據(jù)美國鉆井網(wǎng)站2023年6月5日報道，數(shù)據(jù)科學是石油和天然氣行業(yè)的未來，這是數(shù)據(jù)解決方案公司Prescient的創(chuàng)始人兼首席執(zhí)行官Andy Wang在接受美國鉆井網(wǎng)站記者采訪時表示的。

Wang告訴美國鉆井網(wǎng)站記者，“數(shù)據(jù)科學將極大地提高油氣作業(yè)的能力，從而提高效率、降低成本、增強安全性，并授權于IT團隊和現(xiàn)場團隊的員工”。

在采訪中，Wang強調，數(shù)據(jù)科學包括許多數(shù)據(jù)工具，包括機器學習，他指出這將是石油和天然氣未來的重要組成部分。當被問及他是否認為越來越多的石油公司會采用數(shù)據(jù)科學和機器學習時，Wang對這兩個問題都做出了積極的回應。

當被問及石油和天然氣行業(yè)數(shù)據(jù)科學的一些缺點時，Wang告訴美國鉆井網(wǎng)站記者，數(shù)據(jù)科學需要向數(shù)據(jù)驅動的文化轉變，需要了解如何應用正確的數(shù)據(jù)科學來解決正確的問題。

當美國鉆井網(wǎng)站記者詢問目前石油和天然氣行業(yè)對數(shù)據(jù)科學專業(yè)人員的需求是否很高時，Wang說：“我們看到需求正在增加。”

當美國鉆井網(wǎng)站記者向總部位于休斯敦的Piper-Morgan Search的創(chuàng)始人兼總裁Gladney B. Darroh提出同樣的問題時，Darroh告訴記者：“人們對該領域的興趣正在增加。”

Darroh補充說：“我預計這種興趣將轉化為對數(shù)據(jù)科學專業(yè)人員的不斷增加的需求，無論是直接招聘還是簽訂合同。”

“目前，我還沒有看到需求激增，但我已經(jīng)收到了越來越多的公司咨詢。這絕對是一股正在積聚的浪潮。”Darroh繼續(xù)說道。

位于路易斯安那州的OneSource專業(yè)數(shù)據(jù)搜索公司總裁戴夫·芒特表示，至少從該公司的油氣運營公司客戶群來看，對專業(yè)數(shù)據(jù)科學家的需求“在過去的兩到三年里有所放緩”。

芒特說：“至少從我們看到的情況來看，需求峰值是在油價暴跌和疫情暴發(fā)之前。”

他補充說：“需求似乎主要來自陸上非常規(guī)頁巖公司，這些公司處于高增長模式，擁有數(shù)百口井，他們有很多數(shù)據(jù)點可以進行抽樣和統(tǒng)計，而不是地質分析公司、生產(chǎn)作業(yè)公司和礦產(chǎn)開采公司。”

“我們不確定對專職數(shù)據(jù)科學家的需求下降是在整個能源供應鏈上，還是僅僅在我們服務的領域。”芒特繼續(xù)說道。

芒特繼續(xù)表示：“我們看到，對能夠在傳統(tǒng)崗位上整合高端統(tǒng)計和數(shù)據(jù)分析的員工的需求越來越大，比如生產(chǎn)分析師、生產(chǎn)工程師和油藏工程師。”

Andy Wang最近參加了在得克薩斯州休斯敦舉行的美國石油工程師協(xié)會(SPE)國際墨西哥灣岸區(qū)數(shù)據(jù)科學大會，美國鉆井網(wǎng)站總編也出席了此次會議。此次會議被稱為SPE-GCS數(shù)據(jù)分析研究小組的年度旗艦活動，來自能源和技術部門的代表參加了這次活動。

李峻 編譯自 美國鉆井網(wǎng)站

原文如下：

Data Science is the Future of Oil and Gas

That’s according to Andy Wang, the Founder and Chief Executive Officer of data solutions company Prescient, who made the statement during an interview with Rigzone.

“Data science will increase the capability of oil and gas operations dramatically to improve efficiency, reduce cost, enhance safety, and empower employees from the IT team to the field team,” Wang told Rigzone.

In the interview, Wang highlighted that data sciences includes many data tools, including machine learning, which he noted will be an important part of the future of oil and gas. When asked if he thought more and more oil companies would adopt data science, and machine learning, Wang responded positively on both counts.

Asked to list some drawbacks of data science in the oil and gas world, Wang told Rigzone that data science requires a shift to a data driven culture and requires the understanding of how to apply the right data science to solve the right problems.

When Rigzone enquired if data science professionals are in high demand in the oil and gas sector right now, Wang said “we see that the demand is growing”.

When Rigzone took the same question to Gladney B. Darroh, the founder and president of Houston based Piper-Morgan Search, Darroh told Rigzone that “interest is increasing”.

“I expect this interest will translate into an ever-increasing demand for data science professionals both on a direct hire and contract basis,” Darroh added.

“At the moment I haven’t seen a spike in demand, but I’ve been fielding more queries from companies. It’s definitely a wave that is building,” he continued.

Offering his view, Dave Mount, the President of Louisiana based oneSource Professional Search, told Rigzone that the demand, at least from the company’s client base of oil and gas operating companies, for dedicated data scientists “has been somewhat slow in the last two to three years”. 

“The peak demand, at least from what we saw, was prior to the oil price crash/Covid,” Mount said.

Wang took part in the Society of Petroleum Engineers (SPE) International Gulf Coast Section’s recent Data Science Convention event in Houston, Texas, which was attended by Rigzone’s President. The event, which is described as the annual flagship event of the SPE-GCS Data Analytics Study Group, hosted representatives from the energy and technology sectors. 

“The demand seemed to emanate mostly from onshore unconventional shale players that were in high growth mode with hundreds of wells under their operating umbrella, where they had a lot of data points to sample and statistically, vs. geologically, analyze production operations and mineral royalty companies,” he added.

“We’re not sure if the drop in demand for dedicated data scientists is across the energy supply chain or just in the segments we serve,” Mount continued.

“What we have seen is more demand for employees that can integrate higher end statistical and data analysis in their more traditional roles, i.e., production analysts, production engineers, and reservoir engineers,” Mount went on to state.