Testing big data analysis technology with the use of deconvolution algorithm to increase oil production

Objective. To develop and test new approaches to the analysis of big data sets in the process of hydrocarbon field development to reveal the potential of production increase. Materials and methods. The article discusses the improvement of a technology for analyzing the results of the fixed-depth and the fiber-optic-based distributed temperature monitoring during well production by deconvolution algorithm. Based on the methodology for interpreting a single bottomhole temperature sensor, an algorithm for calculating the production dynamics was proposed and tested when temperature is measured near the interval of the reservoir. Results. The data obtained using the deconvolution algorithm were compared with the results of analytical calculations and the technique was tested on the real well data obtained with a fiber-optic sensor cable, confirming the effectiveness of the technique. By utilizing information on bottomhole pressure dynamics and well production history reconstructed from thermal field data, it is possible to make quick decisions on well intervention to improve production. Conclusions. The use of the proposed algorithm enabled to expand the limits of applicability of big data obtained using a fiber-optic distributed temperature sensor, which significantly increases the efficiency of monitoring the hydrocarbon field development.