Abstract
Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) measurements during hydraulic fracturing treatments are used to estimate fluid volume distribution among perforation clusters. DAS is sensitive to the acoustic signal induced by fluid flow in the near-well region during pumping a stage, while DTS is sensitive to temperature variation caused by fluid flow inside the wellbore and in the reservoir.
Raw acoustic signal has to be transferred to frequency band energy (FBE) which is defined as the integration of the squared raw measurements in each DAS channel location for a fixed period of time. In order to be used in further interpretation, FBE has to be averaged between several fiber-optic channels for each cluster on each time step. Based on this input, DAS allows us to consider fluid flow through perforation stage by stage during an injection period, and to evaluate the volume of fluid pumped in each cluster location as a function of time, and therefore to estimate the cumulative volume of fluid injected into each cluster. This procedure is based on a lab-derived and computational dynamics model confirmed correlation between the acoustic signal and the flow rate. At each time step, we apply the perforation/fracture noise correlation to determine the flow rate into each cluster, constrained by the requirement that the sum of the flow rates into individual clusters must equal the total injection rate at that time.
On the other hand, the DTS interpretation method is based on the transient temperature behavior during the fracturing stimulation. During injection, the temperature of the reservoir surrounding the well is cooled by the injection fluid inside the well. After shut-in of stage pumping, temperature recovers at a rate depending on the injected volume of fluid at the location. The interpretation procedure is based on the temperature behavior during the warm-back period. This temperature distribution is obtained by solution of a coupled 3-D reservoir thermal model with 1-D wellbore thermal model iteratively.
Once we confirm that the DAS and DTS interpretation methods provide comparable results of the fluid volume distribution, either of the interpretation results can be used as a known input parameter for the other interpretation method to estimate additional unknown such as one of the fracture properties. In this work, the injected fluid volume distribution obtained by the DAS interpretation is used as an input parameter for a forward model which computes the temperature profile in the reservoir. By conducting temperature inversion to reproduce the temperature profile that matches the measured temperature with the fixed injection rate for each cluster, we can predict distribution of injected fluid for hydraulic fractures along a wellbore. The temperature inversion shows that multiple fractures are created in a swarm pattern from each perforation cluster with a much tighter spacing than the cluster spacing.
The field data from MIP-3H provided by the Marcellus Shale Energy and Environmental Laboratory is used to demonstrate the DAS/DTS integrated interpretation method. This approach can be a valuable means to evaluate the fracturing treatment design and further understand the field observation of hydraulic fractures.
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