Upstream Digitalization Is Proving Itself in the Real World

Abstract

At its most basic, digitalization is about creating value from data. For the upstream industry, where even a 1% improvement in efficiency or production can drive huge financial results, there is no question that the size of the digital prize is huge. So are the challenges. “The demand people increasingly make now when presented with … digitalization … is, ‘prove it works,’” said Gareth Smith, head of consulting for Sword Venture. “They want to see solid examples of real value, not just a Power-Point promise.” And, that is starting to happen at growing scale. There is a reason that 92% of survey respondents for DNV-GL’s 2020 oil and gas industry outlook said they expect to increase or maintain levels of spending on digitalization in 2020: Digital technologies combined with data-driven insights are transforming operations, improving efficiency, boosting agility, and enabling strategic decision-making at numerous points along the E&P value chain (Fig. 1). For example, scaling digital solutions across Equinor’s global portfolio faster than expected delivered cash flow impact of more than $400 million in 2019—primarily from earlier startup of Johan Sverdrup and increased uptime on assets connected to its integrated operations center—and enabled the company to increase its 2025 efficiency improvement ambition from $2 billion to $3 billion. Using the Cloud To Turn Big Data Into Smart Data One of the key enablers of data-science-driven technologies for the industry is its ability to convert big data into “smart” data, said Vural Sander Suicmez, Middle East region manager for QRI. The ability to not only feed data into the cloud, but also retrieve information from it, makes it possible to conduct analytics that add a layer of insight on top of the data. “The cloud is opening all sorts of opportunities to do data analytics on it, and not just from a single facility, where most of our work is done today, but globally,” said Ed Moore, senior technologist for Chevron’s Emerging Technologies Group. “Looking at a larger sample size can provide new insights about how to operate.” Since August 2019, Chevron has leveraged edge computing and data analytics to directionally drill more than 3 million ft remotely in its Mid-Continent business unit and is now using remote drilling for 100% of its directional drilling in the unit. The company is also using predictive analytics with either artificial intelligence (AI)-based algorithms or first-principle engineering models to monitor its wells in remote locations and to know earlier and respond faster if they start to lose production. Predictive maintenance with digital twins. “Digital twin is a broad term,” explained Moore. “The engineering version of digital twin is an example of AI that allows us to simulate how a piece of equipment is working. This helps us determine when the equipment will start to not operate as efficiently as needed, which in turn helps us schedule maintenance to maximize the life of the equipment while saving downtime,” Moore said. Through a partnership with Emerson and Microsoft, Chevron is applying the engineering digital twin concept to extend the life of its heat exchangers to a major turnaround. The exchangers are designed with software that embeds all the engineering aspects, then tells how much heat they can exchange, based on engineering thermodynamics.