In the recent years the number of oil and gas wells, in which hydrofracturing is carried out in order to increase productivity, grows all over the world. In the hydrofracturing process a system of cracks is formed through the action of high pressure on the formation, into which the granular material (proppant), designed to fix the cracks in the opened state after removal of the excess pressure, is transported. In this regard, the need arises to monitoring the work of wells with hydrofracturing in order to control the spontaneous release of proppant from the created fracture beyond the productive strata (into the water-saturated horizons) and to optimize the design of the hydrofracturing.
The traditional way of estimating inflows from well productive intervals with standard downhole logging does not allow uniquely determination of proppant backflow intervals. The article describes an effective technique for detecting solids backflow intervals using high-sensitivity broadband spectral noise logging. The method was tested in the slanted well with a known interval of possible proppant backflow, since the hydrofracturing was carried out selectively in a certain interval of the formation. The article presents the results of the study, indicating a good correlation between the intervals, in which the proppant backflow was assumed, and the signals, caused by solid particles impacts on the instrument body. To analyze the data and extract the proppant backflow zones, a neural network recognition system for such signals was used.
The obtained data were compared with the profile of fluid inflow from the formation, obtained with the help of an extended logging complex. The developed technique of recognition of sand production intervals together with determination of the fluid inflow profile will allow to qualitatively improve the design of the subsequent hydrofracturings on a deposit.
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