摘要
地下储层地质建模对油气和水资源的开发以及CO_(2)地质封存(CCS)具有重要意义。传统基于地质统计学的建模方法(如基于变差函数或多点统计学的方法)产生的储层地质模型可在一定程度上与地质模式保持一致,但当模式特征变得复杂时,则具有明显的缺陷。深度学习中的生成对抗网络(GANs)能够抽象和再现复杂的空间模式特征,而在许多领域得到了成功的应用。近年来,学者将生成对抗网络与地质建模相结合,利用由卷积神经网络(CNN)构成的生成器(模拟器)去学习复杂的地质模式特征,进而产生非常逼真的地质模型。基于生成对抗网络的地质建模方法在许多方面得到了深入研究,该方法甚至已被应用于实际油田复杂储层的三维地质建模,取得了很好的效果。本文综述了基于生成对抗网络地质建模方法的研究进展,主要包括无条件约束和有条件约束两大类。无条件约束即只需产生吻合期望模式特征的地质模型而无须与条件数据一致。根据生成对抗网络训练方式的不同,其可分为传统生成对抗网络地质建模方法和渐进增长的生成对抗网络地质建模方法。前者对模拟器和判别器中的所有卷积层同时进行训练,而后者则是从浅到深逐层进行训练。渐进增长的方法允许模拟器从大到小逐尺度学习地质模式特征,因而在地质模型效果和训练时间方面优于传统方法。为了构建同时吻合模式特征和给定条件数据的地质模型,有条件约束的生成对抗网络地质建模方法被提出并得到广泛研究。其中,基于模拟器输入向量搜寻的条件化方法主要应用梯度下降法或马尔可夫链蒙特卡罗法(MCMC)去搜寻适当的潜在向量,使其通过预训练模拟器生成与给定条件数据一致的地质模型。但是,当条件数据发生变化时,则需要重新搜寻另一组合适的潜在向量,需要大量的时间和计算资源。为此,�
Geomodelling of subsurface reservoirs is of great significance to the development of hydrocarbon and water resources as well as carbon capture and storage(CCS). Traditional geostatistics-based geomodelling approaches(e.g., variogram-or multiple point statistics-based) produce geomodels that are to some extent consistent with geological patterns but have apparent flaws when the patterns become complicated. Generative Adversarial Networks(GANs) in deep learning can abstract and reproduce complicated spatial patterns and have been used successfully in many areas. In recent years, GANs have been combined with geomodelling, where the generator composed of Convolutional Neural Networks(CNN) can first learn complicated geological patterns and then produce realistic reservoir geomodels. The GANs-based geomodelling approach has been researched and improved in many aspects. Researchers have even applied this method in the 3 D geomodelling of complicated field reservoirs,much of which has achieved excellent performance. This paper reviews the research progress of the GANs-based geomodelling approach. The unconditional geomodelling approach can be classified into two categories, namely conventional and progressive GANs-based methods, based on the training manner of GANs. With a conventional manner, all CNN layers of the generator and discriminator are concurrently trained, while with a progressive manner, they are trained layer by layer from shallow to deep. The progressive manner allows the generator to learn geological patterns from coarse to fine scales and thus is superior to the conventional alternative in terms of the output quality and training time. To produce geomodels that are consistent with not only expected geological patterns but also the given conditioning data, GANs-based conditional geomodelling approaches are proposed. One of the conditional approaches is the post-GANs latent vector searching method, where proper input latent vectors of a pretrained generator are searched using the gradient descent or the M
作者
宋随宏
史燕青
侯加根
SONG Suihong;SHI Yanqing;HOU Jiagen(College of Geosciences,China University of Petroleum-Beijing,Beijing 102249,China;College of Artificial Intelligence,China University of Petroleum-Beijing,Beijing 102249,China;Peng Cheng Laboratory,Shenzhen 18055,China;State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum-Beijing,Beijing 102249,China)
出处
《石油科学通报》
2022年第1期34-49,共16页
Petroleum Science Bulletin
基金
国家自然科学基金面上项目(42072146)
国家自然科学青年基金项目(42102118)联合资助。
关键词
生成对抗网络
地质建模
卷积神经网络
地质模型
储层
generative adversarial networks
GANs
geomodelling
convolutional neural network
geo-model
reservoir
