Big Data in Oil and Gas

Use cases:

• 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.

Use cases:

• 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.

Use cases

• 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.

Use cases:

• 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).