عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Porosity is one of the most important parameters in evaluation of reserves storage capacity and hydrocarbon production. Commonly, this parameter is calculated via core and well log data. One of the most important limitation of these two data groups, despite their high values are lack of development which is limited to well positions. Estimation of petrophysical parameters in inter- and outer-well spaces is valuable and important. The use of seismic data with a large lateral expansion has an important and critical role in the estimation of petrophysical parameters in the inter-well spaces. In this research, Neutron porosity estimation using seismic attributes, on Asamri Formation in the Chashmeh Khush Field (N. Ahwaz) was done. In the first, by using of Sonic and Density logs, synthetic seismogram were created and then they were correlated with seismic data and average seismic wavelet used in inversion process, were extracted from three well with total correlation 87.47%. Afterward, three dimensional seismic data were converted to the acoustic impedance via inversion process and were used as external attributes to the accompaniment of internal attributes resulted from raw seismic data during the estimation. using stepwise regression, the relationship between porosity and seismic attributes were determined and eight seismic attributes including inversion results, derivative Instantaneous Amplitude, Integrated Absolute Amplitude, Derivative Instantaneous Frequency, Filter 25/30-35/40, Cosine Instantaneous Phase and Raw Seismic were selected as optimized attributes. Then via two Multiattribute linear regression (MLR) and Probabilistic neural network methods )PNN(, porosity estimated and specified according to validation result and error, PNN method was proven to be more efficient. Finally, based on the porosity estimated from seismic data, it was cleared that mixed clastic-carbonate intervals in the lower sequence, generally have a better porosity condition than dolomite and limestone in the upper part, this trend is easily recognizable from well log data.