Abstract Estimating reservoir pressure and temperature is one of the challenges of geothermal reservoir engineering. A new proxy model has been developed to estimate reservoir pressure and temperature using wellhead pressure, temperature and non-condensable gas (NCG) amount. An exhaustive set of wellhead data covering a range of possible wellhead pressure, temperature and NCG data is used in a calibrated wellbore simulator, which is then used to create a knowledgebase to train an artificial neural network (ANN) model. The developed ANN model is tested using numerical simulation model results coupled with a wellbore simulator. It is observed that the ANN model can accurately estimate reservoir pressure and temperature for various production scenarios in liquid phase reservoir.

中文翻译：

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用于确定地热井储层压力和温度的代理模型

摘要 估算储层压力和温度是地热储层工程面临的挑战之一。开发了一种新的代理模型，可以使用井口压力、温度和不可凝气体 (NCG) 量来估算储层压力和温度。一套详尽的井口数据涵盖了一系列可能的井口压力、温度和 NCG 数据，用于校准井眼模拟器，然后用于创建知识库来训练人工神经网络 (ANN) 模型。开发的人工神经网络模型使用数值模拟模型结果与井筒模拟器相结合进行测试。据观察，人工神经网络模型可以准确估计液相油藏各种生产场景的油藏压力和温度。