Abstract
Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.
Highlights
The key link between reservoir characterization and modeling is to explore the mapping relationship between a priori geological model and currently available well log and seismic data
Two-point geostatistical methods based on ‘variogram’ can reflect the correlations between two points in space, but it is difficult for these methods to characterize the spatial structure of complex sedimentary facies
Some scholars have used neural network to carry out 2D reservoir facies modeling research based on logging petrophysical properties or sedimentary microfacies data [26]
Summary
The key link between reservoir characterization and modeling is to explore the mapping relationship between a priori geological model and currently available well log and seismic data. The introduction of training images and search templates into MPG methods has strengthened the learning of a priori geological pattern and the simulation of multipoint associations in space in the process of reservoir facies modeling. Some scholars have used neural network to carry out 2D reservoir facies modeling research based on logging petrophysical properties or sedimentary microfacies data [26]. Some scholars have introduced neural network methods into MPG for 2D sedimentary facies modeling [27]. In view of the distinctive characteristics of both classes of methods, this paper presents an optimized MPG reservoir facies modeling algorithm based on a neural network by optimizing the design in terms of the organizational form of the multigrid training data and the repeated simulation of grid nodes.
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