Investigating potential source rocks in oilfields is important. In this study, in addition to evaluating the hydrocarbon potential, the Arrhenius kinetic model was used to more accurately assess the source rock maturity status as well as the percentage of oil generation More
Investigating potential source rocks in oilfields is important. In this study, in addition to evaluating the hydrocarbon potential, the Arrhenius kinetic model was used to more accurately assess the source rock maturity status as well as the percentage of oil generation in the Parsi oilfield. In the Arrhenius model, the rate of kerogen decomposition is very important. In this research, some source rocks that have been tested by thermal pyrolysis were kinetically analyzed and the source rock conversion ratio (TR) was determined. Based on the results of burial history and thermal modeling, it was found that Kazhdumi and Pabdeh formations were in the oil window well while Gurpi formation did not enter the oil window due to poor organic matter content (TR = 0). Therefore, among the Kazhdomi, Gurpi and Pabdeh formations in the Parsi oilfield, Kazhdumi formation is considered as the main and most effective source rock of this oilfield with high TTI and TR = 100.
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In the oil industry, artificial intelligence is used to identify relationships, optimize, estimate and classify porosity. One of the most important steps in evaluating the petrophysical parameters of the reservoir is to identify the porosity properties. The main purpose More
In the oil industry, artificial intelligence is used to identify relationships, optimize, estimate and classify porosity. One of the most important steps in evaluating the petrophysical parameters of the reservoir is to identify the porosity properties. The main purpose of this study is to compare the accuracy and generalizability of three multilayer feed neural networks (MLFNs), radius base function networks (RBFNs) and probabilistic neural networks (PNNs) to estimate porosity using seismic properties. In this regard, geological data of 7 wells were evaluated from an offshore oil field in Hindijan in the northwest of the Persian Gulf basin. Acoustic impedance was estimated using model-based inversion method and then the mentioned neural networks were designed using optimal seismic properties and evaluated by stepwise regression method. Finally, it became clear that the MLFN model did not work well for estimating porosity. PNN has the best performance accuracy in porosity
interpolation, but RBFN generalizability is better.
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