UDK 622.276:004.8
Tretiakov Ilia
MBA, The Kellogg School of Management at Northwestern University
USA, Evanston
APPLICATION OF AI FOR PRODUCTION PROCESS OPTIMIZATION: INTERNATIONAL EXPERIENCE AND PROSPECTS IN THE OIL AND
GAS INDUSTRY
Abstract: This article explores the application of artificial intelligence (AI) in the oil and gas industry. It analyzes various AI technologies, such as machine learning models, predictive analytics, digital twins, and robotic systems, that help improve efficiency, reduce operating costs, and improve safety in production processes. It provides examples of leading global companies that use AI tools to optimize oil and gas production, refining, and transportation processes. It demonstrates how the implementation of AI contributes to the sustainability and competitiveness of oil and gas enterprises in the context of modern market and natural challenges.
Keywords: Artificial intelligence (AI), oil and gas industry, predictive analytics, digital twins, machine learning (ML), international experience, process optimization.
INTRODUCTION
The oil and gas industry, as one of the most capital-intensive and important sectors in the global economy, faces continuous challenges related to optimizing production processes, reducing costs, and improving safety. With technological advancements, artificial intelligence (AI) becomes a key tool in addressing these issues. It offers new approaches to automation, data analysis, and risk management. Its implementation not only enhances process efficiency but also enables companies to adapt to changing market conditions and potentially reduces maintenance and modernization costs.
One of the primary areas of AI application in the oil and gas sector is predictive analytics, which is based on machine learning (ML) methods and big data analysis. These methods allow for predicting equipment failures, identifying hidden data patterns, and recommending optimal conditions for production operations. Such systems are widely used in various oil and gas corporations to monitor equipment conditions. This approach reduces the frequency of unscheduled shutdowns and minimizes repair costs.
ХОЛОДНАЯ НАУКА №9/2024
The usage of AI in the oil and gas industry is also tied to the need for quick decision-making and adaptation to challenging extraction conditions. Technologies like digital twins and robotic systems enable virtual experiments and real-time asset monitoring, which is especially significant for safe and sustainable operations. The goal of this study is to explore the use of AI technologies in optimizing production processes. The article examines the global experience of AI adoption in the oil and gas industry, analyzing key AI methods used to improve the efficiency of organizations involved in the extraction, processing, and transportation of oil and gas.
MAIN PART. AI METHODS AND TECHNOLOGIES FOR PROCESS
OPTIMIZATION
Technologies based on AI has firmly established itself in the oil and gas industry through innovative and effective solutions for optimizing production processes. Such tools can also be used to solve various financial and strategic problems not only in companies extracting and processing natural resources, but also in other sectors [1]. According to statistics for 2024 [2], the most implemented AI technology is predictive analytics. It is becoming increasingly popular in oil and gas structures, while process automation and predictive maintenance are also widely used to optimize processes (fig.
Business analysis or predictive analytics Process automation
Geology or reservoir engineering Predictive maintenance
Supply chain optimization Drilling and completion
Accounting Other
0 10 20 30 40 50 60 Figure 1. Areas of AI use in the activities of oil and gas companies in the USA in
2024, %
Various ML and predictive analytics technologies play an important role in optimizing production processes in the oil and gas industry, as they can identify hidden
patterns in huge volumes of data and generate forecasts based on them. In the oil and gas sector, such methods allow companies to predict equipment failures, improve productivity, and identify the most promising areas for oil and gas production (table 1).
Table 1. Application of AI to optimize oil and gas production processes [3]
Upstream activity Developed tool AI approach Main effect De-risking
Geological assessment A tool for automatically mapping reservoir rock parameters over an oil area Nongradient optimization, interpolation techniques Sped up the manual mapping procedure to several seconds Removing human mistakes that cause incorrect mapping. Means a more precise determination of the proper hydrocarbon targets
Geological information may be extracted from well records using this tool Gradient boosting 100+ times speedup
Rock typing software based on photographs of rock samples taken from wells Deep neural networks ~1,000,000+ times speedup
Drilling Detects the kind of drilled rock and likely failure using real-time drilling telemetry Combination of ML algorithms Up to 20% time saving and up to 15% money savings at well construction Increasing the amount of contact between the wellbore and the pay zone
Reservoir engineering A tool for speeding up traditional reservoir simulations Deep neural networks Accelerating by a factor of 2002000 Making it feasible to filter through a significantly larger number of field development scenarios in order to find the best one
Production optimization Technique for predicting the efficacy of well treatment initiatives objectively Gradient boosting + expert-based feature selection 100+ times faster estimation of the well treatment effect Up to a 20% increase in the marginality of campaign investments
One of the most popular applications of predictive analytics is maintenance forecasting. Equipment on oil platforms and refineries operate in extreme conditions, which increases the risk of breakdowns and, therefore, unplanned downtime. Using predictive analytics based on ML allows monitoring the condition of equipment in real time and detecting signs of impending failures long before they occur.
Models based on ML are also used to optimize production. Oil and gas companies can process data from sensors installed on drilling rigs and in wells to adapt production parameters to changing conditions. This allows them to increase production and prevent well productivity declines. An example is the use of ML to predict changes in rock characteristics and pressure to precisely adjust drilling parameters [4].
Different ML models can also analyze geological data to identify potential drilling areas. These technologies process large amounts of data from seismic surveys and past drilling to identify promising areas where there is a high probability of finding oil or gas. Algorithms trained on data from previous wells can generate predictions about the likelihood of successful drilling and predict potential risks.
Another important function of predictive analytics is the modeling of different scenarios under uncertainty. Based on historical data and predictive models, companies can conduct scenario analyses to optimize their strategies. It allows taking into account many variables and building models that predict the likely behavior of oil prices, changes in consumption, or the need for production capacity. Such data helps oil and gas companies adapt their strategies to changing market conditions.
Computer vision (CV) plays a key role in improving safety at manufacturing facilities. Using cameras and AI algorithms, companies can automate the process of monitoring production sites, detect leaks, and monitor safety compliance. Cameras with CV can, for example, recognize the presence of protective equipment on workers, monitor the operation of machinery, and warn of potential threats. For infrastructure monitoring, drones equipped with cameras and CV systems are used to inspect hard-to-reach areas such as pipelines and tanks, which increases efficiency and reduces inspection costs.
The Internet of Things (IoT) combined with AI and real-time data analysis is one of the promising areas for the oil and gas industry. Sensors installed at production facilities collect data on temperature, pressure, vibration levels and other parameters, which are transmitted to servers for subsequent processing by AI algorithms. This allows monitoring the condition of equipment in real time and making decisions based on relevant data. In particular, IoT helps in the development of smart wells, where
sensors allow continuous measurement of production parameters and adjustment of production conditions to achieve maximum productivity. This significantly increases the efficiency of processes and reduces the risk of emergency situations.
A digital twin is a virtual copy of a physical object or process created using data and AI. In the oil and gas industry, digital twins are used to model processes and conduct «virtual experiments» in conditions as close to real as possible. With digital twins, companies can simulate various scenarios, including production forecasting, optimizing transportation routes, and testing new extraction methods. Such a system not only saves resources and time, but also significantly reduces the environmental impact by minimizing the need for testing on real objects [5].
Incorporating robotics into manufacturing processes is another area where AI can be used. Robotic systems perform complex and labor-intensive tasks such as drilling, welding, and repair work, reducing risks to workers and increasing the accuracy of operations. AI in this area is responsible for controlling robots and optimizing processes, which is especially important when working in hard-to-reach or dangerous places such as deep-sea wells or arctic areas. In addition, automated drilling rigs equipped with AI are able to independently adjust drilling parameters based on realtime data. This increases drilling efficiency, reduces production costs, and minimizes human error.
Various AI technologies, including ML models, predictive analytics tools and CV, in the oil and gas industry not only improve the productivity and reliability of equipment, but also help companies minimize risks and optimally use resources. Such effective methods become the basis for the industry's transition to more accurate and cost-effective production management methods.
INTERNATIONAL PRACTICE OF IMPLEMENTING AI IN THE OIL AND
GAS INDUSTRY
With the global development of technologies and the introduction of innovations, the oil and gas industry are increasingly using AI to improve the efficiency and sustainability of its processes. This trend is confirmed by a number of examples among
major global oil and gas companies that have achieved tangible results thanks to the implementation of AI technologies.
Total S.A., one of France's leading oil and gas companies, has partnered with Google Cloud (United States) to develop AI-powered solutions that optimize the analysis of subsurface data in exploration and production [6]. This collaboration has enabled Total to use cutting-edge technologies such as computer vision to interpret subsurface images obtained during seismic surveys. Algorithms created in collaboration with Google Cloud automate the processing of technical documentation using natural language processing technologies, which significantly speeds up work with data. Along with this collaboration, the oil and gas company use ML to determine the characteristics of oil and gas fields, as well as predictive maintenance for turbines, pumps, and compressors, which helps save huge amounts of money by reducing unexpected downtime and improving equipment reliability.
Aker BP, an independent oil and gas company from Norway, is partnering with SparkCognition (United States) to implement a predictive maintenance system on its Tambar offshore platform. With unplanned downtime costing $2-3 million per day, Aker BP decided to improve productivity with the help of AI that can predict equipment failure. SparkCognition developed a model of normal behavior for a multichannel pump and implemented it into predictive maintenance software that analyzes sensor data and promptly signals potential failures. In one case, the software predicted the possibility of a pump failure due to a seal defect, which helped Aker BP avoid losses of more than $10 million [7].
American company Chevron uses AI not only for logistics, but also for advanced data analysis in exploration and field evaluation. In particular, the company actively uses digital twins to monitor its gas plants and other facilities. These virtual models allow engineers to predict equipment behavior in real time, optimize production processes, and identify potential problems before they occur. At the Wheatstone platform in Australia, a digital twin helps remote teams identify and fix problems faster, which improves operational efficiency and reduces downtime [8]. Chevron also uses ML technologies and advanced seismic processing, such as ocean bottom sensors
and full wave inversion modeling, to improve the understanding of complex geological conditions, which allows for optimized asset management from exploration to reservoir management.
Each of these examples shows how the implementation of AI can improve the efficiency of production and exploration processes in the oil and gas industry, reduce costs and improve the safety of operations. In this regard, international practice confirms that the implementation of AI is an integral part of the transformation of the oil and gas industry in the coming years.
CONCLUSION
The application of AI in the oil and gas industry is proving to be highly effective and capable of improving various production processes. Global examples such as TotalEnergies' collaboration with Google Cloud to optimize subsurface data analysis and Aker BP's implementation of a predictive maintenance system using SparkCognition demonstrate the broad capabilities of AI to improve safety, reduce costs, and increase productivity. Such technologies enable companies to predict and prevent equipment failures, as well as plan exploration and production processes more effectively, adapting to dynamically changing market and environmental conditions.
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