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Oil or the aptly nicknamed “black gold” has been fueling humanity for nearly five millennia. Since it was discovered that kerosene could be extracted from crude oil, the demand for petroleum skyrocketed and by the early 20th century, it became one of the most traded commodities in the world market. Its many applications have made this industry one that is indispensable. The market of AI for oil and gas has been estimated to reach US$2.85 billion by 2022, growing by a compound annual growth rate of 12.66 percent.
Off late, however, the rise in oil prices has been considerably slow. In fact, since the drastic drop in the latter part of 2014, the industry has been slow to recover. That drop could be attributed to a lot of reasons like changes in the political climate, a decline in demand and delay in supply. Adopting new techniques like directional drilling and hydraulic fracking to increase yield has worked. However, emerging technologies like Artificial Intelligence or AI has the potential to revolutionize the petroleum industry.
The petroleum industry has three predominant sectors – upstream, midstream and, downstream. Let’s take a look at how AI can improve each of these sectors.
One of the most expensive operations in the oil and gas industry is locating oil and gas fields. This is accomplished by drilling exploration wells in places that could have a reserve. It also involves drilling into existing wells to recover crude oil and natural gas. What makes this a cost-intensive business is the human resources required to maintain and operate the machinery. But these costs could be drastically reduced by implementing AI enabled systems. For instance, you could control the machinery and gather data about how it is working in real-time with the help of systems powered by the Internet of Things (IoT) and AI combined. This way you would know how long your machines were working and when they need maintenance and so on. AI-powered job scheduling and facilities management systems can recommend an optimal resource plan, machinery maintenance alerts etc which can help the business to run more efficiently.
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Applying reinforcement learning – a form of “semi-supervised” machine learning, in the drilling operations, can enhance the precision of drilling equipment. In this case, the movements and placement of the drills can be controlled by machine learning algorithms that are trained on historical drilling data as well as data gathered from simulated exploration. The data includes information from the drill bit, such as temperature and pressures, as well as data on the subsurface from seismic surveys. Drilling process guided by machine learning is able to understand its operating environment more accurately, leading to faster results and less wear, tear to machinery.
In the case of offshore exploration, you will need to set up an offshore facility to house the human resources who will be required to go exploring. This is where Intelligent Autonomous Vehicles could prove invaluable. These vehicles that are controlled by cognitive systems will be able to adapt quickly to the environment that it is operating in. It also can go deeper underwater than is possible for a human and scout the area for possible oil deposits and collect samples.
After the exploration, when you recover natural gas and crude oil, those resources need to be transported via pipelines, ships or fleets of trucks. The resources are then processed and stored for the downstream sector or sold in the wholesale market. This is what happens in the midstream sector. So, how can AI improve processes in this sector?
AI-powered systems can make a world of difference in refineries. When machines are powered by IoT, the data gathered can be put to good use by using predictive analytics and cognitive security. Based on the analysis, recommendations can be made about using the equipment in a way that is not taxing on it but at the same time, maintain yields at a steady rate. It is estimated that this method can decrease downtime by up to 5 percent and spare parts inventory by 20 percent.
Oil and gas industry is highly prone to accidents that can lead to hazardous effects. From lighting a cigarette to irresponsible driving, many of the common human activities could lead to accidents and heavy losses. Using computer vision technology, service station forecourts can be made more secure as the normal CCTV camera can identify the potential threats and notify security personnel to take immediate actions. Computer vision enables CCTV cameras to “think” and understand what they are recording. The algorithms are trained to look out for the potential hazard of customers or employees lighting cigarettes in the vicinity of pumps, tankers etc. Deploying solutions powered by real-time video analytics can help identify dangerous driving, criminal damage, theft, detection of unusual activities etc.
Crude oil and gas once recovered are stored in large tanks and moved through pipelines and a risk factor here is the corrosion of the equipment because of the oil itself. With the help of Artificial Neural Networks (ANN), you would be able to predict how much protection it can offer against CO2 corrosion as a function of its properties. The model should be fed with data about the physical and chemical attributes of the crude oil which are measured routinely.
This sector handles the production and marketing of the final products. From storage, the crude oil is transported to refineries where it is processed and converted to diesel, petrol, LPG and so on. The final commodities are then marketed and sold to the customer and other end users.
One of the most important materials required at refineries is water. This is, therefore, one of the areas where you could incur a lot of expense if you are not careful. You could use predictive algorithms to assess the usage and make a determination about the optimal requirement and how best to use the water so as to reduce cost and wastage.
Another very popular application that AI has offered this industry is Virtual Assistants or VA. Whenever your customers call you or ping you with queries, VAs are able to handle those simultaneously and efficiently and at all hours of the day. Only high-level concerns need to be brought to the attention of human agents who can be fully dedicated to that customer.
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With the digital age rolling in, we can expect automation across industries at an unprecedented rate. In fact, oil and gas sector’s market value is expected to rise to more than 2 billion USD by 2022. Bringing AI into the mix will only appreciate the value of the industry. Leveraging AI for oil and gas sector is helping the industry chart its future course by facilitating cost-effective and efficient operations. These and many other benefits that AI promises this industry is the reason why it is being rapidly adopted across the industry.
