论文标题
使用基于beta-vae回归的替代模型进行现场优化
Surrogate Model For Field Optimization Using Beta-VAE Based Regression
论文作者
论文摘要
使用基于储层模拟的优化研究做出了与油田开发相关的决定，其中比较了不同的生产场景和井对照。这样的模拟在计算上很昂贵，因此替代模型用于加速研究。过去已经使用了深度学习来产生替代物，但是这种模型通常无法量化预测不确定性，并且无法解释。在这项工作中，提出了基于β-VAE的回归来生成用于优化工作流程的模拟替代物。 Beta-vae可以对潜在空间中的决策变量进行可解释的，分解的表示，然后进一步用于回归。概率致密层用于量化预测不确定性并实现近似贝叶斯推断。使用基于beta-vae的回归开发的替代模型发现了可解释和相关的潜在表示。 Beta的合理价值可确保因素分解与重建之间的良好平衡。概率密集层有助于量化目标函数的预测不确定性，然后将其用于决定是否需要全物理模拟。
Oilfield development related decisions are made using reservoir simulation-based optimization study in which different production scenarios and well controls are compared. Such simulations are computationally expensive and so surrogate models are used to accelerate studies. Deep learning has been used in past to generate surrogates, but such models often fail to quantify prediction uncertainty and are not interpretable. In this work, beta-VAE based regression is proposed to generate simulation surrogates for use in optimization workflow. beta-VAE enables interpretable, factorized representation of decision variables in latent space, which is then further used for regression. Probabilistic dense layers are used to quantify prediction uncertainty and enable approximate Bayesian inference. Surrogate model developed using beta-VAE based regression finds interpretable and relevant latent representation. A reasonable value of beta ensures a good balance between factor disentanglement and reconstruction. Probabilistic dense layer helps in quantifying predicted uncertainty for objective function, which is then used to decide whether full-physics simulation is required for a case.
