AI for Oil and Gas

Oil and gas exploration are an expensive process. Between the costly specialized equipment operated by highly trained scientists and technicians, the red tape associated with permitting for seismic surveys, and the environmental impact of test wells, companies have been looking for better ways to utilize their petroleum portfolios.

The large amount of data produced by past surveys and explorations can be analyzed by an AI for oil and gas system, looking for patterns and correlations that may escape other forms of analysis. AI for oil and gas can make use of the data produced by active wells during extraction to make predictions about probable reserves, provide advice about the best ways to access known reserves, and create projections about lifetime yield of current wells.

AI for oil and gas can also help reduce accidents and other disruptions by using predictive maintenance models to anticipate equipment failures and dangerous operating conditions.


Data for AI for oil and gas

Current artificial intelligence algorithms utilize deep-learning techniques to make predictions based on past observations and patterns. In order to learn what sort of connections are present in the data, industrial AI must be trained with large sets of data.

Petroleum companies can utilize extensive datasets to make decisions about how best make use of their resources. Given how critical oil and natural gas are for the energy requirements of almost all industrialized countries, it is no surprise that the size of the data from geological surveys performed by government agencies is enormous. For example, the UK’s Oil and Gas Authority has published 130 terabytes of data for public use. This single repository of information, if printed out, would fill a stack of dictionary-sized books 650 kilometers high.

In addition to traditional surveys performed by both public and private researchers and scientists, many companies are able to draw on the flood of data produced by installed sensors. Due to the inherent inaccessibility of many of the components that need to be monitored, such as drills thousands of feet underground or well heads miles underwater, many oil or gas extraction assets are already wired with sensors. An oil or gas company may have also undertaken a sensorization program for the purposes of using digital twin technology to create a digital twin of an asset, or have a digital model used for refinery modeling.

All these sources of information provide raw data, the most important input for training an AI for oil and gas system


Increased extraction and reduced disruptions

The sheer quantity of variables in petroleum extraction, as well as the volume of information sources, can reduce the science of oil and gas retrieval to rules of thumb and best guesses. One of the many benefits that AI for oil and gas brings to the table is identifying likely reserves and providing a good estimate about ease of extraction. As the low-hanging fruit of easily extracted oil and gas reserves are tapped dry, the ability to manage and utilize marginal sources is becoming an essential practice for oil and gas companies.

AI for oil and gas can also increase workplace safety using predictive maintenance algorithms. An AI can analyze past data to find the common thread between equipment failures, as well as identify conditions in which human error results in disaster. AI for oil and gas software can make recommendations about how to adjust operating conditions to increase safety and decrease asset failures. This increases reliability and decreases the human cost of oil and gas extraction.


Shifting decision-making

An oil or gas company fully embracing the possibilities afforded by artificial intelligence must undergo a shift in how decisions are made and enacted. The suite of adjustments suggested by an AI may seem counterintuitive or fly in the face of received wisdom. For example, running machinery at a lower capacity to increase lifespan may seem like common sense, but this idea may not be borne out by the data, and an AI for oil and gas will tell you as much. If a company uses digital twin software, the changes may be demonstrated on a computer before being applied on the ground.

The benefits of using AI for oil and gas aren’t limited to reducing accidents and equipment failures; industrial AI can also incorporate market conditions to make the best use of assets and resources. This type of value chain optimization adjusts operations to get the most out of any given asset.

AI for oil and gas can make sure a company doesn’t waste resources wearing out equipment if the price of the commodity being produced doesn’t justify doing so. With so many variables to juggle, a plant operator can be forgiven for being focused on the here and now. If a methane tank is leaking, stopping the leak would seem like the natural priority, but it might be more economical to simply let it outgas and wait to replace the tank at the next upgrade.



How can AI for oil and gas help in refinery optimization?
An artificial intelligence algorithm can be programmed to seek solutions for a variety of desired outcomes. Every company seeks to maximize margins, but AI for oil and gas can be programmed for a variety of desired outcomes such as reducing greenhouse gas emissions, manipulating required maintenance schedules, or maximizing the life of a reserve.

How does AI for oil and gas help a smart refinery?
For a data-led refinery, having an artificial intelligence algorithm that can independently propose ways to make the most out of a refinery is invaluable. AI for oil and gas can be specifically programmed to not only look for specific phenomena, but to discover them as well.