Abstract
Abstract Training image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.
Highlights
Based on the training images that are used to interpret the a priori geological model, the MPS method can effectively reconstruct the complex geometry of the reservoir while satisfying the conditioning well data
Several MPS algorithms have been introduced: for example, the probabilistic modeling algorithm via SNESIM Program [8], the pattern similarity matching modeling algorithm represented by SIMPAT [9], the direct sampling modeling algorithm by DS [10], the image quilting modeling algorithm represented by CIQ [11], and other algorithms [12,13,14], as well as the optimization and improvement methods for prediction accuracy, efficiency, memory, and nonstationary problems [15,16,17,18,19]
At present, training image acquisition methods mainly include a manual drawing approach based on geological knowledge, object-based modeling method [20, 21], methods based on sedimentation [22, 23], and methods based on the simulated depositional process [24, 25]
Summary
Based on the training images that are used to interpret the a priori geological model, the MPS method can effectively reconstruct the complex geometry of the reservoir while satisfying the conditioning well data. In the conditioning data from stationary fluvial region, nonstationary fan TI is clearly not the best choice This means if there is a higher compatibility between TI and the conditioning data, there will be a higher similarity between the spatial characteristics of the samples from the training images and the conditioning data. When the best possible match between training images of 2D slice conditioning data is achieved layer by layer, the optimization results of all layers were counted, and the candidate TI with the most frequent (a) Fractured Model C1.
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