Oil and Gas
Our AI solutions for the oil and gas industry improve exploration and production efficiency, enhance decision-making, and reduce risks. We offer predictive analytics, real-time monitoring, and AI-driven automation to streamline processes and boost profitability
Defect Detection and Enhance Quality Assurance
One of the challenges in the oil and gas industry is identifying improper threading in pipelines or defects in error-prone mechanisms. Defects found at the end of the production line from upstream issues cost factory and budget resources. For example, if the defected oil pipeline or machine is installed into production, this could result in severe damages. These losses are comparatively far higher than the cost of AI adoption. Deploying a computer based system can verify the quality of production and provide deep insights of defects in analytics. AI powered Defect Detection solutions are cost-effective and is extremely economical in comparison to the prevailing processes.
Make Better Decisions with Analytics
Oil and gas businesses deal with lots of data coming from manufacturing processes but due to a lack of proper analytics tools, they’re unable to capitalize on the massive data resting in data silos. Companies can employ data engineers to manually analyze data to draw insights, but this is a limited option in time and cost. Furthermore, no amount of data engineers can possibly get to all the data that’s produced in a single day of operations. AI algorithms study various data streams from various sensors and machinery of different plants or entire Geoscience data and extract real-time analytics to generate intelligent suggestions based on business needs. These deep insights enable geoscientists to have better visibility of the overall processes and operations, thereby enabling them to make better strategic decisions. This leads to improved operations efficiency, cost reduction and even reduces the risk of failure.
Ensure Safety and Security Standards
Oil and gas plants operate in extremely critical environments and the risk of injury is much higher than traditional manufacturing environments. Employees in oil plants work under different temperatures, are sometimes exposed to toxic fumes and must be aware of many moving mechanisms. Not following proper safety protocols can result in injury and financial penalties.
Companies are obliged to adhere to safety standards enforced by law. Not adhering to these standards result in hefty fines. Even though there is a ton of data to monitor safety issues, it is still a largely manual processes, such as manually monitoring camera feeds or physical safety sweeps, to make sure measures are effective. Current solutions only ensure employees are wearing Personal Protection Equipment (PPE) at the point of entry into the plant, not throughout the workday.
An AI-powered computer-vision solution can monitor the work site to ensure workers are following safety procedures without any deviations. The camera data is fed into an AI algorithm which will then be analyzed to send alerts and proactive recommendations. AI solutions can alert management even for the smallest deviations in compliance.
Reduce Production and Maintenance Cost
"The annual cost of corrosion in the oil and gas production industry is estimated to be $1.372 billion."
- International Journal of Industrial Chemistry, Springer
Oil or gas extracted using oil rigs is stored in a central repository and then distributed across pipelines. Due to various temperatures and environmental conditions, oil and gas components often face material degradation and corrosion. Corrosion can cause component deformation, which results in faded threading or can weaken the pipeline itself. Not handling this problem can result in catastrophic damages halting the entire production process. This is one of the biggest concerns of the industry and companies employ corrosion engineers to monitor and handle the health of components to avoid corrosion activities.
AI solutions can prevent incidents like this from occurring. AI and IoT technologies can detect signs of corrosion by analyzing various parameters using knowledge graphs and predictive intelligence to approximate the corrosion occurrence probability and raise alerts to pipeline operators. This way companies can be proactive in handling the corrosion risks and moreover, based on knowledge graph analysis, study various machinery downtimes and predict time to carry out maintenance activity. This way, companies plan and adjust for downtime.
Big Data infrastructures in oil and gas
Big Data infrastructures are revolutionizing the petroleum industry, enabling companies to extract valuable insights from vast amounts of data generated throughout the oil and gas value chain. This data-driven approach is transforming various aspects of the industry, from exploration and production to refining and distribution.
Key Components of Big Data
Data Generation: The petroleum industry generates a diverse range of data, including seismic data, well logs, production data, sensor data from drilling and production equipment, and market data.
Data Storage: This data is stored in various formats, including structured, semi-structured, and unstructured data. Cloud based storage platforms are used to handle the massive volumes of data.
Data Processing: Powerful computing platforms are employed to process and analyze the data in real-time or near real-time. These platforms enable complex data processing tasks, such as machine learning, statistical analysis, and data mining.
Data Analytics: Advanced analytics techniques, including predictive analytics, prescriptive analytics, and machine learning, are applied to extract insights from the data. These insights help in optimizing operations, reducing costs, and improving decision-making.
Data Visualization: Data visualization tools are used to present complex data in a visual and easily understandable format. This enables stakeholders to make informed decisions based on data-driven insights.
Benefits of Big Data
Enhanced Exploration and Production: Big data analytics can improve the accuracy of seismic data interpretation, leading to more precise identification of potential hydrocarbon reservoirs. It can also optimize drilling operations by analyzing real-time data from drilling equipment, reducing drilling time and costs.
Improved Production Optimization: By analyzing production data in real-time, companies can optimize production rates, identify potential production issues, and implement timely interventions. This leads to increased production efficiency and reduced downtime.
Predictive Maintenance: Big data analytics can be used to predict equipment failures, enabling proactive maintenance and reducing unplanned downtime. This significantly improves operational reliability and reduces maintenance costs.
Risk Management: By analyzing historical data and real-time sensor data, companies can identify potential risks and take preventive measures to mitigate them. This helps in ensuring safety and compliance with regulatory requirements.
Supply Chain Optimization: Big data can be used to optimize supply chain operations, reducing transportation costs and improving delivery times. This leads to increased efficiency and cost savings.
Market Intelligence: By analyzing market data, companies can gain insights into market trends, customer preferences, and competitor activities. This information can be used to make informed business decisions and develop effective marketing strategies.
