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迎校慶六十七周年系列學(xué)術(shù)活動(dòng)之三十三

來(lái)源:全國(guó)重點(diǎn)實(shí)驗(yàn)室A403     報(bào)告人:楊濤    審核:李早元    編輯:姜博     發(fā)布日期:2025年11月18日    瀏覽量:[]

報(bào)告題目:通過(guò)新型鉆屑分析最大限度地了解油藏流體 (Maximizing Reservoir Fluid Insights Through Novel Drill Cuttings Analysis)

報(bào) 告 人:楊濤

報(bào)告時(shí)間:11月24日  10:30-12:00

報(bào)告地點(diǎn):全國(guó)重點(diǎn)實(shí)驗(yàn)室A403

報(bào)告人簡(jiǎn)介:

楊濤博士現(xiàn)任挪威Equinor(原挪威國(guó)家石油公司)首席科學(xué)家,兼任挪威大陸架能源資源可持續(xù)利用國(guó)家中心(NCS2030)副主席。2021年當(dāng)選挪威工程院院士,2023年當(dāng)選挪威皇家科學(xué)與文學(xué)院院士。長(zhǎng)期從事提高原油采收率、儲(chǔ)層流體性質(zhì)及CCUS技術(shù)研究,研發(fā)實(shí)時(shí)布井完井技術(shù)及實(shí)時(shí)流體識(shí)別機(jī)器學(xué)習(xí)技術(shù),應(yīng)用于Equinor全球油氣項(xiàng)目,獲2022年世界石油最佳數(shù)據(jù)管理和應(yīng)用解決方案獎(jiǎng)。擔(dān)任國(guó)際石油工程師協(xié)會(huì)(SPE)杰出講座專(zhuān)家,獲SPE萊斯特·尤倫技術(shù)卓越獎(jiǎng)、北海地區(qū)油藏工程技術(shù)獎(jiǎng)及挪威工程院最高榮譽(yù)獎(jiǎng),主導(dǎo)Sleipner CCS項(xiàng)目,兼任《SPE油藏評(píng)價(jià)與工程》、《地質(zhì)能源科學(xué)與工程》編委。

內(nèi)容簡(jiǎn)介:

This presentation outlines a transformative approach to reservoir fluid identification using real-time drill cuttings analysis. Traditionally, obtaining crucial fluid data relies on scarce and expensive PVT samples. In contrast, drill cuttings are abundant and cost-effective, but conventional methods struggle with contamination from oil-based drilling mud. The solution is the innovative application of Gel Permeation Chromatography with Ultraviolet detection (GPC-UV). Building on earlier work, the research introduces key advancements: a 4-column GPC-UV system and multi-wavelength iso-absorbance analysis. This multi-wavelength technique is particularly powerful, as it can distinguish between oil and gas samples and predict a wide range of fluid properties directly from cuttings. This methodology effectively treats "every cutting as a PVT sample, "unlocking significant commercial value. Successful field applications in well placement and reservoir management have validated its effectiveness. The GPC-UV method provides a practical tool for estimating fluid properties like API gravity and paves the way for future integration with AI, marking a substantial leap forward in reservoir characterization.

主辦單位:油氣藏地質(zhì)及開(kāi)發(fā)工程全國(guó)重點(diǎn)實(shí)驗(yàn)室

SPE成都分會(huì)

科學(xué)技術(shù)發(fā)展研究院

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