Big Data in Oil and Gas
Adoption, Use Cases, and Techs
ScienceSoft provides big data consulting and development services since 2003 and delivers scalable, fault-tolerant big data solutions for the oil and gas industry.
Big Data in the Oil & Gas Industry: Market Size and Growth
Big data solutions can provide companies in the oil and gas industry with insights into exploration, drilling, and production processes to ensure their optimization, reduce environmental risks, streamline equipment maintenance planning, enhance oil recovery, and more. The global big data oil & gas market size is estimated at $20 billion and is expected to grow at a CAGR of 19% by 2032.
The Adoption of Big Data Analytics is on the Agenda of Leading O&G Companies
According to another source, Research & Markets, major oil & gas companies like ExxonMobil and Shell heavily invest in big data and AI solutions. The key technology adoption drivers include the growing demand for predictive maintenance and the necessity to improve production and drilling performance.
Below, ScienceSoft outlined how big data techs can help address these and other demands of the upstream sector.
Big Data Software Architecture
Big data software for the oil and petroleum industry gathers multi-source data and analyzes it both in real time and in batches, i.e., according to a certain computation schedule (e.g., every 12 hours, every 24 hours, every week). The real-time component enables reactions to events as they happen (e.g., deactivating equipment in case of oil spill, adjusting drilling fluid pressure), while the batch layer is responsible for comprehensive historical analytics (e.g., drilling performance patterns).
The insights and alerts are available to users in BI apps and dashboards. The data warehouse stores highly structured data ready for analysis and querying by the BI layer.
Application of Big Data in the Oil & Gas Industry
Big data for exploration management
- Drilling location identification.
- Oil & gas reserves estimation.
How it works: an exploration area is equipped with sensors connected to big data software and capturing the travel time and amplitude of reflected seismic waves. The software uses this seismic and microseismic data to build 2D and 3D images of underground rock formations. A big data solution analyzes these images in combination with geological and historical drilling data, helping make optimal development decisions (e.g., well location and spacing) and estimate the amount of oil and gas in a reservoir.
Big data for reservoir engineering
- Reservoir characterization.
- Reservoir behavior analysis.
- Estimated ultimate recovery forecasting.
- Reservoir simulation and design.
How it works: Big data software analyzes well log, seismic survey, and sensor data and provides a 360-degree view of each reservoir’s unique characteristics. They include hydrocarbon saturation, porosity, permeability, well depth, length and thickness, fluid and phase behavior, etc. Based on this data, the software can perform AI-powered reservoir simulations to help engineers identify optimal improved oil recovery (IOR) or enhanced oil recovery (EOR) methods, choose the best reservoir construction models, and strategies for drilling and completing wells in the reservoir.
Big data for drilling management
- Drilling processes optimization.
- Predictive and preventive maintenance.
- Equipment maintenance planning.
- Remote equipment monitoring and control.
- Inventory management optimization.
How it works: Sensors installed on the drilling equipment send temperature, pressure, vibration, flow, position, torque, and other readings. Gathered and analyzed by the big data solution, this data powers real-time insights into drilling processes (e.g., drilling direction, drilling fluid composition and pressure, drilling bit position) and intelligent software-to-equipment commands (e.g., adjusting drilling bit position to target specific formations or avoid obstruction).
Big data software also gathers equipment operational data (e.g., the rotation speed of the drilling bit, drilling fluid temperature) and equipment metadata (e.g., model, operational settings). This data is used to build accurate AI/ML models that help generate alerts on abnormal events and identify failure-causing equipment usage patterns. This allows O&G companies to minimize NPT (non-productive time), optimize inventory management processes and equipment maintenance schedules, extend equipment lifespan, and more.
Drilling and equipment data can also be coupled with real-time and historical geological data (e.g., rock formation evaluation, mud properties) to build and adjust drilling models, predict anomalies, and prevent unwanted events like kicks and blowouts.
Big data for production management
- Real-time production monitoring and optimization.
- Production rates prediction.
- Remote control of production operations.
- Environmental impact control.
How it works: downhole and uphole sensors feed the readings into the big data solution for continuous analysis to ensure real-time visibility into production rates, equipment performance, resource utilization, environmental impact, and other production characteristics. The software can utilize this data for immediate alerts, intelligent remote control (e.g., deactivating equipment in case of abnormal pressure detection), and historical analytics (e.g., identifying excessive pump vibration as a root cause of its short lifespan).
Development of Defect Recognition Software for a Drilling Equipment Manufacturer
- ScienceSoft developed an application that processes drilling equipment images from 3D cameras to detect emerging defects.
- The app’s core is a neural network and visual recognition algorithms.
- The app calculates wear percentage and recommends optimal drill bit replacement dates.
Result: Reduced drill bit inspection time and operational costs, minimized downtime.
Development of Oil Well Monitoring Application
- ScienceSoft developed a web-based application that provides users with real-time access to information on the state of rode pumping equipment (e.g., rod or gearbox loading, alarms).
- Tree-structured data representation allows users to find and analyze the needed details quickly.
- The web app seamlessly fits multiple mobile screens.
Result: Real-time and convenient access to equipment state data anywhere and from any device.
Development of Software for Well Log Data Tracking
- ScienceSoft developed software that tracks well log data to automate the well development workflow.
- Users can set up requirements to define the development workflow and monitor the development status in real time.
- The tasks and progress are shared with the regulatory authorities and other parties.
Result: Structured and transparent well development processes and multi-party accountability.
Design of an Image Analysis Application for Remote Monitoring of Oil Storage Tanks
- ScienceSoft designed an application that analyzes images from on-site cameras and drones.
- The app uses neural networks and computer vision algorithms to detect liquid levels in each tank in real time.
Result: Optimized oil inventory management and timely detection of oil leaks.
Real-Life Benefits of Big Data in the Oil & Gas Industry
faster drilling design planning for new wells due to comprehensive multi-source data analytics
Up to 10%
increase in hydrocarbon recovery factor thanks to AI-powered reservoir simulations
reduction in drilling costs due to prediction of non-productive time events
Up to 35%
reduced downtime due to predictive maintenance
causes of non-productive time thanks to accurate forecasts
lower emission levels due to ML-powered emission source identification
Big Data in Oil & Gas: Consulting and Development by ScienceSoft
With 34 years of experience in data analytics, 13 years in oil & gas IT, and 10 years in big data services, ScienceSoft delivers big data software supporting the smooth operation of data-rich systems and providing accurate analytical insights.
Consulting on big data and big data solutions
- Conceptualization of big data software, including user and control apps, analytics and ML modules.
- Business case delivery with timeline, costs, and ROI estimated.
- Architecture design, including data lakes, ETL/ELT pipelines, DWHs, stream- and batch-processing engines, ML-training modules.
- Tech stack selection optimized to data processing specifics, e.g., real-time, batch, or both.
- Steps to improve existing solutions.
- ML/AI consulting to add intelligent components or enhance their accuracy.
- Expert advice on achieving data security and regulatory compliance.
Big data solution development
- Business case creation and risk mitigation planning.
- Market and competitor research (for software product companies).
- Optimal software capabilities planning, e.g., ML-powered or rule-based analytics.
- Integration with your existing software and legacy solutions.
- Fault-tolerant, cost-effective architecture.
- UX and UI design tailored to user roles, e.g., interactive dashboards for field specialists and BI teams and static interfaces for C-level reports.
- User training.
- Solution maintenance, support and evolution.
ScienceSoft is a global software development company headquartered in McKinney, Texas. Since 2013, we have been building big data solutions for oil and gas companies and helped our customers achieve informed decision-making, reliable automation, optimized processes, and reduced costs. With ISO 9001 and ISO 27001-certified quality and data security management systems, we guarantee top software quality and complete protection of our customers’ data.