Data Assimilation for a Geological Process Model Using the Ensemble Kalman Filter

TL;DR

This paper applies an ensemble Kalman filter-based data assimilation method to geological process models, enabling uncertainty quantification and improved conditioning of basin simulations using synthetic and real well data.

Contribution

It introduces a Bayesian inverse problem framework for geological modeling and demonstrates the effectiveness of an ensemble Kalman filter variant in real-world basin data assimilation.

Findings

Effective data assimilation with synthetic data

Successful application to real well data from Alaska basin

Improved uncertainty quantification in geological models

Abstract

We consider the problem of conditioning a geological process-based computer simulation, which produces basin models by simulating transport and deposition of sediments, to data. Emphasising uncertainty quantification, we frame this as a Bayesian inverse problem, and propose to characterize the posterior probability distribution of the geological quantities of interest by using a variant of the ensemble Kalman filter, an estimation method which linearly and sequentially conditions realisations of the system state to data. A test case involving synthetic data is used to assess the performance of the proposed estimation method, and to compare it with similar approaches. We further apply the method to a more realistic test case, involving real well data from the Colville foreland basin, North Slope, Alaska.