论文标题
使用增强学习的并行自动历史记录匹配算法
Parallel Automatic History Matching Algorithm Using Reinforcement Learning
论文作者
论文摘要
将历史匹配的问题从最小二乘数学优化问题重新匹配到马尔可夫决策过程中引入了一种方法，其中可以利用强化学习来解决该问题。该方法提供了一种机制，人为的深神经网络代理可以与储层模拟器进行交互并找到问题的多种解决方案。这样的公式可以通过启动多个并发环境来并行解决问题，从而使代理能够同时从所有环境中同时学习，从而实现了显着的速度。
Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.
