ASCG Month in Review: June 2018
In Case You Missed It
The relationship between the University of Alberta and Tsinghua University has led to the creation of the newly founded TEC Edmonton-TusStar Accelerator Exchange. The accelerator is meant to help facilitate penetration into the Chinese market for Alberta technology innovators.
The Long Lake oilsands project is about to undergo a $400 million dollar expansion adding three steam-driven well pads for the transport of bitumen. The project has already been approved by the Alberta Energy Regulator.
As a part of CN’s $3.4 billion dollar capital program for 2018, $320 million dollars of that budget has been allocated to infrastructure upgrades and further railroad extensions in the province of Alberta. These upgrades are in anticipation to meet demand for grain, energy, and forest products.
After suspension of Alberta wheat, South Korea continued their imports following an 8 week hiatus. The suspension was due to unauthorized genetically modified plants being found, however, this report was later changed following testing of wheat coming from the province of Alberta.
Enbridge shares jumped 7% after receiving approval for the biggest and most expensive project in its backlog. The company announced its needs to replace the pipeline over concerns of corrosion and cracking, which have forced it to run the pipeline at half its original capacity.
Feature Story: Disruption in Oil and Gas: How is AI being utilized in Alberta’s largest industry?
In June, the price of WTI reached $74 for the first time since November 2014, largely due to geopolitical factors causing concerns over global supply. Despite a rebound in the market, questions arise over how oil and gas companies will position themselves for the coming years. It is a volatile industry after all. Historically, capital investments have been closely tied to the price of crude oil meaning energy producers are now thinking about where to allocate their investment dollars to maximize returns.
Geopolitical uncertainty and concerns over the environmental impact of energy production have put pressure on oil and gas producers. This has created a need to optimize operations and become adaptable to external factors while maintaining profitability and minimizing environmental impact.
In a 2018 Energy Outlook white paper, Deloitte executive John England wrote:
“Companies that are willing to innovate and invest can unlock tremendous value and may remain financially strong regardless of what happens to global supply and demand trends”
The oil and gas industry has long invested in new technologies to improve their bottom line. The adoption of technologies such as hydraulic fracturing and directional drilling have increased yields, and the industry is constantly looks for ways to boost business. Moreover, every oil and gas company wants to increase production without increasing resources. Many see the adoption of AI as the next step in the digital revolution, and for good reason. There are three main catalysts driving this transformation:
- Abundance of Data: As companies begin to gather data at an exponential pace, it becomes increasingly important to generate the tools necessary to harness it. The usage of data gives companies the ability to revolutionize their operations through increased optimization and efficiency.
- Maturity of AI: The technology isn’t science fiction anymore. In the past, AI could only advise companies in retrospect. This means that companies can only derive insights on events that happened in the past. Nowadays, predictive analytics uses techniques from data mining, statistical modelling, and machine learning to analyze current data and make predictions about the future.
- Capital Allocation: Oil and gas companies are increasingly investing more in artificial intelligence. Exxon Mobil announced a collaboration with MIT in regards to developing AI technologies for their deep sea operations. As the “more for less” approach becomes increasingly important and it becomes the norm for companies to become technologically advanced, it is expected for companies to invest further capital into areas underlying artificial intelligence. A study conducted by Market Insider states that AI investments in oil and gas are projected to reach USD 2.85 billion by 2022.
A variety of different approaches have recently been made to integrate artificial intelligence with the energy sector. In the segment below, we’ve highlighted only a few of these current applications in oil and gas, across the entire value chain.
Also known as the exploration and production (E&P) sector, this segment includes locating and extracting oil and natural gas from the ground.
Well Service: Simply looking at the number of stops a pumper makes and the distance between wells is an inefficient method of quantifying well service, as some wells are a higher priority than others. AI tools can leverage event-based algorithms and location awareness aid to allow pumpers to prioritize certain wells and enhance their productivity.
Failure Prediction: Equipment failure is costly. Data collected from equipment through the integration of hardware and instrumentation can allow companies to predict failures before they occur, saving millions in repair costs. This is achieved by combining physical modelling and machine learning, as done by Silicon Valley’s Tachyus.
The midstream sector includes processing, storing, and transporting crude oil and natural gas.
Forecasting and Optimization: The midstream sector is expected to grow at a CAGR of 12.66% between 2017 and 2022, largely driven by the increase in shale oil and gas production in the US. There is demand for an expansion of pipelines, rail, tankers, and terminals where AI could be used to gather transportation data and provide forecasting and optimization to allow for better decisions and operating performance.
The downstream sector includes refining crude oil, and selling and distributing its products.
Water Management: Refineries use large volumes of water with most going towards cooling systems. Although cooling water is primarily recycled, up to 50% can be lost due to evaporation. The ability to effectively manage water usage presents a significant opportunity for cost reduction, which US-based Digital H2O have capitalized on. Digital H2O’s solutions leverage a proprietary data model and predictive algorithms for end-to-end management of water in oil and gas production. This aids in water planning, forecasting, and transportation optimization.
It is evident that the implementation of AI can transform the oil and gas industry. With energy producers facing commodity price volatility, the pressures of achieving more with less, and increasing environmental concerns, there is a dire need for a solution. AI is rapidly maturing as a technology and can simply overlay on existing assets, making it a prime candidate to alleviate those pains. With an unclear path ahead, some questions still remain. How soon will this technology implemented on a large scale? How will this transformation affect the way companies organize their workforce? Should companies develop AI solutions in-house or should the work be contracted out to research experts?
Those questions, and many more, will soon be answered as we see more companies implement AI-based solutions in an industry-wide digital revolution.