Reservoir Fluid-Density Inversion and Application Based on PNN
Xiangui Li · Dizhi ke-ji qingbao · 2011
The fluid-density in reservoir rock porosity can reflect the reservoir characteristics directly.This is one of the key parameters of today′s seismic exploration for hydrocarbon.Probabilistic neural network(PNN) is a neural net work based on probability and statistics idea.The probabilistic neural network inversion reservoir fluid-density is used for the preferred combination of multi ple attributes to complete the training of neural network learning and probabili ty estimation through its non-linear expansion.This can effectively eliminate t he adverse effects of individual data,stabilize the inversion process and reduc e the inversion multi-solvability.The gas reservoir fluid-density inversion res ult of a gas field in western Sichuan shows that the PNN inversion method,with its high accuracy and correspondence with the actual test gas well,can solve so me problems that some conventional seismic hydrocarbon detection methods are una ble to solve.It can conduct a quantitative analysis for reservoir gas-bearing, and provide an important data support for reservoir prediction,gas-water identi fication and gas reservoir description.