How Big Data is Transforming the Oil and Gas Industry

Petroleum engineers and geoscientists spend over 50% of their time collecting data, as per Brule’s report. Many firms seek to streamline this process through big data solutions in the oil and gas sector. North America dominates the global market, fueled by demand in the Gulf of Mexico and US shale fields. It aims for informed decision-making and enhanced performance. ConocoPhillips cites a 50% reduction in drilling time in South Texas’s Eagle Ford shale basin, thanks to deployed sensors. The North Sea follows closely, benefitting from oilfield digitalization, resulting in a 40% operating cost reduction during low oil prices. According to Mehta, a survey by General Electric and Accenture in 2018 revealed that 81% of executives ranked big data among the top three priorities for oil and gas companies, driven primarily by the quest for efficiency in exploration endeavors.

Big data solutions offer valuable insights across exploration, drilling, and production processes in the oil and gas sector, optimizing operations, mitigating environmental risks, and improving equipment maintenance. With a global market estimated at $20 billion, the big data oil & gas sector is projected to grow at a CAGR of 19% by 2032. – REPORTS AND DATA

Big Data analytics represents an emerging trend in both upstream and downstream sectors of the oil and gas industry. This technology addresses the handling of large datasets characterized by volume, variety, velocity, veracity, value, and complexity. The integration of data recording sensors in exploration, drilling, and production operations has transformed the industry into a data-intensive sector. The article will delve into how big data is used in the oil and gas sector, discussing its challenges, tools, and advantages.

Leveraging Big Data in the Oil and Gas Industry

Managing Seismic Data

Drilling deepwater oil wells incurs substantial costs, often exceeding $100 million. Precision is paramount in site selection to mitigate risks and economize resources. For instance, Shell employs fiber optic cables, developed in collaboration with Hewlett-Packard, and stores data on private servers maintained by Amazon Web Services. Utilizing upstream analytics, seismic data collected across potential sites undergoes aggregation, cleaning, processing, and analysis to optimize drilling locations. Integration with other datasets facilitates reservoir assessment. In research conducted by Joshi et al., Big Data solutions analyzed micro-seismic data for hydraulic fracturing, enhancing success rates through anomaly detection based on past failures.

Optimizing Drilling Processes

Modern offshore drilling platforms incorporate approximately 80,000 sensors, expected to generate 15 petabytes of data over their lifespan. These sensors monitor machinery performance, enabling predictive maintenance and minimizing downtime. Real-time analytics ensure operational efficiency and prevent breakdowns, resulting in cost and time savings.

Enhancing Reservoir Engineering

Big Data solutions facilitate the collection and analysis of data crucial for enhancing reservoir production. Downhole sensors, including temperature, acoustic, and pressure sensors, provide actionable insights into reservoir dynamics. Applications such as reservoir management enable timely responses to changes in pressure, temperature, and flow, enhancing operational control and profitability.

Improving Logistics

Effective petroleum transportation hinges on minimizing risks. Employing sensors and predictive maintenance, companies detect and address pipeline and tanker faults promptly. This proactive approach ensures safe transportation and mitigates potential hazards.

Benefits of Big Data in the Oil & Gas Industry

• 20% Faster Drilling Design Planning: Comprehensive multi-source data analytics expedite the design planning process for new wells.
• Up to 10% Increase in Hydrocarbon Recovery Factor: AI-powered reservoir simulations enhance recovery rates.
• 15% Reduction in Drilling Costs: Predictive maintenance reduces non-productive time events, lowering overall drilling costs.
• Up to 35% Reduced Downtime: Predictive maintenance minimizes downtime, optimizing operational efficiency.
• -75% Causes of Non-Productive Time: Accurate forecasts enable proactive measures to prevent downtime.
• 30% Lower Emission Levels: Machine learning identifies emission sources, leading to reduced environmental impact.

Challenges in Leveraging Big Data for the Oil and Gas Industry

Managing Costs and Infrastructure

One of the primary hurdles in implementing Big Data solutions across industries, including oil and gas, is the significant costs associated with data management, storage, and analysis. While technological advancements such as fog computing, cloud computing, and the Internet of Things (IoT) offer potential solutions, challenges persist, particularly regarding cost-effectiveness and suitability for non-fixed or latency-sensitive applications.

Personnel Expertise and Data Ownership

Navigating Big Data applications within oilfield service companies presents challenges concerning personnel expertise and data ownership. Ensuring personnel possess the requisite knowledge and addressing ownership issues are essential for successful utilization of Big Data in areas such as seismic analysis, reservoir modeling, drilling services, and production reporting.

Data Transfer and Digital Oilfield Challenges

The emergence of digital oilfields, characterized by extensive sensor and recording device usage, brings forth challenges in data transfer from the field to processing facilities. Factors such as data type, volume, and protocols influence the efficiency of data transfer processes, posing logistical hurdles.

Awareness and Business Support

Lack of awareness and insufficient business support emerge as significant barriers to Big Data utilization in the oil and gas sector. Challenges also include decision-making regarding relevant data, scarcity of skilled personnel, and the high cost associated with Big Data infrastructure. Enhancing staff and executive understanding of Big Data technology and its potential applications is crucial for successful implementation.

Technical Limitations and Collaboration

Technical challenges in Big Data applications within the oil and gas industry encompass limitations of data recording sensors, data frequency, and quality. Collaborative efforts between expert petroleum engineers and data scientists are necessary to address these challenges effectively, ensuring accurate application of Big Data tools in solving various petroleum engineering problems.

Tailored Solutions and Cost Optimization

Recommendations include the development of specific Big Data tools tailored to individual company needs, encompassing data recording, storage, and analytics. Customized solutions not only reduce software ownership costs but also optimize the value derived from recorded data.

Top 5 Big Data Tools

Hadoop

Apache Spark

MongoDB

Tableau

Cloudera

Utilizing Big Data in the Upstream Oil and Gas Industry

Big Data in Exploration

Interpreting seismic data demands sophisticated processing capabilities and robust visualization tools. Recent advancements in seismic devices have led to a surge in data volume, necessitating advanced analytical techniques. One crucial application of Big Data in the oil and gas sector is seismic data analysis, where machine learning tools enhance data interpretation efficiency, especially with vast datasets. For instance, Roden incorporated principal component analysis (PCA) and self-organizing maps (SOM) in multi-component seismic analysis, showcasing a structured approach to geological problem-solving. Similarly, Joshi et al. utilized Big Data to model fracture propagation maps during hydraulic fracturing, leveraging the Hadoop platform for managing extensive datasets.

Big Data in Drilling

Drilling operations generate diverse datasets, from digital rig site outputs to manually entered operator data. These datasets facilitate various analyses, from scheduling to operational optimizations. The proliferation of data recording tools and formats has made Big Data applications increasingly relevant in drilling operations. Notably, advancements in sensors, now numbering over 60, enable comprehensive monitoring throughout drilling processes. Studies by Duffy et al. and Maidla et al. demonstrate how Big Data analytics improve drilling efficiency and performance by implementing best practices and optimizing drilling parameters. However, challenges such as data filtering, quality control, and understanding underlying physics remain critical considerations for reliable outcomes.

Big Data in Reservoir Engineering

Distributed downhole sensors generate vast datasets critical for reservoir characterization. Bello et al. developed a reservoir management application utilizing Big Data analytics, incorporating components for data visualization, filtering, model building, and application. Additionally, innovative reservoir modeling techniques, integrating artificial intelligence and data mining with closed-loop reservoir management and integrated asset modeling, promise enhanced reservoir modeling accuracy and efficiency. Studies by Haghighat et al. and Popa et al. showcase how Big Data and machine learning enable real-time CO2 leakage detection and optimization of heavy oil reservoirs, improving operational performance and environmental management.

Big Data in Production Engineering

Big Data analytics play a pivotal role in optimizing production engineering processes. Smart forecasting and flow methods developed by Seemann et al. enable automated decline analysis, enhancing production performance prediction. Similarly, studies by Rollins et al. and Sarapulov and Khabibullin demonstrate how Big Data is utilized in production allocation and electric submersible pump (ESP) performance evaluation, respectively, improving operational efficiency and reliability. Palmer and Turland’s study highlights the optimization of rod pump wells using Big Data, employing a workflow encompassing data acquisition, automated workflows, and interactive data visualization. Moreover, shale operators leverage Big Data to enhance hydraulic fracturing projects, as evidenced by Southwestern Energy’s findings on proppant loading and fracturing stage spacing’s impact on the productivity index.

Big Data Applications in Downstream Oil and Gas Operations

Big Data in Refining

Plate’s study delves into the application of Big Data in refining processes, particularly focusing on enhancing petrochemical asset management. By analyzing historical data, a predictive maintenance approach is adopted to optimize the performance of critical equipment such as cracked gas compressors (CGCs). Predictive reports derived from data analysis not only minimize downtime but also reduce maintenance costs significantly. Additionally, Repsol SA leverages Big Data analytics for management optimization in one of its integrated refineries, partnering with Google Cloud to harness data analytics products and machine learning services.

Big Data in Oil and Gas Transportation

Anagnostopoulos pioneers research in leveraging Big Data analytics to enhance shipping performance, aiming to predict propulsion power and reduce greenhouse gas emissions. Data collected from sensors installed throughout a Large Car Truck Carrier (LCTC) M/V are analyzed using eXtreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) neural networks to optimize ship operations.

Big Data in Health, Safety, and Environmental (HSE) Management

Park et al. utilize Big Data to develop an energy efficiency model based on operational data gathered during ship operations, enhancing energy efficiency through the estimation of the energy efficiency operational indicator (EEOI). Employing the Hadoop framework and Apache Spark for machine learning tasks, the study aims to optimize fuel consumption relative to operational weight and distance.

Tarrahi and Shadravan employ Big Data analytics to enhance occupational safety in the oil and gas industry by managing risks and improving safety protocols. Utilizing a case study dataset from the Bureau of Labor Statistics (BLS), the study employs data clustering, mapping, and multidimensional statistical analysis to identify hidden trends and enhance safety measures.

Pettinger highlights the potential of safety predictive analytics derived from continuous safety inspection data. Incorporating safety indicators such as behavioral assessments and compliance evaluations into predictive analytics frameworks is crucial for proactive safety management.

Cadei et al. with the research develop prediction software using Big Data to forecast hazard events and operational upsets during oil and gas production operations. Utilizing various data sources including real-time series, historical data, and maintenance reports, the study employs artificial neural networks (ANN) and random forest algorithms for modeling, aiming to enhance hazard event prediction and operational safety.

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