Low and volatile oil prices in the last decades have created a challenging environment for the oil and gas industry. More than ever, oil and gas companies must improve their efficiency, optimize their operations and improve capital efficiency. The reduction of lift cost per barrel, the optimization of production at no CAPEX, and lower OPEX has become vital today. In the meantime, operators still need to maintain high safety standards and accelerate the reduction of their environmental footprint.
What issues is the industry facing?
The oil & gas industry has been at the forefront of several technological transformations in the last decades from the use of HPCs, reservoir and process simulators to real-time monitoring and sophisticated control systems. The use of artificial intelligence and machine learning is the next technology frontier and a big promise of productivity for the industry. But, it faces some significant challenges.
The first challenge comes from data integration. Companies have assembled over the last years, a wealth of data coming from multiple sources such as seismic, geology, reservoir, well logs, well design, real-time production, maintenance, and economics. In order to extract value, this needs to be integrated, consolidated, and curated onto one single platform where, then, prediction and simulators aiming at fast decision making can be run.
The next challenge comes from the initial idea that a purely data-driven approach will heal all industry issues and bring enough value. Experience shows that the combination of AI and physics’ first principles demonstrates a greater model accuracy with a higher speed of simulation runtime than a data-only workflow that ignores the laws of physics & thermodynamics.
Finally, artificial intelligence faces adoption issues from users. Most artificial intelligence tools are designed for data scientists, but not for petroleum or reservoir engineers. Together with too many closed and proprietary tools, it makes the democratization of AI at the oilfield level very difficult.
What operators need?
In the Covid19 world, more than ever, digital tools are democratizing. Oil companies need to enable their workforce to access data and work remotely on the most advanced simulators. While cloud collaboration tools are massively adopted, specialty E&P software still needs to make a leap to new ways of working. It passes by advanced multi-source data integration as well as the democratization of the latest AI-powered simulation tools to allow engineers to quickly make decisions on high business impact issues.
This is why he oilfield.ai solutions hub, developed by Maillance combines machine learning and first principles of physics and thermodynamics to quickly ingest and curate all types of E&P data sources to run thousands of scenarios and identify the optimal production, drilling, and reservoir plans. Designed for engineers, it puts prediction and fast decision making tools at the fingertips of petroleum and reservoir engineers, geologists, economists, and investors.
