Enhancing internal multiple prediction by using the inverse-scattering series: Methodology and field application

Abstract

We have introduced four approaches that dramatically enhance the application of the inverse-scattering series method for field data internal multiple prediction. The first approach aims to tackle challenges related to input data conditioning and interpolation. We address this through an efficient and fit-for-purpose data regularization strategy, which in this work is a nearest-neighbor search followed by differential moveout to accommodate various acquisition configurations. The second approach addresses cost challenges through application of angle constraints over the dip angle and opening angle, reducing computational cost without compromising model quality. We also develop an automatic solution for parameterization. The third approach segments the prediction by limiting the range of the multiples’ generator, which can benefit the subsequent adaptive subtraction. The fourth approach works on improving predicted model quality. The strategy includes correctly incorporating the 3D source effect and obliquity factor to enhance the amplitude fidelity of the predicted multiples in terms of frequency spectrum and angle information. We illustrate challenges and report on the improvements in cost, quality, or both from the new workflow, using examples from synthetic data and from three field data 2D lines representative of shallow- and deepwater environments.