Advanced analytics of self-colmatation in terrigenous oil reservoirs

Abstract

One of the most significant issues of petroleum engineering nowadays is a decline in productivity and injectivity of wells due to formation damage, which is typically associated with the migration and retention of fines. The prediction of formation damage is a challenging problem due to the lack of information about concentrations and localizations of potential mobile fines in the reservoir. To solve this problem different algorithms of machine learning and data mining were tested: linear regressions, decision trees, random forest, gradient boosting and artificial neural networks. We developed the predictive model of permeability reduction in Vendian deposits (Russia) based on the analysis of rock properties and flooding conditions. This model allowed to describe the dynamic of permeability reduction as a multi-parametric function of injected pore volumes of water. Three defining parameters in the model were unique colmatation characteristics which have been predicted for each core sample. All the features of the self-colmatation process were studied and arranged by their importance. To build the model of permeability reduction we used two approaches. The first one was to discover all possible 2D cross-plot correlations between colmatation characteristics and features (manual analysis). The second is applying machine learning algorithms where all features were taken into account simultaneously. The benefits and disadvantages of both approaches were discussed in details.