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基于神经网络的剩余油分布预测及注采参数优化
吴君达,李治平,孙妍,曹旭升
0
(中国地质大学(北京)能源学院,北京100083)
摘要:
针对注水开发过程中注采参数的优化问题,提出采用神经网络代替数值模拟对剩余油分布进行预测,并结合无梯度差分进化算法对注采参数进行优化。该模型不仅建立了注采参数与目标函数的非线性关系,还能准确预测不同生产阶段剩余油分布。其预测原理是将注采参数和生产时间视为剩余油分布图像的高级特征,利用卷积层提取特征、转置卷积层进行上采样,通过多个卷积与转置卷积的组合逐级恢复原图像,从而达到准确预测的效果。在神经网络构建过程中,选择多个3×3的小卷积核来代替大卷积核,在不影响感受野的情况下减少了参数量,节约了计算成本,有效提高了模型训练时的迭代效率。以某区块4口注入井、5口生产井的五点井网为例,将不同阶段生产井的井底压力、注入井的注入量以及生产时间作为输入参数,建立了基于神经网络的预测模型,以净现值作为目标函数,通过差分优化算法对4个阶段的注采参数进行了优化。相比于基础方案,优化后的方案净现值提高了约 21%。
关键词:  神经网络  转置卷积  差分进化算法  注采优化  剩余油分布
DOI:10.13673/j.cnki.cn37-1359/te.2020.04.010
基金项目:国家科技重大专项“致密油气藏数值模拟新方法与开发设计”(2017ZX05009-005)。
Neural network-based prediction of remaining oil distribution and optimization of injection-production parameters
WU Junda,LI Zhiping,SUN Yan,CAO Xusheng
(School of Energy Resources,China University of Geosciences(Beijing),Beijing City,100083,China)
Abstract:
Aiming at the optimization of injection-production parameters in the process of waterflooding development,it is proposed to use neural network instead of numerical simulation to predict the distribution of remaining oil,and combine the gradient-free differential evolution algorithm to optimize injection and production parameters. This model not only establishes the nonlinear relationship between injection-production parameters and the objective function,but also accurately predicts the distribution characteristics of remaining oil in different production stages. The prediction principle of the model is that the injection-production parameters and production time are regarded as advanced features of the remaining oil distribution image. Then,the convolution layer is used to extract image features and the transposed convolution layer is used to carry out up-sampling on the image. The original image is recovered step by step through a combination of multi-layer “convolution+transpose convolution”,so as to achieve the effect of accurate prediction. During the construction of the neural network,multiple 3×3 small convolution kernels are selected to replace the large convolution kernels. Without affecting the receptive field,the amount of parameters is reduced,and the calculation cost is saved. The iteration efficiency during model training is effectively improved. Taking the five-point well pattern of 4 injection wells and 5 production wells in a block as an example,a neural network prediction model was established by taking the bottom hole pressure of the production well,the injection volume of the injection well and production time as input parameters. Taking the net present value (NPV)as the objective function,the injection-production parameters of the four stages were optimized through the differential optimization algorithm. Compared with the basic plan,the NPV of the optimized plan has increased by about 21%.
Key words:  neural network  transposed convolution  differential evolution algorithm  injection-production optimization  remaining oil distribution

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