FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4287448254> ?p ?o ?g. }

• W4287448254 endingPage "104" @default.
• W4287448254 startingPage "96" @default.
• W4287448254 abstract "We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2 trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2 injection strategies during field-scale CO2 sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2 injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations." @default.
• W4287448254 created "2022-07-25" @default.
• W4287448254 creator A5003120570 @default.
• W4287448254 creator A5040280528 @default.
• W4287448254 creator A5083384568 @default.
• W4287448254 creator A5084050064 @default.
• W4287448254 creator A5090162964 @default.
• W4287448254 date "2022-11-01" @default.
• W4287448254 modified "2023-10-14" @default.
• W4287448254 title "Bayesian Optimization for Field-Scale Geological Carbon Storage" @default.
• W4287448254 cites W1497250338 @default.
• W4287448254 cites W1600962523 @default.
• W4287448254 cites W2001888180 @default.
• W4287448254 cites W2004465380 @default.
• W4287448254 cites W2020222307 @default.
• W4287448254 cites W2029144653 @default.
• W4287448254 cites W2032411249 @default.
• W4287448254 cites W2037372924 @default.
• W4287448254 cites W2059251959 @default.
• W4287448254 cites W2060451372 @default.
• W4287448254 cites W2072433625 @default.
• W4287448254 cites W2083684835 @default.
• W4287448254 cites W2105896816 @default.
• W4287448254 cites W2117785542 @default.
• W4287448254 cites W2129288307 @default.
• W4287448254 cites W2170480532 @default.
• W4287448254 cites W2182272378 @default.
• W4287448254 cites W2192203593 @default.
• W4287448254 cites W2215778448 @default.
• W4287448254 cites W2501741252 @default.
• W4287448254 cites W2552885917 @default.
• W4287448254 cites W2738462770 @default.
• W4287448254 cites W2784733489 @default.
• W4287448254 cites W2809658903 @default.
• W4287448254 cites W2923657103 @default.
• W4287448254 cites W2981821672 @default.
• W4287448254 cites W3016309349 @default.
• W4287448254 cites W3042021489 @default.
• W4287448254 cites W4238746485 @default.
• W4287448254 cites W770013183 @default.
• W4287448254 doi "https://doi.org/10.1016/j.eng.2022.06.011" @default.
• W4287448254 hasPublicationYear "2022" @default.
• W4287448254 type Work @default.
• W4287448254 citedByCount "0" @default.
• W4287448254 crossrefType "journal-article" @default.
• W4287448254 hasAuthorship W4287448254A5003120570 @default.
• W4287448254 hasAuthorship W4287448254A5040280528 @default.
• W4287448254 hasAuthorship W4287448254A5083384568 @default.
• W4287448254 hasAuthorship W4287448254A5084050064 @default.
• W4287448254 hasAuthorship W4287448254A5090162964 @default.
• W4287448254 hasConcept C111368507 @default.
• W4287448254 hasConcept C111919701 @default.
• W4287448254 hasConcept C119857082 @default.
• W4287448254 hasConcept C126255220 @default.
• W4287448254 hasConcept C127313418 @default.
• W4287448254 hasConcept C131675550 @default.
• W4287448254 hasConcept C132651083 @default.
• W4287448254 hasConcept C147597530 @default.
• W4287448254 hasConcept C154945302 @default.
• W4287448254 hasConcept C163716315 @default.
• W4287448254 hasConcept C185592680 @default.
• W4287448254 hasConcept C2778049539 @default.
• W4287448254 hasConcept C2778379663 @default.
• W4287448254 hasConcept C33923547 @default.
• W4287448254 hasConcept C41008148 @default.
• W4287448254 hasConcept C48044578 @default.
• W4287448254 hasConcept C49937458 @default.
• W4287448254 hasConcept C61326573 @default.
• W4287448254 hasConcept C68387754 @default.
• W4287448254 hasConcept C77088390 @default.
• W4287448254 hasConceptScore W4287448254C111368507 @default.
• W4287448254 hasConceptScore W4287448254C111919701 @default.
• W4287448254 hasConceptScore W4287448254C119857082 @default.
• W4287448254 hasConceptScore W4287448254C126255220 @default.
• W4287448254 hasConceptScore W4287448254C127313418 @default.
• W4287448254 hasConceptScore W4287448254C131675550 @default.
• W4287448254 hasConceptScore W4287448254C132651083 @default.
• W4287448254 hasConceptScore W4287448254C147597530 @default.
• W4287448254 hasConceptScore W4287448254C154945302 @default.
• W4287448254 hasConceptScore W4287448254C163716315 @default.
• W4287448254 hasConceptScore W4287448254C185592680 @default.
• W4287448254 hasConceptScore W4287448254C2778049539 @default.
• W4287448254 hasConceptScore W4287448254C2778379663 @default.
• W4287448254 hasConceptScore W4287448254C33923547 @default.
• W4287448254 hasConceptScore W4287448254C41008148 @default.
• W4287448254 hasConceptScore W4287448254C48044578 @default.
• W4287448254 hasConceptScore W4287448254C49937458 @default.
• W4287448254 hasConceptScore W4287448254C61326573 @default.
• W4287448254 hasConceptScore W4287448254C68387754 @default.
• W4287448254 hasConceptScore W4287448254C77088390 @default.
• W4287448254 hasFunder F4320306076 @default.
• W4287448254 hasLocation W42874482541 @default.
• W4287448254 hasOpenAccess W4287448254 @default.
• W4287448254 hasPrimaryLocation W42874482541 @default.
• W4287448254 hasRelatedWork W2950792054 @default.
• W4287448254 hasRelatedWork W3007674363 @default.
• W4287448254 hasRelatedWork W3104422856 @default.
• W4287448254 hasRelatedWork W3134690064 @default.

• next