Distributed acoustic sensing technology is a diagnostic method that has been implemented in the oil and gas industry for flow monitoring during injection or production. One of the applications is to estimate fluid distribution during hydraulic fracturing treatment. This is an invaluable diagnostic tool for multistage fracture treatments because of the large number of parameters and high uncertainty involved in these fracture treatments. Distributed acoustic sensor (DAS) measurements are based on data extracted from a fiber optic cable installed in a wellbore. Fiber optic cable strain is sensitive to temperature and acoustic variations induced by fluid flow with different fluid properties and fracture geometries. These parameters are evaluated from the back-scattered laser pulse through the fiber using coherent Rayleigh backscattering for DAS and Raman backscattering for distributed temperature sensing (DTS). The DAS interrogation system acquires strain response to acoustic variations along the measured depth of the wellbore for all periods of time.

This study presents a method of interpretation of flow rate distribution from acoustic signals from DAS measurements. Raw acoustic measurements are transformed to energy attribute distribution for all necessary depth locations and timeframe. It allows to calculate energy for each perforation cluster location. This calculation demands proper depth interval consideration for each perforation cluster. Local minimums of time average energy attribute allow to find depth window where integrate DAS channels measurements for each time step occurs. Based on the previous experimental and computational fluid dynamic investigations, the correlation between acoustic signal and flow rate is applied to interpret the measured DAS data to flow distribution.

In this paper, the system of equations which connects acoustic energy response with clusters flow rate distribution during the injection period using these correlations is introduced. The solution of this system allows to calculate cumulative volume for each perforation cluster on each time step and in the end of fracture treatment. As additional verification of flow rate distribution, the results of DAS interpretation were compared with interpretation results of DTS. A field example is used to illustrate the interpretation procedure.

From this work, it is concluded that besides qualitative analysis, the DAS interpretation method provides a quantitative estimation of flow distribution. Based on the current assumptions, the interpretation results from DAS and DTS are comparable with satisfactory agreement. The combined DAS and DTS interpretations help to understand cluster efficiency in multistage fracture treatments.

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