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This is a Literature Review study (also classified as: Quantitative Study).

October 16, 2025

Transforming Oil And Gas Operations Through Ai-Driven Innovations: Field Applications and Economic Impacts

Key Points

AI-driven techniques improve waterflooding efficiency, enhancing oil recovery and reducing costs.
Field case studies show that AI applications lead to significant economic optimization in oil and gas operations.
Waterflooding, accounting for nearly 50% of IOR projects, can significantly benefit from AI advancements.
Real-time data analytics from IoT sensors optimize injection strategies to avoid inefficiencies in conventional methods.

Abstract

The oil and gas industry faces mounting pressure to enhance recovery rates while reducing operational costs and environmental impact. Waterflooding, a dominant secondary recovery method, accounts for nearly 50% of global improved oil recovery (IOR) projects (IEA, 2023). However, conventional waterflooding suffers from inefficiencies such as poor sweep efficiency, premature water breakthrough, and suboptimal injection strategies. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a game-changer in optimizing waterflooding operations. AI enables: Real-time reservoir surveillance through IoT sensors and automated data analytics.Predictive modeling for water breakthrough and injection optimization.Enhanced reservoir characterization using seismic and production data.Economic optimization by reducing downtime and improving recovery factors. This paper provides a detailed review of AI applications in waterflooding, supported by field case studies and economic analyses.

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