Abstract:
The Dhurnal Oilfield is present in the triangle zone in the foreland belt of North Potwar deformed zone, and it is one of the oldest and major hydrocarbons producing fields of the area. The problems that undertake in this thesis can be broadly divided into four parts. Starting with seismic data interpretation by marking the structure and faulting mechanism of the area using the Synthetic results of Dhurnal 01 & 02 which shows an anticlinal Pop-up thrust dipping in NE-SW direction. Then contour their results in the form of time, velocity, and depth contours. Then bring into play Attributes of Instantaneous Frequency, Phase, Amplitude and Average Energy for confirming and characterizing the reservoir formations. For Petrophysical analysis, four wells were used along with computing the Net pay zone or Net thickness. Rock Physics moduli’s have been calculated with the help of interval velocities along the seismic lines and then gridding and contouring these results on a base map, which helps us in determining mechanical properties of reservoir formations. At last, inverse approaches were used for seismic data inversion which is Model-Based Inversion and Neural Network. In model-based inversion the first step is to calibrate data then built up low-frequency model and inverting its results for Impedance conversion. Then in last generate a regression equation along well and extract porosity and posted this porosity over impedance volume section and compare these results with our Petrophysical interpretation at the well location. On the other hand, Neural Networks were used for estimation of porosity based on elastic attributes which were derived from inversion. Dhurnal 01 & 02 were used for training the network and porosity were computed along seismic sections. These neural networks were used as crossvalidation for predicted porosity, and it shows an excellent correlation when comparing those porosities results at the well location.
