# Realization of Precise Perforating Using Dynamic Threshold and Physical Plausibility Algorithm for Self-Locating Perforating in Oil and Gas Wells

## TL;DR

This paper presents a lightweight, real-time depth measurement system for oil and gas well perforating that uses dynamic thresholding and physical plausibility algorithms, enabling accurate collar recognition with minimal computational resources.

## Contribution

The work introduces the DTPPMP system, a novel in situ depth calibration method combining lightweight algorithms for collar recognition and plausibility verification suitable for high-temperature downhole electronics.

## Key findings

- Achieved 98.6% collar recognition F1 score in field tests.
- Algorithm requires only 1.5 microseconds per sample, demonstrating high efficiency.
- System effectively correlates CCL signals with casing tally for accurate depth measurement.

## Abstract

Accurate depth measurement is critical for targeting designated perforation intervals to maximize hydrocarbon recovery. While next-generation automated wireless perforating techniques reduce reliance on costly surface infrastructure and personnel, they lack the continuous depth correlation provided by conventional wireline cables. Consequently, correlating real-time casing collar locator (CCL) signals with a pre-recorded casing tally is essential for automatic depth determination. However, implementing this measurement remains challenging: downhole instruments must process CCL signals in real-time to identify collar signatures from complex interference, a task severely restricted by the limited computational resources and power budget of high-temperature downhole electronics. To address these constraints, this work proposes the Dynamic Threshold and Physical Plausibility Depth Measurement and Perforation Control (DTPPMP) system. This integrated solution enables in situ depth calibration by correlating CCL signals with the casing tally using lightweight algorithms for dynamic-threshold-based collar recognition and physical plausibility verification. Field tests demonstrate a collar recognition F1 score of 98.6% at a throughput of 1000 Sa/s. Notably, the algorithm requires only 1.5 {\mu}s per sample, confirming its computational efficiency and suitability for deployment on resource-constrained, high-temperature downhole platforms.