The protection of target formations with low-porosity and low-permeability is an advanced research project in petroleum engineering and has important significance for the increase in reserves and production. Conventionally, the core flooding tests have been adopted for determining the mechanism of formation damage. However, the permeability is too low to be carried out the core flooding tests easily for this type of formations, furthermore, the typical core samples are hard to get in some reservoirs.

Focusing on the typical formations with low-porosity and low-permeability, the mathematical models for predicting five kinds of sensitivities of formations and the damage induced by water blocking quantitatively were established in this study on the basis of numerous experimental results, applying the theory of petrophysics and interfacial chemistry for reservoirs and the intelligent method of ANN (Artificial Neural Network).

The practical software has been developed to predict the degree of sensitivities and water blocking of formations, so as to determine the mechanisms of damage and guide the design of drilling fluids. This software is very convenient for use only by inputting the related formation parameters. It can be instead of the complicated and time-consuming laboratory tests. Extensive applications show that more than 85% of predicted results are in agreement with the measured results, indicating its satisfactory reliability.

In this study, the methods for preventing the damage caused by water blocking effectively have been discussed by virtue of selecting proper surfactants. In addition, the novel drilling fluid suitable for protecting the reservoirs with low-porosity and low-permeability are developed.

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