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Maurya, S. P.
- Estimating Elastic Impedance from Seismic Inversion Method:A Study from Nova Scotia Field, Canada
Abstract Views :202 |
PDF Views:84
Authors
Affiliations
1 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi 221 005, IN
1 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi 221 005, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 628-635Abstract
In the present study, elastic impedance (EI) inversion is performed to estimate subsurface elastic properties in inter-well regions. These elastic properties are helpful to discriminate gas-bearing formation from gasfree formation, as well as overpressure zone. Seismic reflection data from the Penobscot Scotia shelf, Canada are used for the analysis which is performed in two steps. First, the method is tested with noise-free synthetic data, as well as with addition of 5%, 10%, 20% and 30% Gaussian noise. The analysis shows that efficacy of EI inversion decreases by 3.4% with addition of 30% noise in the data compared to noise-free data. In the second step, EI inversion is applied to the real data and variation of EI is estimated for near- and far-angle stack gathers. The analysis demonstrates that the inverted results follow the well-log curve satisfactorily. The results also show higher resolution images for the far-stack data compared to the nearstack data. Incidentally, it is noticed that the area of study does not contain any major gas or overpressure zones. As of now, the analysis has been performed for small datasets of the region. Robustness of the method needs to be tested with more data from other parts of the region as well.Keywords
Elastic Impedance, Gas Formation, Modelbased Inversion, Overpressure Zone.Full Text
References
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- Porosity Prediction from Offshore Seismic Data of F3 Block, the Netherlands using Multi-Layer Feed-Forward Neural Network
Abstract Views :235 |
PDF Views:99
Authors
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
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
Source
Current Science, Vol 119, No 10 (2020), Pagination: 1652-1662Abstract
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.Full Text