A new screening tool for improved oil recovery methods using artificial neural networks

C. H. Parada, T. Ertekin

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

32 Scopus citations

Abstract

In more recent years, improved oil recovery (IOR) techniques are applied to reservoirs even before their natural energy drive is exhausted by primary depletion. Screening criteria for IOR methods are used to select the appropriate recovery technique in view of the reservoir characteristics. However, further reservoir appraisal is necessary after the applicable recovery technique is identified. The methodology proposed in this paper allows the preliminary evaluation of reservoir performance in parallel with the IOR screening process. In this study, artificial neural network (ANN) methodology is used to build a high-performance neuro-simulation tool for screening IOR methods such as waterflooding, steam injection and miscible injection of CO2 and N2. This innovative tool integrates the field development plan into the screening method. The reservoir characteristics are evaluated together with a proposed production scenario to assess the most suitable recovery process and, at the same time, the reservoir performance is forecasted by providing the estimated oil production curve. The screening toolbox consists of proxy models that implement a multilayer cascade feedforward back propagation artificial network algorithm. The proxy models work for a diverse range of reservoir fluids and rock properties. The field development plan is featured in the tool by different well patterns, well spacing and well operating conditions. The ANN screening tool predicts oil production rate, cumulative oil production and estimated production time. The tool also provides the flexibility to compare the hydrocarbon production for different sets of inputs, which facilitates comparison of various depletion strategies in the screening process as well. The results of this study show that the networks are able to recognize the strong correlation between the displacement mechanism and the reservoir characteristics as they effectively forecast hydrocarbon production for different reservoirs. The tool presents a new means to design an efficient and feasible IOR project by using artificial intelligence. The proposed tool facilitates the appraisal of diverse field development strategies for oil reservoirs and allows comparison of reservoir performance under diverse production schemes.

Original languageEnglish (US)
Title of host publicationSociety of Petroleum Engineers Western Regional Meeting 2012
PublisherSociety of Petroleum Engineers
Pages225-241
Number of pages17
ISBN (Print)9781618399298
DOIs
StatePublished - 2012
EventSociety of Petroleum Engineers Western Regional Meeting 2012 - Bakersfield, CA, United States
Duration: Mar 21 2012Mar 23 2012

Publication series

NameSociety of Petroleum Engineers Western Regional Meeting 2012

Other

OtherSociety of Petroleum Engineers Western Regional Meeting 2012
CountryUnited States
CityBakersfield, CA
Period3/21/123/23/12

All Science Journal Classification (ASJC) codes

  • Geochemistry and Petrology

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  • Cite this

    Parada, C. H., & Ertekin, T. (2012). A new screening tool for improved oil recovery methods using artificial neural networks. In Society of Petroleum Engineers Western Regional Meeting 2012 (pp. 225-241). (Society of Petroleum Engineers Western Regional Meeting 2012). Society of Petroleum Engineers. https://doi.org/10.2118/153321-ms