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Bayesian neural network surrogates for Bayesian optimization of Carbon Capture and Storage operations
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DOI：10.1016/j.egyr.2026.109549.png)
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Abstract
En 中文
• Benchmarked GP and several uncertainty-aware neural surrogates for Bayesian optimization in CCS operations. • BO delayed net-sequestration decline by ∼10 years and increased stored CO2 from ∼1.9 to ∼2.3 Tscf. • Long-horizon scheduling ( ≈1000 controls) recovered ∼1.76 Tscf stored CO2 with five fewer producers. • NPV improved from ∼€1.15B to ∼€1.36B, reaching ∼90%–93% of the placement-optimum benchmark. • Constraint-induced control clipping and recycling-dominated solutions are key failure modes for CCS BO surrogates.
Keywords:
CCS
Bayesian optimization
Markov chain Monte Carlo
Variational inference
Gaussian process
Reservoir simulation
Journal
E
IF:
5.1
Papers:
658
Citations:
0
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Cited Papers