Abstract

The possibility that the productivity is seriously reduced if the producing formation is exposed to the fresh water has been recognized for many years. Many experiments and theoretical studies have been carried out to elucidate the mechanism of water sensitivity. Despite years of field observations and laboratory research, methods of predicting quantitatively the susceptibility of formation to water-sensitivity have not been developed to complete reliability. Until now, there was no model to predict the productivity decreasing according to the experiment results except time consuming simulation.

Based on the latest researches on water sensitivity characteristics and mechanism, core experiments were set up to analyze the permeability and relative permeability parameters changing along with the salinity decreasing under different reservoir properties. Based on material balance equation, analytical function of relative permeability, Backley Leverett theory and Welge equation, a new productivity decline model which can quantitatively predict water sensitivity influence is derived with all the coefficients clearly. The new model has been proven to accurately predict the production performance. The changing coefficients could be obtained through analyzing permeability loss and relative permeability changes through laboratory measurements.

The most significant innovation is that the production performance could be quantitatively predicted rely on the laboratory results. It can be concluded from experiment analysis that the permeability loss was slow after the first fast, and the relative permeability parameters also changed (residual oil saturation and irreducible water saturation becomes bigger, and the relative permeability of both oil and water phase becomes smaller at the same water saturation) along with salinity decreasing because of the clay swelling and the colloidal clay particles captured at a local pore constriction. Then the change of the coefficients in new model could be calculated and the production performance could be predicted. The proposed model has been tested using production data from several reservoirs and the results have demonstrated that the proposed new model works satisfactorily.

The novelty of the new dynamics model is in the ability to solve the production prediction approach for water-sensitive reservoirs according to experiment measurements without time consuming simulation.

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