Abstract:

The current multiple point geostatistical （MPS） modeling methods take the probability or geometric distance， which is inferred from training image based on a certain dimension of data template， as the criterion for determining the simulated value in the unknown area of the simulation grid. The relation between the criterion and geological meaning is weak in geological modeling. In view of the shortcomings of geological connection in the previous MPS modeling methods， returning to the essence of geological modeling， considering the sedimentological significance and geological implication of the similarity comparison between data events and training patterns during the facies modeling process， a facies sequence-based MPS modeling method is proposed. The method takes the facies sequence with sedimentological significance as the basic similarity comparison unit， creates a new variable data template suitable for facies sequence similarity comparison， utilizes the semantic recognition technology， directly calculates the similarity between the known facies sequence in the data event and the corresponding facies sequence in the training pattern， and then simulates the facies of unknown area in the simulation grid， through which realizes MPS modeling of strongly heterogeneous reservoirs from the sedimentological significance. The sedimentary facies simulation results of ideal models and the delta front reservoir in Tahe X area show that the proposed method can effectively reproduce the geometric shape displayed in the training image and well reflect the deposition and distribution of different facies in the reservoir， and greatly improve the accuracy of facies modeling. The method effectively combines sedimentology with MPS， and puts forward a novel similarity comparison means and modeling framework of MPS， which provides a new method for facies modeling of complex reservoirs.

Key words: facies modeling, modeling algorithm, similarity comparison, facies sequence, data template, multiple point geostatistics （MPS）, Tahe oilfield