Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash Calculation 2

Abstract : 11 The flash calculation with large capillary pressure often turns out to be time-consuming and 12 unstable. Consequently, the compositional simulation of unconventional oil/gas reservoirs, where large 13 capillary pressure exists on the vapor-liquid phase interface due to the narrow pore channel, becomes 14 a challenge to traditional reservoir simulation techniques. In this work, we try to resolve this issue by 15 combining deep learning technology with reservoir simulation. We have developed a compositional 16 simulator that is accelerated and stabilized by stochastically-trained proxy flash calculation. 17 We first randomly generated 300,000 data samples from a standalone physical flash calculator. 18 We have constructed a two-step neural network, in which the first step is the classify the phase 19 condition of the system and the second step is to predict the concentration distribution among the 20 determined phases. Each network consists of four hidden layers in between the input layer and the 21 output layer. The network is trained by Stochastic Gradient Descent (SGD) method with 100 epochs. 22 With given temperature, pressure, feed concentration pore radius, the trained network predicts 23 the phases and concentration distribution in the system with very low computational cost. Our results 24 show that the accuracy of the network is above 97% in the metric of mean absolute percentage error. 25 The predicted result is used as the initial guess of the flash calculation module in the reservoir 26 simulator. With the implementation of the deep learning based flash calculation module, the speed of 27 the simulator has been effectively increased and the stability (in the manner of the ratio of 28 convergence) has been improved as well. 29
Document type :
Journal articles
Complete list of metadatas

Cited literature [54 references]  Display  Hide  Download

https://hal-ifp.archives-ouvertes.fr/hal-02142339
Contributor : Catherine Belli <>
Submitted on : Tuesday, May 28, 2019 - 3:17:28 PM
Last modification on : Friday, July 26, 2019 - 2:28:07 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-05-09

Please log in to resquest access to the document

Identifiers

Collections

IFP

Citation

Shihao Wang, Nicolas Sobecki, Didier Ding, Lingchen Zhu, Yu-Shu Wu. Accelerating and Stabilizing the Vapor-Liquid Equilibrium (VLE) Calculation in Compositional Simulation of Unconventional Reservoirs Using Deep Learning Based Flash Calculation 2. Fuel, Elsevier, 2019, 253, pp.209-219. ⟨10.1016/j.fuel.2019.05.023⟩. ⟨hal-02142339⟩

Share

Metrics

Record views

62