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dc.contributor.authorShi, Mingyu
dc.contributor.authorZhang, Fengyuan
dc.contributor.authorLu, Ruihan
dc.contributor.authorCao, Wenxing
dc.contributor.authorLiu, Xia
dc.contributor.authorLiu, Weidian
dc.contributor.authorLiu, Dianyu
dc.date.accessioned2025-04-21T10:14:20Z
dc.date.available2025-04-21T10:14:20Z
dc.date.copyright1/21/2025 12:00:00 AM
dc.date.issued2025
dc.identifier.issn2998-1638
dc.identifier.otherjertb_1_2_021008.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305772
dc.description.abstractDue to recent oil price fluctuations, oil companies have optimized assets and promoted joint ventures, leading to a need for fast, accurate oilfield asset evaluation, including development and growth costs. Oilfield development costs are dynamically influenced by internal and macroeconomic factors. Using big data analysis with a eviews economic analysis software owned by IHS (E-VIEW), a model was developed to analyze cost influencers. Research on nine main factors showed strong scientific correlations, linking expansion costs with market indexes. This provides technical support for more efficient and accurate cost estimations and predictions of oilfield assets.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleStudy of Oilfield Development Cost Market Index Modeling Based on Big Data
dc.typeJournal Paper
dc.journal.volume1
dc.journal.issue2
dc.journal.titleJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture
dc.identifier.doi10.1115/1.4067547
dc.journal.fristpage21008-1
dc.journal.lastpage21008-8
dc.page8
dc.treeJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture:;2025:;volume( 001 ):;issue: 002
dc.contenttypeFulltext


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