Position prediction of marine seismic streamer cables using various kalman filter methods

Abstract

Towed seismic streamer cables are extensively employed for offshore marine petroleum exploration. With the increasing need for accurate streamer steering due to rising number and length of streamers and decreasing intrastreamer separation, as well as new types of survey configurations, accurate modeling, positioning, and path prediction of the streamers are imperative. In the present study, a variety of models and methods have been implemented and utilized for data assimilation of full-scale seismic streamer position data for a marine seismic streamer, followed by path prediction ahead of time. The methods implemented are described, including various models used with the Kalman filter, extended Kalman filter, and ensemble Kalman filter, with comparison and evaluation of prediction results. One particular method, the Path-In-the-Water ensemble Kalman filter (PIW-EnKF), appears to be the most robust method with good prediction results compared to the other methods, as well as having low computational cost. As a case study with full-scale data, the PIW-EnKF is further employed for estimation and prediction of a complete streamer spread.