Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation

A. D. Hartono, Farizal Hakiki, Z. Syihab, F. Ambia, A. Yasutra, S. Sutopo, M. Efendi, V. Sitompul, I. Primasari, R. Apriandi

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

7 Scopus citations

Abstract

EOR preliminary analysis is pivotal to be performed at early stage of assessment in order to elucidate EOR feasibility. This study proposes an in-depth analysis toolkit for EOR preliminary evaluation. The toolkit incorporates EOR screening, predictive, economic, risk analysis and optimisation modules. The screening module introduces algorithms which assimilates statistical and engineering notions into consideration. The United States Department of Energy (U.S. DOE) predictive models were implemented in the predictive module. The economic module is available to assess project attractiveness, while Monte Carlo Simulation is applied to quantify risk and uncertainty of the evaluated project. Optimization scenario of EOR practice can be evaluated using the optimisation module, in which stochastic methods of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES) were applied in the algorithms. The modules were combined into an integrated package of EOR preliminary assessment. Finally, we utilised the toolkit to evaluate several Indonesian oil fields for EOR evaluation (past projects) and feasibility (future projects). The attempt was able to update the previous consideration regarding EOR attractiveness and open new opportunity for EOR implementation in Indonesia.
Original languageEnglish (US)
Title of host publicationSPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition
PublisherSociety of Petroleum Engineers (SPE)
DOIs
StatePublished - Oct 17 2017

Fingerprint

Dive into the research topics of 'Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation'. Together they form a unique fingerprint.

Cite this