The development of artificial-neural-network-based universal proxies to study steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) processes

Qian Sun, Turgay Ertekin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Steam injection is one of the most broadly deployed enhanced oil recovery techniques in heavy oil reservoirs. Numerical reservoir simulation plays a significant role in studying the mechanism and design of the field development strategies of steam injection procedures. Artificial neural network (ANN) is considered as a powerful subsidiary tool for high fidelity numerical models for its fast computational speed, especially when large volume of simulation runs are required (Monte Carlo simulation, sensitivity analysis and population-based optimization). This paper focuses on the discussion of the development of ANN-based proxy models studying steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) procedures. The proxy models will consider rock and fluid properties such as relative permeability and temperature dependent fluid viscosity as variables so that they will be capable of handling different types of reservoirs and formation fluids. Half of the SAGD well pair is selected as the minimum unit to study. The ANN model will predict the oil flow rate and cumulative oil production profiles of a SAGD project. To better utilize the injected heat, the CSS procedure in this paper is designed in such a way that the cycle will automatically switch when the oil flow rate in the production phase drops down to a certain threshold value. The project will be terminated when the initial flow rate of one cycle could not maintain the threshold oil flow rate. Following this design scheme, the total number of cycles within the production life will be an unknown. Given a certain set of input parameters, the proxy model will predict the number of CSS cycles and the corresponding oil flow rate and cumulative production profiles. The CSS proxy model developed in this work could be implemented in studying both conventional oil sands and naturally fractured reservoirs. Furthermore, the proxy models developed in this work could be implemented as screening tools which provide engineers with an opportunity to obtain fast recovery estimation of SAGD and CSS projects. They may also assist high fidelity model in history matching, or be employed as proxies in sensitivity analysis and population-based optimization. The ANN proxy models discussed in this paper are parts of a comprehensive ANN-based screening toolbox which is an ensemble extensive EOR processes.

Original languageEnglish (US)
Title of host publicationSPE Western Regional Meeting 2015
Subtitle of host publicationOld Horizons, New Horizons Through Enabling Technology
PublisherSociety of Petroleum Engineers
Pages1140-1166
Number of pages27
ISBN (Electronic)9781510803541
Publication statusPublished - Jan 1 2015
EventSPE Western Regional Meeting 2015: Old Horizons, New Horizons Through Enabling Technology - Garden Grove, United States
Duration: Apr 27 2015Apr 30 2015

Other

OtherSPE Western Regional Meeting 2015: Old Horizons, New Horizons Through Enabling Technology
CountryUnited States
CityGarden Grove
Period4/27/154/30/15

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All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology

Cite this

Sun, Q., & Ertekin, T. (2015). The development of artificial-neural-network-based universal proxies to study steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) processes. In SPE Western Regional Meeting 2015: Old Horizons, New Horizons Through Enabling Technology (pp. 1140-1166). Society of Petroleum Engineers.