Current Science

Prabodh Kumar Kushwaha ¹, S. P. Maurya ², Piyush Rai ¹, N. P. Singh ²

Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi 221 005, IN
2 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi 221 005, IN

Abstract

In the present study, seismic and well log information is incorporated with a multi-layer feed-forward neural network (MLFN) to predict porosity in the inter-well region. The aim of this study is to estimate a relationship between porosity and impedance to characterize the reservoir, if any, in the offshore F3 block, the Netherlands. MLFN is used to generate a connection between porosity logs and a set of seismic attributes, which are further used for porosity prediction. Modelbased inversion is employed to produce an acoustic impedance volume, which is a reliable technique for quantitative estimation of reservoir characteristics and acoustic impedance. The model-based inversion results indicate that the acoustic impedance (AI) in the region varies from 2500 to 6200 m/s*g/cm3, which is comparatively low and indicates loose formation. Thereafter, AI along with other attributes estimated from seismic data, is used as an input in MLFN, and porosity is predicted. The technique is first implemented on the traces close to well locations, and the findings are correlated with well log information, and after appropriate matching, the entire seismic segment is inverted for porosity. The results indicate that the porosity varies from 0.07 to 0.40. Further, a relationship between predicted porosity and inverted impedance is derived to represent the connection between these two parameters in the region. Moreover, based on this study, it is concluded that there is no significant reservoir in the region. However, as the analyses are performed for a specific range of data, it is possible that other parts of the area may have a different stratigraphy and possibility of the primary reservoir in the area.

Keywords

Acoustic Impedance, Multi-layer Feed-forward Neural Network Reservoir, Porosity, Seismic Inversion.

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