Articles | Volume 16, issue 23
Methods for assessment of models
05 Dec 2023
Methods for assessment of models |  | 05 Dec 2023

An emulation-based approach for interrogating reactive transport models

Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-729', Anonymous Referee #1, 03 Jan 2023
  • RC2: 'Comment on egusphere-2022-729', Anonymous Referee #2, 25 Mar 2023
  • AC1: 'Response to reviewers', Angus Fotherby, 21 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Angus Fotherby on behalf of the Authors (21 Apr 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 May 2023) by Richard Mills
RR by Anonymous Referee #1 (11 Jul 2023)
ED: Publish subject to technical corrections (30 Sep 2023) by Richard Mills
AR by Angus Fotherby on behalf of the Authors (05 Oct 2023)  Manuscript 
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluidrock simulation and showcase two applications to different fluidrock simulations. This approach has applications for improving model development and sensitivity analyses.