Development of Smart Downhole Pressure Gauges with Embedded Artificial Intelligence for Real-Time Pressure Transient Interpretation and Flow Regime Identification

Abstract

The increasing complexity of modern wells and the demand for rapid, data-driven decision-making have exposed fundamental limitations in conventional pressure transient analysis workflows, which rely heavily on surface data transmission and offline interpretation. These approaches introduce latency, increase data bandwidth requirements, and constrain real-time reservoir and wellbore diagnostics. Downhole pressure gauges, while capable of high-fidelity measurements, remain largely passive sensing devices, offering limited autonomous interpretive capability. This gap presents a critical opportunity for integrating intelligence directly at the sensor level to enable real-time well performance assessment. This research presents the design, implementation, and performance evaluation of smart downhole pressure gauges with embedded artificial intelligence for real-time pressure transient interpretation and onboard flow regime identification. The proposed system integrates high-resolution downhole pressure sensors with low-power edge-computing architectures, enabling in-situ signal conditioning, feature extraction, and machine-learning-based classification without continuous reliance on surface computing infrastructure. The study focuses on transient pressure responses encountered during well testing and early production operations, including wellbore storage, radial flow, fracture-dominated flow, and boundary-influenced regimes. A comprehensive transient modeling framework was developed to generate representative pressure datasets under varying reservoir and wellbore conditions. These datasets were used to train and validate lightweight convolutional and hybrid rule-based AI models optimized for embedded deployment under downhole temperature and power constraints. Real-time inference performance was evaluated using derivative-based feature recognition and time-log pressure signatures. The embedded models achieved flow regime classification accuracies exceeding 92% while maintaining inference latencies below 50 milliseconds, demonstrating feasibility for continuous downhole operation. Results indicate that onboard transient interpretation significantly reduces data transmission requirements by up to 70% and enables immediate identification of anomalous flow behavior, such as early boundary effects or fracture interference. From an operational perspective, the system supports faster well test decision-making, adaptive choke management, and enhanced integration with intelligent completion systems. The study concludes that embedding AI directly within downhole pressure gauges represents a transformative step toward autonomous well monitoring, offering substantial improvements in operational efficiency, data economy, and real-time reservoir surveillance for digital oilfield applications.