The responses of hydrate reservoir during gas production are complex due to the spatially and temporally evolving thermo-hydro-mechanical properties. Accurate modeling of the behavior, therefore, requires a coupled multiphysics simulator with a large number of parameters, leading to substantial computational demands. This makes it challenging to efficiently predict long-term reservoir responses. In this study, by utilizing an artificial neural network (ANN) algorithm, a meta-model is proposed to deep learn the relationship between the material properties and reservoir responses, including borehole displacement and fluid production. As such, a set of 950 coupled thermo-hydro-mechanical simulations of a one-layer sediment axisymmetric model is carried out for six-day gas production via depressurization. Eighteen input parameters are considered in each simulation covering four physical aspects, namely hydrate dissociation, thermal flow, fluid flow, and mechanical response. With this comprehensive dataset of the responses, a meta-model is established based on the trained neural network, resulting in an efficient prediction of the responses with significantly reduced computational demand. The model is then further utilized to predict the future reservoir responses, and it is found that the results are in a good agreement with those from the fully-coupled simulator.
- Hydrate reservoir
- Machine learning
- Thermo-hydro-mechanical model
- Wellbore deformation
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Computer Science Applications