AI-Driven Pressure Anomaly Detection in Geothermal Wells
While temperature is a critical metric, pressure dynamics within geothermal reservoirs present a more complex and often more telling picture of system health and efficiency. At Geosync, we have developed a proprietary AI model specifically for real-time pressure anomaly detection, moving beyond simple threshold alerts to predictive fault diagnosis.
The Challenge of Subsurface Pressure Signals
Pressure data from downhole sensors is inherently noisy, influenced by pump cycles, valve operations, and even seismic micro-events. Traditional monitoring systems flag deviations from a static "normal" range, leading to a high rate of false positives and operator alert fatigue. More subtle, gradual trends indicative of scaling, fracture closure, or injector-producer short-circuiting are often missed until they cause significant production loss.
Our Multi-Layered AI Approach
Our solution employs a layered analytical framework:
- Spectral Decomposition: Separates the pressure signal into constituent frequencies, isolating pump noise from genuine reservoir response.
- Recurrent Neural Network (RNN) Forecasting: Predicts the expected pressure trend for the next 6-12 hours based on historical patterns, current extraction rates, and neighboring well activity.
- Anomaly Scoring: Deviations between predicted and actual pressure are scored not just on magnitude, but on the shape of the deviation curve, its duration, and correlation with other parameters like temperature and flow rate.
This allows the system to distinguish between a harmless operational blip and the early signature of a wellbore integrity issue. For instance, a specific, slow-ramping pressure drop coupled with a minor temperature increase at a producer well can signal the onset of cool water breakthrough from an injector.
Case Study: Early Warning in Alberta
At the "Deep Heat" facility in Alberta, our model flagged an anomalous pressure signature in Well P-12. The deviation was only 2.3% outside the historical norm—below the site's manual alert threshold of 5%. The AI's diagnosis suggested early scaling in the production zone. A targeted maintenance intervention confirmed calcium carbonate buildup. Addressing it at this stage prevented an estimated 14 days of downtime and a 22% production loss for that well.
Integration into the Dispatching Logic
These pressure insights are fed directly into Geosync's thermal dispatching engine. If an anomaly suggests a potential reduction in a well's near-term capacity, the AI can preemptively adjust load distribution to other wells in the network, maintaining total energy output while the issue is investigated. This transforms asset monitoring from a reactive cost center into a proactive, value-preserving component of operations.
The future of geothermal asset management lies in understanding the language of the subsurface. By teaching our AI to interpret the nuanced grammar of pressure data, we move closer to truly predictive and resilient geothermal energy systems.