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IoT Applications in the Oil and Gas Industry

AI opens up great prospects for the optimization of technological processes. Employing machine learning can help reduce expenses further by coordinating different functional groups and assets.

The past few months have been tough for the oil and gas industry after OPEC and its allies failed to agree to cut production. In the long run, this means that in addition to low prices, the sector is threatened by a much larger structural crisis. Certain trends seem to confirm this, generating uncertainty.

The International Energy Agency (IEA) has highlighted the risks the current market situation poses for vulnerable producer countries. The organization’s initial estimates of the reduction in the net profit of some producer countries in 2020 by around 50 to 85% compared to 2019 are impressive. But this drop can turn out to be much more, depending on falling demand and slowing economic growth.

The growth of the global economy is slowing as a whole and is associated with the transition to renewable energies. The cost of oil extraction is rising as old oil fields and light oil reserves run out. The third problem is the aging infrastructure of oil and gas companies. The fourth is the notable decline in the workforce, poor technical know-how and a lack of specialists. Everything that has been listed above highlights the need to develop and introduce new technologies. What are the possibilities of the application of advanced methods of artificial intelligence for the development of the oil and gas industry?

According to BNamericas, digitalization and automation, both within the company, and in exploration and extraction, are becoming paramount in reducing costs and extracting profit in the oil and gas sector of Latin America at this critical moment of falling demand for oil and falling prices.

The oil and gas industry is becoming a pioneer among industrial companies in introducing artificial intelligence (AI) in attempts to accelerate digital transformation. According to the study by Mordor Intelligence, rapid growth of the AI ​​market is expected between 2020 and 2025, the annual growth rate (CAGR) is estimated at 12.66%, with a market potential of 3.98 billion dollars around 2025 for these solutions.

If you work in data analysis or related fields, you've certainly heard that data is the new oil. In the oil and gas industry, AI has great prospects for optimizing technological processes. Employing machine learning can help reduce expenses further by coordinating different functional groups and assets. Here are some of the important technologies used in 2020:

Detecting Leak Risk Using Deep Learning Algorithms and Using Drones to Detect Pipeline Faults

Testing and technical maintenance of pipelines is complex and expensive. Checking every corner of the pipeline is an extremely expensive task for employees. With AI, in the form of deep learning algorithms, risk detection can be automated. The AI ​​detects anomalies in construction that otherwise would go unnoticed. Video recording of pipelines and facilities is performed by aerial drones. Using their cameras, they provide a video feed of any cracks or leaks which are then analyzed by the integrated AI, and the result is sent to a central server.


Machine learning algorithms help detect anomalies in the mining process

Modern oil and gas platforms are equipped with a large number of sensors. These are already collecting a huge amount of data. But detecting anomalies manually is very difficult, if not impossible. BCG Company has proven that AI can help detect early signs of failures that would normally go unnoticed, and prevent further damage. This additional supply of information makes it possible to react more quickly to problems that arise. In this way, preventing downtime and breakdowns becomes possible.

AI makes recommendations on technological process regimes

Intelligent switching of pump speeds remains a potential that has not been realized in oil extraction. Today, the operation of the pump is checked manually. This means that the limits of human perception do not allow the optimal level of oil extraction to be achieved by changing pump operating regimes in real time. Routine analysis of the operating regime and changes are made infrequently, about once a month.

There are solutions to optimize oil extraction by increasing or maintaining the volume of fluid pumped from the well through the use of AI. Human decisions are supplemented by monitoring problem wells and prioritizing recommendations for modifying pump operating regimes. The system analyzes the expected and actual increase in fluid flow, calculates actual savings, and self-learns over time.

AI selects the best drilling sites and optimizes well drilling

Artificial intelligence helps choose the optimal well path, minimizes the risk of complications during drilling. According to generally adopted best practice, there are still too many false positive results. AI is able to predict drill targets based on collected core drill data, soil samples, site surveys and strong impact data. In this way, exploration and extraction (E&P) processes can be optimized for their best efficiency.

Predict the risk of petroleum corrosion based on historical data and machine learning

Another application of AI is the generation of new ideas from existing data. This trend is becoming more and more interesting as data repositories become accessible across entire companies. All past incidents and their origins can be analyzed using a computer knowledge graph and used to make predictions and recommendations. In this way, the extraction phase can become more reliable and the storage of crude and refined oil in large reservoirs can be made safe. In addition, pipeline transport is becoming less interrupted.

Transform and optimize business models using AI

Machine learning can use existing data to create adequate and accurate business models. It allows to make profit and loss forecasts in detail. Thanks to the use of AI, this prognosis is opportune to achieve a real diet. The traditional "production at any cost" model can be modified or replaced by the "conditional production" model. This progress allows companies in the energy sector to gain flexibility and be able to adjust to low oil and gas prices.

It is not enough to have only data, they say: it must also be analyzed. But why then dwell on descriptive analysis? Why not take advantage of the wealth of valuable data available and use advanced analytical methods for the predictive and prescriptive analytics offered by machine learning. You have to move forward, take on the challenge and not be afraid to ask tough questions.

AI used on boreholes helps technicians in the field

Voice interfaces are more and more popular. Spoken AI, used in customer support, can be changed to support field technicians. They can interact with diagnostic applications using local control, using conventional natural language processing to solve specific problems under hazardous conditions. This increases the productivity and speed of the technician.

The digital transformation of oil and gas companies is impossible without a smart organization of production and supply

With the coronavirus (COVID-19) pandemic currently affecting the entire world, the entire oil sector is experiencing the biggest shock in its history. This is the reason why Latin American oil and gas companies must introduce solutions to organize suitable automation of production, logistics and services.

Machine learning can use existing data to create adequate and accurate business models in Latin America. It allows to make profit and loss forecasts in detail. Thanks to the use of AI, this prognosis is opportune to achieve a real diet. For decision-making, the main dependencies of the problem, its priorities and limits are taken into account. Then the machine learning model chooses the optimal scenario for the current situation. Its implementation is accompanied by the issuance of regular recommendations on the execution of the plan and the improvement of the technological process.

All real operations involve uncertainties in terms of time and resources, but also the risk of rejections. Very often, existing solutions ignore this uncertainty and formulate action plans that are not only unstable with regard to changes in boundaries and conditions, but whose very degree of stability remains uncertain. To face this challenge, it is recommended to use a dynamic programming horizon (as opposed to the static horizon used in most programming tools), taking into account the uncertainty of operations over time. Dynamic pricing and marginal risk control provide a complete digital analogue of the programming process that provides fully automated calculation of optimal prices for customers.

Brodies LLP, a Scottish law firm, believes that a lack of on-site staff will cause delays and interruptions in deliveries and services, but also in the operation of the platform. Logistics service providers may experience delays or be unable to make deliveries to offshore platforms. This can lead to a delay or suspension of various operations on the platform, such as technical maintenance of the installations, checks, repair and replacement of equipment, but also drilling works. This can have considerable consequences in terms of compliance with standards of safety and protection at work, and ultimately energy production.

Latin American governments must take measures to develop the digital economy. This will be an excellent possibility for carrying out projects such as those listed above, because compared to the general volume of investments the value of the introduction of digital technologies is not significant, but they can give results as soon as possible. first six months or so. In addition, these results will be very important, which will lead to an increase in labour productivity by 10-15%.

Collecting better data on operations is the first step in getting the best answers. Companies need to encourage their employees more to ask different questions, because their curiosity and creativity will uncover and highlight new potential for business. In the past, most reporting and analysis systems were created to serve professionals. Today there are many tools and languages ​​available for data analysis. Expanding access to data brings a new perspective on business. Asking questions better leads to more complete answers and much better business results. People have to be freed from their routine so that they can put all their strength into the realization of their own ideas.