Schlumberger

Technical Paper: EOR Advisor System: A Comprehensive Approach to EOR Selection

Society: IPTC
Paper Number: 17798
Presentation Date: 2014
 

Abstract

Hydrocarbon exploration is reaching global frontiers in the search for near- and long-term solutions to increase oil production from the existing assets. Enhanced oil recovery (EOR) offers a substantial alternative to improve recovery from existing and mature fields by means of enhancing pore level sweeping in the reservoir.

While the principles of EOR are not new, field implementation has been scarce and as a consequence, the physics governing the displacement process have not been completely understood, nor has the screening criterion been properly defined. Several authors have proposed different binary approaches to address the issue of screening, with the drawback of different, and in some cases, contradictory screening results.

This paper presents a workflow to assess the EOR applicability for a given reservoir. The backbone of the advisor system is an expert system that has been calibrated using information from a systematic data review of a newly developed global EOR project database, merged with in-house EOR engineering expertise. One of the advantages of this approach over other published EOR expert systems is the capability for handling uncertain input, which enables qualitative ranking of the applicability of various EOR methods in heterogeneous reservoirs. Moreover, is the workflow makes it possible to interrogate the expert system with incomplete input information and obtain consistent results, which enables comparative sensitivity runs to assess the most valuable additional information needed to resolve ambiguities in the EOR method ranking. The system is equipped with a comprehensive evaluation of the current static and dynamic conditions of the reservoir, and is fully linked to geological and geophysical modeling and simulation, enabling the extraction of geological-consistent input values for the expert system directly from the model to facilitate quick forecasts of highly ranked EOR methods through surrogate reservoir models.

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