Using Machine Learning and AI to Solve Problems in the Oil & Gas Industry
No matter the industry in question, technology disrupts traditional business processes and enables organization-wide optimization. Artificial intelligence and machine learning, in particular, have become a fundamental component in the majority of enterprise operations.
Across industries, AI and machine learning are backed by the unspoken promise of work, without the work–embodying the term ‘advanced technology.’ And from a financial perspective, the global artificial intelligence market currently rakes in around $15 billion in revenues and is expected to increase even more, reaching almost $120 billion by 2025.
The mainstream adoption of automation has awarded businesses the ability to boost efficiency while simultaneously reducing costs and workloads–a goal in the forefront of every CEO’s mind. Knowing this, we often wonder why major industries, primarily within the energy sector, have been less than welcoming with these technologies. Diving deeper, you’ll find that the oil and gas industry as a whole has been hesitant to embrace the impending digital transformation on its horizon.
1. Basis of Machine Learning
Simply put, machine learning allows computers, programs and software to sift through and analyze massive subsets of data in a seamless, efficient way. And for oil and gas companies who gather enormous amounts of seismic and geological data daily, this streamlines and simplifies processes from the ground up, delivering a more holistic view of the available information. Not only this, but with expert algorithms, machine learning can also take both the guesswork and human intervention out of data analysis, evaluation and optimization.
Within the massive volume of data and information coming from the oil and gas industry, there exists untapped potential waiting to be uncovered and leveraged. Here are a few common problem areas machine learning could address in the oil and gas industry:
2. Geological and Seismic Modeling Data
Due to the remote nature of unearthed oil wells and seismic activity, many of the resource-intensive strategies used to model, drill and extract materials take place on difficult terrains. Because of this, it’s increasingly difficult for teams to gather and analyze information in a way that’s both fast and efficient enough to streamline complex seismic processes and maintain drilling accuracy. By utilizing machine learning and artificial intelligence, organizations gain the ability to construct detailed, accurate and consistent models in even the most isolated locations, as well as the ability to better interpret geological and seismic data in a way that allows for a more thoughtful, knowledge-backed approach to drilling.
4. Oil Well Drilling
While the oil and gas industry presents many economic benefits, it also presents a significant amount of safety concerns. There’s the obvious risk of working with large, heavy machinery in remote locations. There’s also the fact that drilling wells deeply nestled within the earth in an attempt to release fossil fuels is an extremely risky strategy. Implementing a machine-learned solution like Case-Based Reasoning (CBR), an algorithm that examines similar use cases to solve like problems, within modern rigs gives project managers real-time and projected data points to aid in overall safety as potential concerns are addressed quickly and appropriately. Predictive analytics allow algorithms to identify problem areas no matter how simple or complex, such as machine malfunctions, geological concerns and model errors, among others.
5. Repetitive Manual Tasks
AI doesn’t just improve processes relating to exploration. With predictive modeling and advanced analytical abilities, these technologies automate traditionally arduous tasks. Not only does automation make conventional processes inherently safer, but it gives workers space to shift their focus away from repetitive tasks and towards solutions that further business. By allowing a skilled workforce the freedom to focus on business-driving innovation, organizations are awarded more efficient processes and the ability to optimize their overall business model; because AI and machine learning gain a deeper, holistic view of an organizations workflow, inefficiencies are able to be pinpointed and predicted at levels that are otherwise unattainable.
According to market research, AI technologies in the energy sector are expected to reach a growth of $2.85 billion by 2022 at a CAGR of 12.66%, a major increase from its 2017 worth of $1.57 billion. While the unprecedented growth of advanced technology in oil and gas is likely due to the influx of big data and the struggle to leverage the information at hand, organizations across the industry are also feeling pressure from both the big data giants leading the charge on clean energy projects and the negative public opinion surrounding fossil fuels. As oil and gas currently account for 67% of all energy consumption in the U.S., we’re right to assume that crude oil and other substances will continue being the primary source of power nation-wide. And as the use of fossil fuels continues, a greater emphasis is placed on the industry to not only improve work and environmental safety, but to also implement more efficient processes overall.
Being based in a city where 55% of business stems directly from the oil and gas industry, we’re experiencing first-hand the pressing digital evolution waiting to play its hand on the energy sector. With almost 200,000 oil and gas employees in Houston alone, we’re excited to see the role advanced technology can play in an industry reliant on many defunct processes.
To learn more about our presence in Houston and learn more about our service offerings, contact the Skybox team today.