional waveform correlations between the synthetic and actual seismic data. Anomalous observed waveform responses include complex tuning curves; diagnostic isochron measurements even in unresolved thin-bed reservoirs; and extreme variations in the seismic expression of hydro-carbon-fluid contacts. While IPT modeling examples illustrate both the variability and nonuniqueness of seismic responses to hydrocarbon reservoirs, they often show good seismic predictability of pay thickness if the appropriate choice of amplitude-isochron versus pay thickness is made (i.e., peak amplitude, trough amplitude, or average amplitude versus gross pay thickness, net pay thickness, net porosity feet of pay, or hydrocarbons in place).

Recent amplitude versus offset (AVO) interpretations in the Norwegian Barents Sea and offshore West Greenland have identified subtle and complex anomalies which are best interpreted using a layer-specific, amplitude and gradient cross plot analysis scheme. The creation of AVO intercept (A) and gradient (B) seismic sections allows for a variety of stacks including A, B, A times B, and A plus B. Also, A versus B cross plots are easily generated to help define shale, wet sand and hydrocarbon trend lines that better differentiate the anomalies and provide information concerning the geological framework that created the AVO response.Determination of the background trend on an A versus B cross plot is problematic in AVO analysis since variations in lithology and depth are interdependent, with both factors greatly influencing this trend. Also, the background trend is empirically defined by the AVO response of shale-on-shale boundaries or shale-on-silt reflectors, and this may not be indicative of the trend line for a shale-on-wet sand response. However, once the empirical background trend or wet trend is established, A and B data can be rescaled such that the shale line takes on a slope of −1. Potential hydrocarbon anomalies will then plot as data points with maximum scatter away from the normalized shale line. An A plus B section based upon a normalized shale line shows the potential hydrocarbon anomalies at maximum amplitude and the wet sands at minimum amplitude.AVO analysis of A and B is generally performed together with forward modelling that generates theoretical responses for intercept amplitude (A) and gradient (B) parameters. The forward modelling helps validate the processing steps, give clues as to the non-uniqueness of various A and B responses, and integrates the geology into the study.In the Fylla area, offshore West Greenland, an AVO analysis focused on large flat spots identified on the seismic data, as part of a licence application. A and B analysis was used to delineate trends in the data, to isolate three different classes of AVO anomalies which suggest the presence of hydrocarbons, and to provide insight as to the rock properties of the interbedded sandstones and shales.An AVO analysis was also performed to help evaluate the prospectivity of an area in the Norwegian Hammerfest Basin. The purpose of the study was to establish the expected AVO response for the prospective interval from nearby gas discoveries and determine if similar anomalies exist within the prospect area. The AVO work consisted of forward modelling and A plus B analysis. Subtle AVO effects associated with gas in the Middle Jurassic section were identified.