# Near-Wellbore Fracture Diagnosis via Strain Decoupling from Integrated In-Well LF-DAS and DTS Data

## TL;DR

This paper introduces a new method to diagnose fractures near a well by combining acoustic and temperature data to better understand hydraulic fracturing processes.

## Contribution

An improved strain–temperature decoupling method for LF-DAS/DTS data is introduced to enhance fracture diagnosis.

## Key findings

- The decoupling method uses pre-processed strain and temperature changes to reduce noise in DTS data.
- Tensile strain peaks at perforation clusters indicate fluid inflow and fracture initiation effectiveness.
- Automatic identification of temperature-affected regions improves mechanical strain extraction.

## Abstract

The low-frequency distributed acoustic sensing (LF-DAS) data acquired through fiber-optic cables cemented behind the fracturing well casing can dynamically capture the hydraulic fracturing process. After removing the thermal effect, the LF-DAS data can reveal the strain evolution induced by the initiation of hydraulic fractures. This paper presented an improved strain–temperature decoupling method for LF-DAS measurements based on joint LF-DAS/distributed temperature sensing (DTS) monitoring. The decoupling method was based on strain change and temperature change pre-processed from the raw DAS and DTS data to avoid the enhancement of DTS data noise. The moving window function method and the image processing parameter cosine similarity was introduced to cope with the differences in temporal and spatial resolution between LF-DAS and DTS data. The region significantly affected by temperature change could be identified automatically and the mechanical strain change could be extracted. The tensile strain response generally reached a local peak at perforation clusters and increased significantly at those with dominant fracture fluid inflow. By analyzing the evolution of strain profile during fracturing, the effectiveness of multi-cluster fracture initiation and fracture temporary plugging could be evaluated.