Reservoir characterization of sawan gas field using seismic inversion and machine learning algorithms, central indus basin, Pakistan
Keywords:
Seismic Interpretation, Rock Physics, Lower Goru Formation, Model Based Inversion, Reservoir CharacterizationAbstract
The study's primary goal is to identify the thin sands packages of B and C Intervals of Lower Goru Formation of Sawan Gas Field, by using machine learning along with reservoir characterization by performing seismic interpretation, seismic inversion, petrophysics, Rock physics modelling and using machine learning algorithms. The Sawan Gas Field is situated within the Central Indus Basin of Pakistan, and its geological setting is characterized by extensional tectonic processes. Following the completion of seismic interpretation, seismic inversion, and petrophysical analysis, the findings indicate that the C Interval of the Lower Goru Formation within the Sawan Gas Field exhibits a more substantial hydrocarbon potential when contrasted with the B-Interval. Rock physics modeling have used for the prediction of P and S waves variations and how Poisson’s ratio occurs. Rock physics depicted the missing log prediction like S-wave specifically and fluid substitution also occurred with different conditions to confirm reservoir availability. Model Based Inversion (MBI) have been used to predict and confirm better results came from seismic interpretation. Wavelet was extracted from the control line and the correlation occurred. LFM (Low Frequency Models) have been generated as well. Afterwards Quality control of data was occurred by inverted techniques. Lastly to confirm results with seismic inversion technique, Machine learning (PNN) based on Bayesian classifier method which gives reliable prediction of petro physical properties, so it is used predict Volume of clay prediction, Porosity prediction and Saturation of water was predicted by Actual and Predicted values.
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