Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework

You, Junyu; Ampomah, William; Sun, Qian

doi:10.1016/j.apenergy.2020.115695

Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework

Journal Article · Sun Sep 06 00:00:00 EDT 2020 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2020.115695· OSTI ID:1849087

You, Junyu ^[1]; Ampomah, William ^[2]; Sun, Qian ^[2]

New Mexico Tech, Socorro NM (United States). Petroleum Recovery Research Center; New Mexico Tech, Socorro NM (United States)
New Mexico Tech, Socorro NM (United States). Petroleum Recovery Research Center

In this report, a robust machine-learning-based computational framework that couples multi-layer neural network (MLNN) proxies and a multi-objective particle swarm optimizer (MOPSO) to design water-alternating-carbon dioxide injection (CO₂-WAG) projects is presented. The proposed optimization protocol considers various objectives, including oil recovery and CO₂ storage volume. Expert MLNN systems are trained and employed as surrogate models of the high-fidelity compositional simulator in the optimization workflow. When multiple objective functions are considered, two approaches are employed to treat the objectives: the weighted sum method and the Pareto-front-based scheme. A field-scale implementation focusing on tertiary recovery in the Morrow B formation at Farnsworth Unit (FWU) is presented. The developed Pareto-optimal solutions indicate the maximal available oil production can be 1.64 × 10⁷ barrels and maximal carbon storage can achieve 2.35 × 10⁷ tons. Trade-offs factor is defined to divide the constructed Pareto front into 4 sections with the trade-off factors’ value ranges from 0.35 to 49.9. This work also compares the optimum solution found by the aggregative objective function and the solution repository covered by the Pareto front that considers the physical and operational constraints and reduces uncertainties involved by the multi-objective optimization process. Our comparison indicates multiple solutions exist to satisfy the objective criteria of the WAG design, and these results cannot be found using the traditional weighted sum method. The Pareto front solution can provide more options for project designers, but decisions regarding necessary trade-offs must be made using the solution repository to balance the project economics and CO₂ storage amount.

View Accepted Manuscript (DOE)

Research Organization:: New Mexico Institute of Mining and Technology, Socorro, NM (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FC26-05NT42591

OSTI ID:: 1849087

Alternate ID(s):: OSTI ID: 1759140

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 279; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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