虽然机器学习 (ML) 广泛用于预测黑油特性，但很少用于预测油藏成分特性，包括与注气相关的油藏特性。通常可以使用广泛的常规实验室数据来帮助预测必要的注气参数吗？这个问题在完整的论文中得到了解决。作者提出了一种基于机器学习的解决方案，可以根据已知的流体特性（例如流体成分和黑油特性）预测相关的注气研究，也就是说，从注气实验室研究的样本中学习，并预测注气流体参数剩余的、更大的数据集。
ML 组件的目标是使用成分和黑油特性来预测溶胀测试的结果。如图。
While machine learning (ML) is used extensively to predict black-oil properties, it is used less often for compositional reservoir properties, including those related to gas injection. Can typically extensive conventional laboratory data be used to help predict the necessary gas-injection parameters? This question is addressed in the complete paper. The authors present an ML-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties—that is, learning from samples with gas-injection laboratory studies and predicting gas-injection fluid parameters for the remaining, much larger data set.
The objective of the ML component is to predict the results of the swelling test using compositional and black-oil properties. Fig.
