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dc.contributor.authorZhang, Yanhui
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKatterbauer, Klemens
dc.contributor.authorMarsala, Alberto
dc.date.accessioned2022-01-13T13:12:06Z
dc.date.available2022-01-13T13:12:06Z
dc.date.issued2021-12-15
dc.identifier.citationZhang, Y., Hoteit, I., Katterbauer, K., & Marsala, A. (2021). Artificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs. Day 2 Mon, November 29, 2021. doi:10.2118/204604-ms
dc.identifier.doi10.2118/204604-ms
dc.identifier.urihttp://hdl.handle.net/10754/674946
dc.description.abstractSaturation mapping in fractured carbonate reservoirs is a major challenge for oil and gas companies. The fracture channels within the reservoir are the primary water conductors that shape water front patterns and cause uneven sweep efficiency. Flow simulation for fractured reservoirs is typically time-consuming due to the inherent high nonlinearity. A data-driven approach to capture the main flow patterns is quintessential for efficient optimization of reservoir performance and uncertainty quantification. We employ an artificial intelligence (AI) aided proxy modeling framework for waterfront tracking in complex fractured carbonate reservoirs. The framework utilizes deep neural networks and reduced-order modeling to achieve an efficient representation of the reservoir dynamics to track and determine the fluid flow patterns within the fracture network. The AI-proxy model is examined on a synthetic two-dimensional (2D) fractured carbonate reservoir model. Training dataset including saturation and pressure maps at a series of time steps is generated using a dual-porosity dual-permeability (DPDP) model. Experimental results indicate a robust performance of the AI-aided proxy model, which successfully reproduce the key flow patterns within the reservoir and achieve orders of shorter running time than the full-order reservoir simulation. This suggests the great potential of utilizing the AI-aided proxy model for heavy-simulation-based reservoir applications such as history matching, production optimization, and uncertainty assessment.
dc.publisherSPE
dc.relation.urlhttps://onepetro.org/SPEMEOS/proceedings/21MEOS/2-21MEOS/D021S001R005/474456
dc.rightsArchived with thanks to SPE
dc.titleArtificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs
dc.typeConference Paper
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.conference.dateNovember 2021
dc.conference.nameSPE Middle East Oil & Gas Show and Conference
dc.conference.locationevent canceled
dc.eprint.versionPre-print
dc.contributor.institutionSaudi Aramco
kaust.personZhang, Yanhui
kaust.personHoteit, Ibrahim
refterms.dateFOA2022-01-13T13:29:47Z
dc.date.published-online2021-12-15
dc.date.published-print2021-12-15


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