Petroleum Exploration in Salt Dom using Hopfield Neural Networks and its comparison with Kohonen Neural Networks results (Case study: Qum Salt Dome)

One of the most important methods for geophysics study to obtain subsurface resources and hydrocarbon exploration is gravimetry method. Gravimetry is a method for measuring density changes and identifying different types of underground structures such as salt domes. Since salt as a leakproof material plays an important role in the formation of the traps and studies have shown that most of the salt structures are kind of associated with oil trap so studies on salt domes are essential for hydrocarbon exploration. Since the use of neural networks as a tool in the interpretation of gravity data has found wide use, their progress always along with correct procedures and interpretation of gravity data introduced new path for researchers and engineers in the use of this method. In this study two algorithm were used for interpretation of gravimetry data: Hopfield and Kohonen neural networks and geology and stratigraphy studies

کلیدواژه ها:

Gravimetry ، Hopfield ، Kohonen ، Salt Dome