Importance Of Internet of Things (IoT) And Artificial Intelligence (AI) In Oil & Gas Sector

The fourth industrial revolution which also symbolized as Industry 4.0, accounts for implementing automation processes and value creation of traditional manufacturing methods and practices using modern and smart technologies. The Oil & Gas industry is setting up to transform and create more efficient, sustainable, and competitive products and processes through digitalization and automation. The artificial intelligence and internet of things offer a competitive edge that enables oil & gas producers and manufacturers to escalate the well and field productivity. The artificial intelligence and internet of things in the oil & gas industry has been trending over the past five years. The use of AIs and IoT to enhance the methods and processes of oil & gas systems.

Additionally, AI enables robotic applications in the oil rigs and refining oil well imaging activities. Numerous oils & gas start-ups have started developing blockchain solutions to provide transparency and visibility across the whole oil & gas value chain. The major drive for using the internet of things (IoT) and artificial intelligence (AI) is to enable remote operations, improve worker safety, accessibility to virtual training and maintenance operations.

The internet of things (IoT) in oil & gas sector is the network of physical objects connected to the internet. Items including wearable devices, equipment, vehicles, building electronics, and others could be embedded with any electronics, sensors, network connectivity, and software. The implementation of IoT develops better field communications, reduces maintenance cost, digital oil field infrastructure, real-time monitoring, mine automation, reduced power consumption, security of assets, and better safety, resulting in higher productivity in the oil and gas sector.

Monitoring tanks to understand the equipment performance and examining the inventory levels are essential tasks in the oil and gas sector. Sensor-based tank monitoring offers better data for cloud-based digital dashboards to monitor real-time equipment performance and inventory levels. Internet of things in oil and gas enables monitoring in real-time, resulting in reduction for pipelines check and escalating the production in the refineries. The remote systems are configured with the system to send alerts over smartphones or other devices when equipment maintenance is required or pipe pressures fall outside the norm. The sensors closely monitor the inventory levels in the onshore oil tanks thereby dispatching trucks when the tanks are needed to be emptied. Sensors can also monitor the performance of above-ground pumps to increase the awareness of the maintenance teams for actual or potential problems, and provide employees warning signs for possible faults to prevent fatalities or injuries. Real-time tracking sensor in the oil tanks notify and enable the continuous pumping by optimizing the inventories for transportation and downtime costs are minimized. Cloud-based digital visualizations systems convey information in an insightful manner that drives improved decision-making.

Using IoT in oil and gas systems reduce the risk of complex and demanding drilling operations which allows to efficient monitoring and management of the systems. This reduces error and the risk to equipment or resources and reduces incidence of human injury and death. Internet of things helps to protect the environment and it is important to use all the energy providers to focus more on environmental concerns and social issues. Internet of things in oil and gas sector helps reduce drilling operations’ carbon footprint. Leveraging IoT to track waste and hazardous spills to make quicker business decisions.

Inexpensive internet of things acoustic sensors in the oil field continuously analyses oil composition (oil, water, gas, etc.) within pipelines, while laboratory tests from simulated field conditions and selected sensors exhibiting optimal oil flow performance enhance the readings. In addition, statistical models estimate the composition and flow rates to continuously improve planning for subsequent operations and reduce expensive equipment use.

IoT also helps to leverage data for the midstream companies to improve the information management systems, predictive asset management, asset risk management, and asset management planning. These companies use IoT as a means to optimize logistics. It helps to maintain a strict set of the rules for oil and gas transportation and temperature regime and routes.

AI systems are used to automate and optimize the initial stages of the exploration and production lifecycle. This includes geology, seismology, petrophysics, drilling, production, and reservoir. They have the potential to enhance productivity, minimize operational costs, and reduce risks. Ability to identify drilling locations and precise targeting helps to maximize the ROI on any drilling activity. Oil and gas is a sector with dynamic landscape. It includes exploration and production, midstream and refining, along with oil field services and equipment. In exploration, AI, operators can understand their reservoirs and minimize geologic risk. Operators use it for better exploration and production decisions, and optimize acquisition strategies with better forecasts of lease transaction prices. Artificial intelligence has proven to be very effective at improving well design, drilling execution, and completion execution. Artificial intelligence helps the producers maximize the ROIs for each well by optimizing well placement and well spacing it to maximize resource recovery. They can also use it to design wells to optimize recovery and total cost, and predicting sub-surface risks. In exploring the oil and gas sector, accurate of well production forecast in the daily, monthly, and lifetime periods is important for successful production. Machine learning helps to optimize pressure, flow rates, and various variables for maximizing lifetime well production. Additionally, the anomaly detection capabilities allow operators to anticipate well issues in advance before they cut off production. AI helps operators forecast product flow, demand, and price to make long-term capital decisions based on product supply-demand imbalances and local market price spreads. They can also model right-of-way (ROW) acquisition costs and improve planning and routing with more informed estimates of easement costs.

AI has triggered substantial transformation and changes in the competition rules. Instead of relying on traditional and human-centred business processes, companies from these industries create value using AI solutions. Advanced algorithms trained on large and useful datasets and continuously supplied with new data drive the value creation process. In Oil and gas sector, the AI's primary target (and other digitalization) efforts is to improve efficiency. Various upstream activity such as Geological assessment, Drilling, Reservoir engineering, and Production optimization are done by the usage of AIs. It helps to accelerate the process while de-risking it mainly.

The major applications of artificial intelligence for the oil and gas industry are machine learning (ML) and data science. ML helps transform oil and gas discovery and development by enabling companies to collect huge real-time information and extract useful insights out of large data sets. The oil and gas sector faces many challenges in recognizing inappropriate threading in pipelines and faults in error-prone mechanisms. The defects that are not detected in upstream are later usually realized in the production process. This leads to higher losses, damage and incurs a heavy cost to a company. The adoption of AIs and deployment of the computer-vision-based system helps to check the quality of production. They also offer detailed information about defects in analytics. The oil & gas sector requires the AI application in its numerous divisions. Industry is needed to optimize its production due to fluctuating oil pricing. It is also essential to extend and to improve the life of the oil well. Various factors are need to be taken care of like pressure, flow rates, and others. The application of AI and ML algorithms helps collect data from various installed sensors and other devices, generate real-time updates as well as helps to maintain the most favourable operation settings.

The oil & gas companies and manufacturers are using artificial intelligence along with their existing digital infrastructure to grow its efficiency and productivity in the exploration operations. The use of machine learning and predictive analytics helps the industry to improve operations and the disruptive technology.

Some of the major innovations by companies globally include, in 2019, Microsoft formed a strategic partnership with AI developer C3.ai and O&G company Baker Hughes in order to bring enterprise AI to the energy industry with its Azure cloud computing platform. The alliance would allow customers to streamline the adoption of artificial intelligence technology which is designed to address issues such as energy management, inventory, equipment reliability, and predictive maintenance. Likewise, in January, 2019, BP invested US$5 million in a start-up Belmont Technology, which will enable them to tap into the cloud-based machine learning platform. BP will feed in data to the platform regarding geology, geophysics, reservoir and historic project information, which Sandy will then link together to identify new connections and workflows.

 

According to US Energy Information Administration, in 2020, the petroleum consumption averaged about 18.12 million barrels per day. Fuel oil which are distillate is the second most-consumed petroleum product in the US. The distillate fuel oil includes heating oil and diesel fuel. Diesel fuel is used in the diesel engines of heavy construction equipment, trucks, buses, tractors, boats, trains, automobiles, and electricity generators. Heating oil, also called fuel oil, is used in boilers and furnaces to heat homes and buildings, industrial heating, and produce electricity in power plants. The total distillate fuel oil consumption in 2020 averaged around 159 million gallons per day. The leading oil-producing area in the US in 2019 was Texas with 5.07 million barrels per day, followed by offshore federal zone of Gulf of Mexico with 1.90 million barrels per day, North Dakota 1.42 million barrels per day, and New Mexico 0.90 million barrels per day. Thus, the adoption of AI and IoT systems by major manufacturers has helped them increase the products' efficacy. For instance, ExxonMobil, an oil and gas giants, headquartered in the US has invested in various AI projects. In 2016, the industry titan collaborated with the Massachusetts Institute of Technology (MIT) to design AI robots for ocean exploration. National Oceanic & Atmospheric Administration, stated that, the naturally occurring oil seeps in the seafloor are the largest source for oil entering the world’s oceans, which accounts to nearly half the oil released in the ocean environment each year. ExxonMobil’s robots which are AI-powered will detect these oil seeps to reduce exploration risk and greatly reduce harm to marine life. This development and initiatives of manufacturers is expected to hold bright future for AIs and IoTs in the Oil and Gas sector.

Internet of things is a major driving factor in transforming drilling, production, and exploration works. These activity takes place in remote locations where various assets are needed to be optimized and tracked. Internet of things connects field workers to applications, such as predictive maintenance, real-time data analytics, environment monitoring, health and safety for faster decision making, asset management, and a safer working environment. Asia is estimated to be one of the biggest consumers of the resource at 47%, followed by the rest of the world (24%). The growing production and consumption make them one of the major markets for adopting IoTs and AIs in the coming years. According to IBEF, Oil and gas sector is among the eight core industries in India. It plays a major role in influencing decision-making for all the other essential economy sections. The economic growth of India is closely related to its energy demand, therefore, the need for oil and gas is projected to grow more, making the sector quite conducive for investment. According to India Energy Outlook 2021 (IEA), primary energy demand is expected to nearly double to 1,123 million tonnes of oil. As of June 01, 2021, the sector’s total installed provisional refinery capacity stood at 249.9 MMT and IOC emerged as the largest domestic refiner, with a capacity of 69.7 MMT.

Similarly, Malaysia is the second-largest oil and natural gas producer in Southeast Asia in 2019. According to Oil & Gas Journal (OGJ), in January 2020, Malaysia has proved oil reserves of around 3.6 billion barrels, which is the fourth-largest reserves in Asia Pacific after China, India, and Vietnam. In addition, the global IT spending’s in the oil and gas sector will be over US$55 billion by end of 2021. This interest in new technology is primarily being driven by an arrival of IoT, aging infrastructure, shortage of skilled labour, increase in cyber-attacks, need to improve operational efficiency to maintain competitiveness. The growing investment in technology in the upstream oil and gas sector is currently centred on mobility, asset management, data analytics and cloud adoption, and is targeted to lower infrastructure costs. Initial IoT adoption is therefore focused on efficiencies and can enable more informed decision making. Thus, these needs for improvements and requirement for more products has allowed IoT and AIs to pick up pace in the recent years. The market is expected to attract significant number of investors and players in the coming years.

The growth in production costs, has increased the adoption of IoT in the upstream oil and gas to overcome several significant challenges, including increasing legislation, difficulties in hiring and retaining a workforce, handling extreme environmental conditions, and continuous monitoring among others. Various companies extend collaborations and partnerships with software companies to deal with these kinds of issues and problems. For instance, in 2018, Total S.A., based in France, has partnered with Google Cloud to develop jointly AI solutions which would optimize subsurface data analysis for exploration and production. Shell is adopting reinforcement learning in order to control its drilling equipment, essentially using a reward system based on the AI’s choices. Aker BP, which is an independent upstream oil and gas company from Norway has partnered with SparkCognition to adopt an AI-powered predictive maintenance solution in its unmanned Tambar platform. Thus, these investments are expected to create major tension for demand and supply of smart technologies in the oil and gas industry in the coming years.

 

The sudden spread of the severe acute respiratory syndrome coronavirus has triggered a medical emergency in all countries worldwide. Several countries placed substantial limitations on the space of a highly dangerous virus. Administrative measures such as national lockdowns and social distancing programs have brought the situation to a halt. For instance, the International Energy Agency (IEA) reported in April 2020 that worldwide energy demand fell by -3.8 percent in the first quarter of 2020. According to International Energy Agency, the Covid-19 crisis will have a longer-term impact on natural gas markets because the main medium-term drivers are highly uncertain.

COVID 19 had thrown the entire ecosystem to a halt, putting a halt to the production and sale of new autos around the world. OEMs waited for the lockdowns to be released before they could resume production, which had a negative impact on their operations. As a result, refiners had to modify their manufacturing volumes. The oil and gas sector are a high-capital-intensive industry that requires periodic funding to stay afloat. As a result, the production halt and lower demand during the outbreak had an extraordinary impact on refiners and oil plants and wells, which has impacted the adoption rate of modern technology systems such as artificial intelligence and the internet of things.

However, new production and infrastructure projects are anticipated to come online while growth trends fall short of projections, bolstering the likelihood of overcapacity and low prices. This throws a pall over future investments, which will be required to maintain the adoption of newer technologies by the major oil and gas industry players in the market during the long run.

Oil and gas companies adopting internet of things to help them ensure safety and security in delivery and extraction of oil and gas resources. The transporting and extracting fuels from the source to the refinery and to the end-user is a complicated process. There are various points to monitor and from drilling equipment to trucks, pipelines, boats, tanks, and trains. IoT modules, platforms and software enables oil and gas solutions to cover many applications, from sensing equipment status to monitoring container levels to maintenance. These series of solutions contribute to a better and efficient and responsive infrastructure and help the oil and gas supply chain be safer and more efficient. The emerging IoT solutions like Low Power Wide Area Networks (LPWAN) are redefining the oil and gas offshore monitoring system. Providing a low-bandwidth and inexpensive wireless link for small and low-computing devices, LPWAN is backbone of the large-scale sensor networks to aggregate the granular telemetry data from the innumerable endpoints. A simple star topology coupled with the extensive range makes LPWAN versatile for IoT deployments in large, structurally complex facilities like an oil and gas platform. Distributed environmental sensors constantly check for the slightest presence of flammable gases and toxic vapours in the atmosphere. Concurrently, 24/7 asset monitoring helps predict disastrous asset failures conducive to explosions or marine oil spills. In emergencies, on-premises alerts can be timely triggered for immediate counteraction to safeguard workers and prevent serious, irreversible catastrophes. Additionally, in the artificial intelligence system, intelligent software tool has several important attributes, including ability to integrate hard and soft statistical data computations and to integrate several AI techniques. The most commonly used AI techniques in the oil and gas sector include genetic algorithm (GA) that consists of a stochastic algorithm, fuzzy logic (FL) which is a is a mathematical tool that allows to covert crisp or discrete information as input and predict a crisp output with help of knowledge database and other specific reasoning mechanism, and by artificial neural network (ANN) technique. The storage of oil and gas is an importance part of large petroleum enterprises globally. It is essential to refer to the data for evaluating the development ability of enterprises. In developed countries including Europe and America, after the evaluation process and auditing assets of oil and gas reserves, it is essential to be disclosed to the SEC society of the United States. The development of artificial intelligence technology, is easier to evaluate and manage SEC oil and gas reserves. Thus, the need for essential smart technologies in the coming years will help the manufacturers and producers to have a more sustainable process and meet demand with efficiency and better quality.