From 30 days to 1: The return on investing in Chevron’s Cloud Migration in real numbers

The first method in which AI changes the 150 -year -old power giant Chevron? How technical practitioners deal with data.

Abroad in the Gulf, Chevron drives oil resources from the ocean bottom in pockets and tanks that may result in results or not. The agents should be able to process Petabytes from important data – which not only provides visions around the location of the drilling, but how to do this without negatively affecting human life or environment – in the cloud and on the edge.

“The data is the final acceleration of all cases of our artificial intelligence.” Vb converting. “It is something that we converted to in a big way.”

How artificial intelligence changed the way Chevron interacts with its indescribable quantities

In 2019, Chevron cooperated with Microsoft And the SLB Oil Glads Services Company in a project called “Triple crownTo update and unify the tools -based tools. The three companies have built NATIVE AZURE applications in SLB Delfi* Exploration and Cognitive Protection (E & P) to help Chevron process, perceive, interpret, explain and gain meaningful visions from multiple data sources.

Bowman said the power giant of $ 250 billion with 1,000 employees in 180 countries around the world has a “huge amount of data there.” Although Chevron has “very strong systems of the record”, large quantities of non -structured data were present in a variety of stock points.

Over the years, Chevron has built someReally great algorithmsHe explained that this was traditionally operated on a small scale on a small scale. However, there was a growing boost to expand their scope, and to operate these algorithms on a large scale and more efficient in the cloud.

By doing this, “instead of looking at one mass three miles in the Gulf of Mexico or the Gulf of America, we can look at much larger areas we try to work on.”

Microsoft-SSLB collaboration focused on three products: FDPLAN, Drillplan and Drillops. FDPLAN uses high -performance computing (HPC) to integrate sub -surface models, allowing employees to make faster and more enlightened decisions in complex environments, and take advantage of the best available data. For example, in the Gulf, FDPLAN Chevron helps analyze different options to develop a tank so that its teams can focus on the most perfect scenarios.

Meanwhile, the Drillplan is designed for engineers who develop drilling plans, while exercises are used by the teams that dig wells.

Before the initiative, some under the surface Chevron employees spent up to 75 % of their time searching for data, Bowman noted. “We can see that the time people spend in searching for data began to decline, and the speed through which we can get visions that really accelerate,” said Bowman.

Drillplan also helped Chevron reduce the depression of wells in deep water by 30 days. For example, in Argentina, the company has reduced the time of the eight well planning course from two weeks to less than one day.

In the end, Bowman called Go to the cloud The “real power double” that allowed Chevron to enter a new stage of modernization.

Focus on standard systems

Now, while working to integrate artificial intelligence, the Bowman team focuses heavily on the model.

He pointed out that the first “Ask” was the research; They have provided a very simple use that allows people to recover the information in the law of “very complex” law. But with the involvement of users more and more, their request is increasing; In response, his team added a retrieval agent, an agent who can evaluate the results from a technical point of view and Ukrakur agent to link Monday.

He said: “We really realized early that we needed to rely heavily on the unit, because we knew that these agents would be called in other workflow tasks, based on the request.”

Another effort is “Chevron Help”, the chat interface to work on health, safety and environment standards (HSE). “We are working in a very complex industry, and the game risks are always higher,” said Bowman.

The tool provides a natural way for people to interact with documents related to critical standards and procedures, which eliminates the need to click through links or research within the documents. Therefore, for example, the user can combine all the criteria they need for drilling staff, operating staff and maintenance staff.

“We realized that we were not thinking about the problem in the way individual users thought about these things together at the same time,” Bowman said. “There was a lot of value in this integration. It really changed the way people do their work.”

Not focusing a lot on POCS

While building its programs, the Bowman team usually avoided falling into the pledge of pilots and proving the long -term POCS. “There is no value in that,” he said.

He said that the goal has always been to spread promising use of production. Everything should be linked to a Chevron summary and a strong value offer.

“We know that with a set of coordinated data and a truly enthusiastic group of users, goodwill, and narrowly specified use, there are approximately 100 % that POC will succeed,” said Bowman.

Another important element in spreading the following generation tools is to overcome the obstacle of confidence. From the point of view of changing behavior, institution leaders must be not only the expectations that the company puts on users locally and at the edge, but what these users expect in turn.

He said: “If you build these systems or tools in a way that makes the individuals who will put them on their hand they do not trust them, or they cannot trust them, or that something that hinders them, you will never get full enthusiastic publication.”

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