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Abstract

Summary

The EOR (or IOR) choice problem is an involved process, often occurring in a circular manner, where information circulates between different experts several times, increasing the decision time without necessarily improving the final choice. A literature review showed that most methods used for EOR (or IOR) selection rely somehow on the statistical learning from prior projects and on the expertise of the different individuals working on the project, thus in the value of learning.

The common steps involved in an EOR (or IOR) implementation are the selection of a suitable EOR (or IOR) process, the prediction of its performance, and finally the optimization of its design. The performance estimation may include laboratory experiments, analytical calculations, correlations, and numerical simulations. Most of these suppose data is available, that estimation under uncertainty can be easily done and that somehow information is perfect. This is often not the case. Furthermore, all of the above may create a lengthy, tedious and expensive process.

Based on these observations, in this study we propose an innovative workflow to screen and rank EOR (or IOR) opportunities among a data base of producing fields. This workflow was designed to be efficient and reproducible, but without overlooking the complexity of the decision process. Notably the ranking procedure explicitly considers the uncertainties on the static properties of the fields, integrates the computation of dynamic performances from semi-analytical physical models, and balances various corporate objectives of possibly contradictory nature.

The main technical components associated are: (1) a method to quickly establish the level of knowledge concerning the various reservoirs through an ontological mapping of their situation prior to the EOR (or IOR) evaluation, (2) a double criteria module (static and dynamic) searching and classifying valid EOR (or IOR) options according to weighted reservoir properties and recovery factors, and (3) a combination of the AHP and TOPSIS techniques to choose amongst alternatives and optimize the decision towards hierarchized goals.

In this paper, examples of elements of the methodology are shown and the possibility to apply this approach in a generic manner is discussed. The innovative aspects are stressed, considering current practices of reservoir management, proposing a cognitive decision process which can integrate fuzzy information concerning EOR (or IOR) application, thus objectivizing investments and long-term commitments.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202133040
2021-04-19
2024-04-26

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A cognitive methodology to improve EOR/IOR choice process: from applied approaches to more generic ones
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202133040
/content/papers/10.3997/2214-4609.202133040
/content/papers/10.3997/2214-4609.202133040
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