Video: Look-Ahead Vertical Seismic Profiling VSP Inversion Approach for Density and Velocity in Bayesian Framework
- Ahmed M. Daoud (Schlumberger) | Muhammad A. Abd El Dayem (Schlumberger)
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- Society of Petroleum Engineers
- Publication Date
- Document Type
- 2018. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- 1.6 Drilling Operations, 5.1 Reservoir Characterisation, 1.2.3 Rock properties, 5.1.6 Near-well and vertical seismic profiles, 5.6.1 Open hole/cased hole log analysis, 5 Reservoir Desciption & Dynamics, 5.6 Formation Evaluation & Management
- Prior model, Senstivity, Bayesian, Geo-statistical, Zero-offset VSPs
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To reduce drilling uncertainties, zero-offset VSPs can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert VSP corridor stacks in Bayesian framework for look-ahead prediction. The implemented approach helps to successfully predict density and velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high-pressure zones, which enables taking the correct decision during drilling.
The inversion algorithm uses Gauss-Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss-Newton has quadratic rate of convergence thus ensure speeding up the inversion process. Moreover, geo-statistical analysis has been used to efficiently utilizes prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero-offset VSP data taken from an offshore well was used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results.
The results from the synthetic case applications were encouraging to accurately predict velocity and density from just a constant prior model. The field case application shows the high strength of our proposed approach in inverting VSP data to obtain density and velocity ahead of a bit with reasonable accuracy.
Unlike the commonly used VSP inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and velocity with using geo-statistical knowledge of density and velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.