**:** ..
**:**
**:** 81
**:**
**:** 2019
**:** .. // . . 81. .: , 2019. .147-167. DOI: https://doi.org/10.25728/ubs.2019.81.6

** :** , ࠖ, ,

** (.):** big data, machine learning, support vector machines, injection, oil rate, well

**:** () , . , . . . , . , , , , , . : - , . , . , , , , , , .

** (.):** Machine learning, namely supervised learning models is widely used for decision making in oil field development. An essential condition for methods application is the availability of digital databases with representative results which allows adequate model training. In this paper SVM-rank model is applied for injectivity prediction of infill wells for giant Western Siberian oilfield. Ranking algorithm also uses Voronoi diagram, proven as an approximation to the well drainage area. Complex method allows combine different reservoir and production parameters: productivity of surrounding wells, area pressure, frac parameters etc without common reservoir dynamics model, which in this particular case is not able to clarify and confirm the parameters of the reservoir system. There is double model used: the first model utilizes productivity and capacity reservoir parameters, the second one uses correlation analysis between infill candidate and surrounding production wells. The method can be particularly useful in complicated reservoirs, e.g. in dual porosity ones, where the relationship between formation parameters (permeability, porosity, saturation) and production rates is unclear and cannot be set by traditional development analysis, particularly in frac environment.

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