关键词: 沉积微相, 自动识别, 神经网络, 图像处理, BP算法, 遗传算法, 测井曲线

Abstract: Aimed at inadaptability in current automatic identification models and algorithms of sedimentary microfacies,a new method is proposed by combination of genetic-BP algorithm and image process technology.Facies style is determined according to data of sampling well and interpretation results of experts.Minimal decision-making rule of rough set is used to choose pattern character and typical stylebooks.All kinds of standard model bases of sedimentary facies are built.Logging curves and stratum parameters are changed to image pattern by image process technology.Neural networks are introduced to extract and remember pattern character of curves automatically. Multi-layer forward neural networks are trained by combing BP and genetic algorithm.The gained network is of steadiness,fast study convergence speed, strong memory and generalization ability.Considered the multi-resolution of cambric facies,identification results nearest well and the same layers are consulted according to the result of small layer contrast.Fuzzy logic method is introduced to affirm and modify facies style according to the stratum rules in big circumstance of this region.The consistency of plane sedimentary facies and single well facies of small layer are assured.The model has a good adaptability to the problem of automatic identification of sedimentary facies. Test data of 15 wells from Daqing Oil Field show good results of the method.

Key words: sedimentary microfacies, automatic identification, neural networks, image process, BP algorithm, genetic algorithm, logging curve