By analyzing core data and investigating its relation to seismic attributes, it is possible to offer a model for HFU prediction from 3D seismic data.
We demonstrate that the value of using these attributes is useful in mapping the HFUs in the Persian Gulf Kangan and Dalan supergiant gas reservoirs.
HFUs are defined as correlatable and mappahle zones within a reservoir that control fluid flow.
To estimate core-derived HFUs from 3D seismic attributes, which is the main objective of this study, the FZI data are calculated for 4 cored wells in one of the Persian Gulf hydrocarbon fields using the available porosity and permeability data.
The results of a multi-regression analysis for predicting HFUs are shown in Table 2.
Design of a probabilistic neural network for the estimation of HFUs
However, quantitative measurements for monitoring the above healing parameters with HFU are limited.
The objective of our study was to develop a quantitative approach for HFU wound analysis that did not require the user to have prior knowledge in interpreting the results.
We then monitored the same region over 21 days (measurements taken on days 3, 7, 14, and 21 after pressure sore generation) by using a 20 MHz B-mode HFU scanner (DUB_USB taberna pro medicum; Luneburg, Germany) in a controlled environment.
Figure 1 illustrates HFU scans obtained from one animal.
In HFU images, the distribution of echoes generates image texture.
In the present study, t takes the values of 3, 7, 14, and 21 (the days that we monitored the tissue with the HFU scanner).