Comparison of Two Models for Prediction of Seismic Streamer State using the Ensemble Kalman filter

[+] Author and Article Information
Jan Vidar Grindheim

GEOGRAF AS, Strandgt. 5, NO-4307 Sandnes, Norway; Faculty of Science and Technology (IMT), Norwegian University of Life Sciences (NMBU), P.O. 5003, NO-1432 Ås, Norway; Laboratório de Ondas e Correntes (LOC) at UFRJ/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 22241-160, Brazil

Inge Revhaug

Professor, Faculty of Science and Technology (IMT), Norwegian University of Life Sciences (NMBU), P.O. 5003, NO-1432 Ås, Norway

Egil Pedersen

Professor, Department of Engineering Science and Safety, The Arctic University of Norway (UiT), Hansine Hansens veg 18, NO- 9037 Tromsø, Norway

Peder Solheim

Geograf AS, Strandgt. 5, NO-4307 Sandnes, Norway

1Corresponding author.

ASME doi:10.1115/1.4040244 History: Received August 16, 2017; Revised May 07, 2018


Towed marine seismic streamers are extensively utilized for petroleum exploration. With the increasing demand for efficiency, leading to longer and more densely spaced streamers, as well as 4D surveys and the advent of more complicated survey configurations, the demand for optimal streamer steering has increased significantly. Prediction of streamer state is an important aspect of optimal streamer steering. In the present study, the Ensemble Kalman filter has been used with two different models for seismic cable position data assimilation including parameter estimation, followed by position prediction. The data used are processed position data for a seismic streamer at the very start of a survey line with particularly large cable movements due to currents. The first model is a Partial Differential Equation model reduced to 2D by assuming constant depth and solved using the Finite Difference Method. This model has been verified against the original 3D model. The second model is based on a Path-In-the-Water model, but including a drift angle. Prediction results using various settings are presented for both models. A variant of the Path-In-the-Water method gives the best results for the present data.

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