Seis2Rock: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data

TL;DR

Seis2Rock is a novel data-driven method that directly links pre-stack seismic data to petrophysical properties using learned basis functions, enabling accurate subsurface characterization and monitoring of reservoir changes.

Contribution

The paper introduces Seis2Rock, a new approach that combines synthetic well logs and seismic data to improve petrophysical inversion accuracy.

Findings

Successfully recovered porosity, shale content, and water saturation models.

Demonstrated effectiveness on synthetic datasets and real reservoir monitoring data.

Enabled time-lapse inversion for water saturation changes.

Abstract

The inversion of petrophysical parameters from seismic data represents a fundamental step in the process of characterizing the subsurface. We propose a novel, data-driven approach named Seis2Rock that utilizes optimal basis functions learned from well log information to directly link band-limited petrophysical reflectivities to pre-stack seismic data. Seis2Rock is composed of two stages: training and inference. During training, a set of optimal basis functions are identified by performing singular value decomposition on one or more synthetic AVO gathers created from measured or rock-physics synthesized elastic well-logs. In inference, seismic pre-stack data are first projected into a set of band-limited petrophysical properties using the previously computed basis functions; this is followed by regularized post-stack seismic inversion of the individual properties. In this work, we apply the Seis2Rock methodology to a synthetic dataset based on the Smeaheia reservoir model and the open Volve field dataset. Numerical results reveal the ability of the proposed method in recovering accurate porosity, shale content, and water saturation models. Finally, the proposed methodology is applied in the context of reservoir monitoring to invert time-lapse, pre-stack seismic data for water saturation changes.