15 minutos de lectura

El impacto de los macrodatos en la industria del petróleo y el gas

Big Data

IoT

Tendencias

The global big data market for oil and gas is booming — valued at $20 billion back in 2022 and expected to keep growing by 19% each year through 2032. Big data solutions are changing the game, offering vital insights across exploration, drilling, and production. With these analytics, oil and gas companies can slash environmental risks, improve maintenance, and boost oil recovery rates.

Major players like ExxonMobil and Shell are already investing in big data and AI to set up centralized data management and support data integration across various applications.

In this blog post, we delve into the impact of big data on the oil and gas industry, spotlighting its benefits and real-world applications.

Importance of big data in oil and gas

Adopting big data is rapidly becoming a cornerstone for achieving success in the oil and gas industry. By leveraging advanced analytics to process and interpret vast amounts of data swiftly and precisely, businesses can significantly slash expenses, bulletproof safety measures, and optimize their operational efficiency.

Exploration and drilling optimization

Production monitoring and optimization

Asset management and predictive maintenance

Supply chain and logistics optimization

Environmental and safety compliance

Reservoir management and enhanced recovery

Exploration and drilling optimization

By blending real-time ML algorithms with seismic and geological data, big data helps pinpoint high-potential drilling spots and fine-tune well placement. With advanced modeling and continuous seismic analysis, companies can predict geological challenges and adjust well paths instantly, boosting accuracy and cutting exploration costs.

Production monitoring and optimization

With real-time data from sensors, you’ll gain real-time insights into production, equipment, and resource use. Continuous analysis enables swift action when issues arise, such as remote shutdowns during abnormal conditions. This way, you can improve maintenance, reduce downtime, and keep production running smoothly.

Asset management and predictive maintenance

By analyzing historical performance data and real-time health indicators, big data systems spot patterns signaling potential equipment issues before they happen. Predictive maintenance lets you schedule interventions to prevent breakdowns — reducing downtime and extending equipment lifespan.

Supply chain and logistics optimization

Weaving big data into supply chain and logistics leads to more precise forecasting of material and equipment needs, better inventory management, and smarter transportation route planning. This way, companies can significantly slash logistics expenses and foster greater collaboration across the entire supply chain.

Environmental and safety compliance

Big data analytics empowers organizations to improve environmental compliance by providing granular insights into their impact and risk profile. By meticulously monitoring emissions, pollution levels, and environmental conditions, they can swiftly address potential issues, mitigate risks, and ensure strict adherence to regulations.

Reservoir management and enhanced recovery

With big data, engineers can analyze vast datasets from well logs, seismic surveys, and sensor data to create a comprehensive profile of each reservoir's unique characteristics. AI simulations then help optimize recovery plans, pick top reservoir models, and craft efficient drilling and completion strategies for maximum output.

Exploration and drilling optimization

Production monitoring and optimization

Asset management and predictive maintenance

Supply chain and logistics optimization

Environmental and safety compliance

Reservoir management and enhanced recovery

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Big data solutions for the oil and gas sector

Equipped with big data analysis, companies can identify technological trends and optimize every step of their operations — from exploration to production. This approach boosts efficiency, cuts costs, and significantly improves safety by reducing the likelihood of accidents and refining workflows.

Big data for exploration management

Exploration teams use seismic, geophysical, and geochemical data to create 3D models of subsurface formations. Applying ML algorithms and big data analytics, they extract insights from these models to improve the prediction accuracy of mineral and hydrocarbon deposits, reducing dry well risks and optimizing drilling locations.

Big data for reservoir engineering

By analyzing large volumes of real-time data on reservoir conditions — like pressure, temperature, and fluid composition — engineers gain invaluable insights into subsurface formations. With ML and data mining, they process this data in real time to create predictive models that refine recovery strategies and maximize extraction efficiency.

Big data for drilling management

By monitoring and analyzing speed, pressure, and temperature, operators can optimize the drilling process instantly. Blending this data with advanced well control systems and sensors allows for precise trajectory adjustments, early detection of issues like blowouts and bottom-hole problems, and significant cost reductions.

Big data for production management

With real-time analytics for sensor and automation data, you can effectively detect anomalies, forecast likely failures, and adjust operational parameters with precision. This not only boosts system efficiency but also reduces maintenance costs, resulting in a smoother and more cost-effective production operation.

Data integration platforms By unifying data from diverse sources — ERP, GIS, and IoT devices — these platforms establish a robust foundation for informed decision-making. This integration is achieved through ETL processes, data virtualization, and cloud-based integration services. With an improved data landscape, businesses can conduct sophisticated analytics, generate insightful reports, and make timely, well-informed decisions.

Predictive analytics and ML Apply statistical and machine learning algorithms on both historical and real-time data to forecast trends, detect anomalies, and anticipate potential issues before they disrupt your business. This data-driven strategy empowers you to optimize processes, minimize downtime, reduce expenses, improve safety, and significantly boost overall efficiency.

IoT and sensor networks Deploy sensors throughout your infrastructure to gather real-time data on equipment performance, environmental conditions, and production metrics. You can gain from real-time monitoring, predictive maintenance capabilities, and the ability to respond swiftly to issues.

Geospatial analytics Leveraging remote sensing, LiDAR, and GIS, you can analyze spatial data to uncover geographic patterns, optimize resource allocation, and assess environmental impact. Mapping and visualization let you make informed decisions for optimal site selection, efficient land use, and reduced environmental footprint.

The oil and gas industry is experiencing a transformative shift, with big data evolving from a digital tool into a strategic catalyst for new business models. By fusing our in-depth industry expertise with cutting-edge techs such as ML, AI, and predictive modeling, we deliver comprehensive solutions to maximize the value of your data from optimizing exploration to streamlining production processes.

Philip Tihonovich

Director de Big Data en Innowise

Discover all the benefits of big data in oil and gas

The oil and gas industry is in the midst of a digital transformation, yet only 30% of companies have successfully scaled their digital manufacturing processes. Big data analytics provide advanced solutions to accelerate this transition and drive substantial value. While the specific benefits may vary based on organizational goals, several key advantages are consistently realized.

Improved exploration and production efficiency

New wells cost about $7 million apiece, with roughly 30% of that just for drilling. That’s why locating the optimal site is so crucial. Armed with big data analytics, AI, ML, and cloud tech, like those used by Shell, geosteering teams analyze vast datasets to identify the most promising location. Also, real-time production data monitoring optimizes extraction, boosts yield and efficiency, and cuts environmental impact.

Mantenimiento predictivo y reducción del tiempo de inactividad

Unplanned downtime on a 200,000 barrels per day (bpd) offshore platform can result in losses of up to $8 million for every 12 hours of idle time. Predictive maintenance mitigates this risk by analyzing data to spot operational anomalies and equipment issues early on. This helps minimize maintenance frequency, avoid unplanned shutdowns, and cut unnecessary preventive maintenance costs.

Cost reduction and operational efficiency

Streamlining key processes such as drilling and production flow management can lead to significant reductions in resource and energy costs. For example, McKinsey highlights that offshore operators can cut costs by 20-25% per barrel — encompassing both operational and capital expenditures—by leveraging connectivity to implement digital tools and analytics

Safety and risk management

With big data analytics, ML, and IoT, companies can scrutinize sensor data and monitor system performance, identify anomalies, and reduce the likelihood of failures. These analytics enable thorough risk assessments by correlating diverse data points – like weather patterns, equipment history, and human factors – to identify potential hazards and develop mitigation strategies.

Environmental monitoring and sustainability

Responsible for about 10% of global emissions, the oil and gas industry can greatly slash its carbon footprint through big data solutions. Advanced data analysis allows organizations to optimize processes, minimize wastage, and ensure compliance with environmental regulations. Also, big data provides a solid foundation to shift to cleaner energy sources.

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Challenges of big data in oil and gas

Let’s be honest: implementing and leveraging big data comes with significant challenges. The vast amount of data from sensors and equipment needs a bulletproof infrastructure and substantial computing resources for storage and processing, which can be quite pricey.

Additionally, the combination of structured and unstructured data makes integration and analysis more complicated. Often, this data can be inaccurate or incomplete, requiring going the extra mile to prepare it. Safeguarding critical data from increasing cyber threats is also paramount, as any compromise could lead to severe operational disruptions and financial losses. And, last but not least, the industry faces a shortage of qualified data experts, hindering the use of big data to its fullest.

At Innowise, we’re adept at conquering any challenges of big data integration, from managing vast volumes and integrating diverse data sources to securing impeccable data quality. Adopting top-tier data analytics and tried-and-tested infrastructure solutions, our team of highly skilled experts guarantees data accuracy and security while maximizing its strategic potential.

Philip Tihonovich

Director de Big Data en Innowise

The use of big data in oil and gas: Innowise real case

To truly grasp how big data can transform the oil and gas sector, let’s delve into a real-world example of how Innowise partnered with one of the leading industry players. The provider grappled with frequent power outages, sluggish incident response times, and soaring operational costs. The root of these issues lies in an outdated grid monitoring system that cannot provide real-time insights.

To overhaul grid management, our data science experts migrated the company's legacy SCADA solution to AWS, enhancing it with advanced data marts and user-friendly dashboards.

The project featured several key elements:

Data integration: Our team consolidated data from various grid components into a single, unified platform using AWS S3 and Apache Kafka. This integration guarantees real-time data accuracy and reliability, with AWS EMR and Apache Spark handling the complex data processing. IoT sensors and gateways provide comprehensive, continuous monitoring across the entire grid network, ensuring a clear and up-to-date view of system performance.

Advanced alert system: We implemented a robust alert system for real-time grid performance monitoring and issue detection. Custom algorithms, combined with Apache Kafka for data streaming, enabled automated notifications for anomalies. This reduced the need for constant manual oversight and allowed for prioritization of alerts based on their severity, helping operators address critical issues more effectively.

Intuitive user interface: React.js-based custom dashboards provide operators with clear visualizations of grid status, including live data, historical trends, and predictive analytics powered by AWS EMR and Spark. With seamless navigation and comprehensive reports, operators can make informed decisions swiftly and more effectively.

The project yielded remarkable results. By implementing a cutting-edge analytic system, the client achieved a 20% reduction in grid downtime, significantly enhancing operational reliability. Streamlined processes and effective technology integration led to substantial cost savings and improved system stability. Additionally, there was a striking 40% reduction in average incident response time. Real-time data access and sophisticated analytics enabled operators to make well-informed decisions, boosting overall operational efficiency.

To see how we’ve addressed similar challenges and driven success in other projects, please explore our case studies.

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Future of big data in oil and gas industry

Investments in smart assets underscore the increasing importance of data-driven insights for reaching operational excellence. By 2028, over 50% of oil and gas companies are expected to realign their strategies with this focus. In 2024, IT spending in the sector is projected to grow by 8.1% to $29 billion, highlighting the critical role of techs.

Despite notable advancements in data collection, challenges related to data quality, integration, and security remain. To overcome these and seize new opportunities, the following tech trends are set to shape the future of big data.

Growth in wireless IIoT devices

By remotely monitoring critical parameters — such as pressure, volume, flow rates, temperature, and equipment status — IIoT devices generate terabytes of data daily. Armed with this wealth of data and advanced analytics, you’re able to make smart business decisions, revamp operations, and refine asset management. As of 2023, the market for wireless devices in the oil and gas sector—incorporating cellular, satellite, and LPWA connectivity—stands at 7.8 million units. This figure is projected to grow substantially, reaching 18.8 million units by 2028, with a CAGR of 19.3%.

AI and ML analysis

To break down the complex datasets, oil and gas companies are turning to AI and ML. These techs help predict equipment failures and improve drilling processes, which reduces downtime, boosts production, and cuts costs. Looking ahead, generative AI is expected to enhance productivity for 30% of oil and gas companies by 2026. This technology will automate routine tasks and improve decision-making. By 2025, 10% of companies that adopt AI best practices are likely to generate at least three times more value from their investments compared to the 90% that do not.

Informática cuántica

By 2030, quantum computing will fuse classical high-performance computing with emerging technologies. Beyond 2030, this tech is expected to accelerate data processing, tackle complex algorithms, and solve large-scale optimization problems that current systems can’t handle. Recognizing this potential, industry giants such as ExxonMobil, Shell, y BP are already investing in quantum technologies to drive innovation and enhance sustainability.

Digital twins

Looking ahead, gemelos digitales have the potential to automate drilling operations when combined with robotics and autonomous systems. Additionally, they may improve smart grids in gas distribution networks, resulting in more reliable and efficient supply chains.
Por ejemplo, Chevron is developing virtual replicas of its facilities to diagnose and predict real-world scenarios. This approach allows them to monitor and predict equipment performance in real time, whether on-site or across the globe.

Cloud computing

The exponential growth in seismic data from exploration and production overwhelms traditional data management systems. Here is where cloud computing comes into play, offering scalable and budget-friendly solutions to handle and analyze these enormous datasets. In 2022, cloud computing revenue in the oil and gas sector surged to $27.8 billion, with projections displaying a growth rate of over 15% CAGR from 2022 to 2026. Notably, SaaS solutions are driving the largest share of this growth.

Gobernanza de datos

In 2024, data governance and security have become a must in the oil and gas industry. The increasing complexity of data, combined with the rapid advancements in AI technology, demands robust control measures and modern governance strategies. Immuta’s State of Data Security Report shows that around 35% of data professionals are focusing on revamping data governance and security measures. This shift is driven by growing concerns about sensitive data exposure through AI prompts, a worry shared by 56% of industry respondents.

Conclusión

By leveraging big data and cutting-edge data analytics, oil and gas companies can make well-informed decisions, improve their processes, grab a greater share of the market, and send their profits soaring. Yet, effectively tapping into the potential of big data requires overcoming complex challenges that demand specialized expertise and strategic planning. Therefore, choosing the right development partner is crucial for navigating these techs and reaching tangible results.

Preguntas frecuentes

What technologies are used to analyze big data in the oil and gas industry?

The oil and gas industry is at the forefront of adopting cutting-edge techs to analyze and manage vast amounts of data from diverse sources, including sensors, drilling operations, and production facilities. Advanced tools like Hadoop and Apache Spark facilitate the processing of large data sets. ML and AI algorithms help reveal complex patterns and relationships within the data. NLP is leveraged to extract valuable insights from unstructured texts, such as reports and logs. Additionally, computer vision technologies analyze images captured by satellites and drones.

How does big data impact decision-making in the oil and gas industry?

Big data is changing the way decisions are made in the oil and gas industry by providing in-depth detailed analysis and forecasts. With big data in place, you can elevate production processes, fine-tune maintenance strategies, and greatly increase equipment efficiency. These insights empower organizations to make smarter decisions – leading to better operational performance and more efficient use of resources.

Can big data help reduce the environmental impact of oil and gas operations?

Big data analytics in oil and gas industry provides a robust solution for reducing the environmental impact of operations. By leveraging extensive data collected from sensors, equipment, and satellite imagery, organizations gain detailed insights into their activities, which enables more accurate monitoring of greenhouse gas emissions and the early detection of methane leaks through advanced analytical techniques. Additionally, predictive modeling helps identify potential environmental risks such as spills or soil contamination. With this information, operators can take timely actions, optimize resource use, and significantly lower their ecological footprint.

How is big data transforming upstream, midstream, and downstream operations in the oil and gas sector?

Big data dramatically improves the accuracy of geological models in the upstream sector, resulting in more precise drilling and exploration, reducing uncertainty, and optimizing resource extraction. In the midstream sector, it plays a crucial role in refining transportation routes and improving inventory management. This leads to more streamlined logistics, increased efficiency, and fewer operational disruptions. For the downstream sector, big data analytics improve refining processes, secure product quality, and optimize resource allocation. As a result, organizations achieve greater efficiency and slash overhead expenses.

What future trends are expected in the use of big data in the oil and gas industry?

Looking ahead, big data is set to transform the oil and gas industry greatly. By harnessing advanced algorithms, AI, and ML, companies will dramatically improve the way they predict future trends and maintain equipment. Furthermore, the integration of big data with NLP, and IoT will create a holistic view of operations, leading to better analytics and risk management. Blockchain technology is also expected to bolster data security and transparency. In essence, big data will drive the industry towards a more data-centric and insightful future.

Dmitry Nazarevich Director de Tecnología de Innowise

Date: Dec 24, 2024

Dmitry Nazarevich Director de Tecnología de Innowise

Date: Dec 24, 2024

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