A novel method of quantitative evaluation and comprehensive classification of low permeability-tight oil reservoirs: A case study of Jidong Oilfield, China

The classification of low permeability-tight reservoirs is the premise of development. The deep reservoir of Shahejie 3 member contains rich low permeability-tight reserves, but the strong heterogeneity and complex micro pore structure make the main controlling factors subjective and the classification boundaries unclear. Therefore, a new indicator considering the interaction between fluid and rock named Threshold Flow Zone Indicator(TFZI) is proposed, it can be used as the main sequence of correlation analysis to screen the main controlling factors, and the clustering algorithm is optimized combined with probability distribution to determine the classification boundaries. The sorting coefficient, main throat radius, movable fluid saturation and displacement pressure are screened as the representative parameters for the following four key aspects: rock composition, microstructure, flow capacity and the interaction between rock and fluid. Compared with the traditional probability distribution and clustering algorithm, the boundary of the optimized clustering algorithm proposed in this paper is more accurate.The classification results are consistent with sedimentary facies, oil levels and oil production intensity.This method provides an important basis for the development of low permeability-tight reservoirs.

Robustness Augmentation of Deep Learning Model Based on Pixel Change

Deep learning has been widely used in many fields. A large number of images can be quickly recognized by the deep learning models to provide information. How to improve the robustness of deep learning applications has become the focus of research. Unfortunately, the recognition ability of the existing deep learning model has been greatly threatened, many images can cause recognition errors in a well-trained model. Although data augmentation is an effective method, the existence of adversarial examples shows that traditional data augmentation methods have no obvious effect on minor pixel changes. After analyzing the impact of pixel changes on model recognition accuracy, a data augmentation method based on a small number of pixel changes is proposed. Our method can optimize the corresponding classification boundary and improve the recognition robustness of the model. Finally, a simple evaluation method to measure the robustness of model recognition is proposed. Our experiments prove the threat of a small number of pixels and the effectiveness of our data augmentation method. Moreover, the data augmentation method has strong generalization ability and can be applied to image recognition in many different fields.

Decomposition and Approximation of Multivariate Functions on the Cube

In this paper, we present a decomposition method of multivariate functions. This method shows that any multivariate function f on [0, 1]d is a finite sum of the form ∑jФjψj, where each Фj can be extended to a smooth periodic function, each ψj is an algebraic polynomial, and each Фjψj is a product of separated variable type and its smoothness is same as f. Since any smooth periodic function can be approximated well by trigonometric polynomials, using our decomposition method, we find that any smooth multivariate function on [0, 1]d can be approximated well by a combination of algebraic polynomials and trigonometric polynomials. Meanwhile, we give a precise estimate of the approximation error.