AI Enhanced Oil Recovery

AI systems for optimizing enhanced oil recovery operations

The Problem

“Optimize EOR injection to maximize recovery”

Organizations face these key challenges:

Slow, manual surveillance and optimization cycles (weeks to months) that miss fast-changing reservoir dynamics and breakthrough events

High cost and uncertainty of EOR agents/energy (polymer, surfactant, steam, CO2) leading to overdesign, inefficient slug sizing, and poor conformance control

Limited ability to test many scenarios due to time-consuming history matching and computationally intensive full-physics simulation, especially across large well populations

Impact When Solved

2–8% EOR recovery uplift via improved sweep efficiency and pattern balancing5–15% reduction in chemical usage or 3–10% reduction in SOR/CO2 recycle through closed-loop injection optimization30–60% faster decision cycles (daily/weekly vs. monthly) with earlier detection of channeling, injectivity loss, and conformance issues

Real-World Use Cases

IoT-Powered Predictive Maintenance for Oilfield Efficiency

This is like putting smart fitness trackers on every critical machine in an oilfield so you can see problems coming before anything breaks, instead of waiting for a breakdown and then sending a repair crew.

Time-SeriesEmerging Standard

8.5

Digital twins and AI for oil and gas energy systems

This is about building detailed “virtual power plants and pipelines” for the oil and gas sector, then using AI to watch how they behave, predict problems before they happen, and suggest how to run them cheaper and safer.