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Porosity plays an important role in the characterization of transport properties. However, it is quite difficult to predict the unknown porosity values only by some sparse hard data in the process of simulation based on current popular interpolation methods. Accuracy of simulated images can be improved using soft data and hard data together. Multiple-point geostatistics (MPS) is used to simulate statistical information of images, which allows extracting multiple-point structures from training images and then copies these structures to the regions to be simulated. To simulate the porosity values accurately, a porosity simulation method using soft data and hard data in MPS is proposed. Dimension reduction is made by filters to reduce the cost of CPU and memory. All similar training patterns fall into a cell in the filter score space, which is created by filters, to make a prototype. Finally, a training pattern is randomly drawn from a cell, and pasted back onto the simulation region. The experimental results show that, compared with the unconditional simulated image and the simulated image using hard data only, the structures of the image using soft data and hard data are more similar to those of the training image.