Case Study: Closing the Loop on Fracture Execution With Real-Time Subsurface Measurements

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Hydraulic fracturing execution has historically relied on surface treating pressure as the primary information source for evaluating a stage. However, treating pressure is a composite signal that blends pipe friction and perforation friction, limiting its usefulness for diagnosing the underlying drivers of performance changes.

As a result, operators can see that a change has occurred, but not why it occurred or how fluid is distributed across clusters. This lack of subsurface visibility means that inefficiencies in cluster utilization are often recognized only after the treatment (i.e., through production data or post-job diagnostics) when the opportunity to take corrective action has already passed.

To address this limitation, high-frequency pressure data is used to deconvolute downhole effects in real time, enabling a closed-loop, measurement-driven fracturing workflow (Fig. 1).

Acoustic friction analysis (AFA) uses high-frequency pressure during pumping to quantify key metrics, including perforation efficiency and uniformity index (UI) (URTEC 4044703). By separating and quantifying the friction components driving stage behavior, the workflow avoids relying on aggregate surface pressure trends as a proxy for subsurface response.

These quantified outputs support a physics-based artificial intelligence (AI) approach that combines acoustic modeling with predefined decision logic to translate friction response into actionable stage-level recommendations, enabling operators to move beyond surface-based interpretation and make decisions based on measured subsurface behavior.

In this context, physics-based AI is anchored to measurement rather than used as a substitute for it. High-frequency pressure response is used to quantify perforation friction, perforation efficiency, and flow distribution, while AI supports interpretation and decision logic. This distinction is important because purely data-driven models rely on historical patterns, which may not represent outlier stages or changing downhole conditions.

Using Direct Measurements

Because these outputs are available while pumping, decision criteria can be established to guide execution (Fig. 2). Perforation efficiency and UI thresholds are used to flag performance degradation or emerging issues in real time. These alerts trigger targeted adjustments, such as modifying rate, proppant concentration, or fluid system, deploying diverters, or reallocating volumes, which can be implemented immediately. This shifts fracturing operations from a reactive process to one where execution is continuously evaluated and optimized as each stage progresses (SPE 230647).

Applying this in the field has shown that real-time insights can be used to identify optimal flow-velocity ranges, refine proppant ramp strategies, and dynamically redistribute volumes away from ineffective stages.

Importantly, these improvements can be achieved without increasing total fluid or proppant volumes. Instead, resources can be deployed more effectively, based on immediate feedback. This represents a shift toward volume-neutral gains in stimulation efficacy and more consistent cluster utilization across stages.

This case study describes the first field deployment of this workflow, where live AFA outputs were used to evaluate perforation efficiency and trigger logic-driven interventions based on predefined evaluation points and performance thresholds.

The objective was to demonstrate how threshold-based responses can limit efficiency degradation within a stage and improve overall execution consistency, providing a scalable framework for measurement-driven fracturing.