This disclosure describes a method of calculating fluid distribution from a hydraulically fractured well, especially during a plug-and-perf hydraulic fracturing operation. The Distributed Acoustic Sensing (DAS) data is used to quantify the fluid distribution in separate perf clusters during fracturing, and the result can be used for completion design and optimization, hydraulic fracturing, and ultimately for oil and gas production.

The invention relates to DAS observation has been proven to be useful for monitoring hydraulic fracturing operations. While published literature has shown focus on the high-frequency components (>1 Hz) of the data, this invention discloses that much of the usable information may reside in the very low frequency band (0-50 milliHz). Due to the large volume of a DAS dataset, an efficient workflow has been developed to process the data by utilizing the parallel computing and the data storage. The processing approach enhances the signal while decreases the data size by 10000 times, thereby enabling easier consumption by other multi-disciplinary groups for further analysis and interpretation. The polarity changes as seen from the high signal to noise ratio (SNR) low frequency DAS images are currently being utilized for interpretation of completions efficiency monitoring in hydraulically stimulated wells.

This disclosure describes a method of combining DAS and DTS data to accurately estimate borehole temperature. The described method takes advantage of the thermal sensitivity of DAS signal in the low-frequency band, and combines with the absolute temperature measurement from DTS, to produce a distributed temperature estimation that is up to 10000 more accurate than the current commercial solution. The DAS and DTS data should be record simultaneously at the same well. The DAS data are first low-pass filtered and then converted into temperature variation measurement. Then an accurate temperature estimation is obtained by fitting both DTS and DAS data.

A method of monitoring hydraulic fracturing using DAS sensors in a treatment well and/or observation well is described. The raw data is transformed using a low pass filter (?0.05 Hz) and down-sampled to show the signal as the stimulation progresses. The resulting data can be used to optimize the hydraulic fracturing or improve reservoir models for other reservoirs.