RMFinder 2.0: An Improved Interactive Multi-Criteria Scenario Reduction Methodology

Abstract

This paper presents an extension of the RMFinder technique, previously proposed to identify representative models (RMs) within the decision-making process in oil fields. As there are several uncertainties associated with this decision-making process, a large number of scenarios are supposed to be analyzed, so that high-quality production strategies can be defined. Such broad analysis is often unfeasible, so techniques to automatically identify RMs are particularly relevant. The original RMFinder does not consider the individual probability of each RM, which may not be accurate when the risk curves of the problem are estimated. Therefore, a mechanism to calculate the individual probability of each RM was developed here, together with a graphical way to visualize different proposals of RMs. To automatically identify the optimal probability of each RM, this new version of RMFinder minimizes the deviation between the risk curves generated with the selected RMs and the original risk curves of the problem. The graphical approach automatically exhibits, in a single page per solution, the RM dispersion in the scatter plots, the resulting risk curves and the differences between attribute-level distributions. This helps the decision makers to visualize and compare different sets of RMs. The proposed methodology was applied to a small synthetic problem and to three reservoir models based on real-world Brazilian fields: (i) UNISIM-I-D, a benchmark case based on the Namorado field; (ii) UNISIM-II-Dβ, a benchmark case based on a highly fractured pre-salt carbonate reservoir; and (iii) ST001a, a highly heterogeneous heavy oil offshore field. The obtained sets of RMs were evaluated by experts and considered appropriate to the studied problems, being adopted as the standard models in the following steps of the decision-making process to define the production strategies under uncertainties.

Model-based decision analysis applied to petroleum field development and management

“…At the time of this study, we applied the initial proposal of RMFinder (Meira et al, 2016), which had some simplifications, namely the set of RMs were assumed to be equiprobable and it only considered a maximum of four field indicators (e.g., NPV, N p , W p , ORF). Meira et al (2017) improved RMFinder by assigning probabilities to each RM. Further improvements are still ongoing, such as increasing the number of objective functions (up to 50), including not only field indicators, but also well indicators (such as fluid rates and BHP).…”

“…8. Selection of Representative Models (RMs) (Costa et al, 2008;Meira et al, 2016Meira et al, , 2017Schiozer et al, 2004) based on multiple system inputs (probability distribution and range of uncertain attributes) and outputs (production, injection, and economic forecasts). 9.…”

“…This study also increases the computational efficiency of the CLFD process by using representative models of the uncertain system. Techniques to select representative models were the focus of recent research (Jiang et al, 2016;Meira et al, 2016Meira et al, , 2017Shirangi and Durlofsky, 2016). This work applies a method developed in related research (Meira et al, 2016(Meira et al, , 2017 to reduce the number of scenarios for production strategy selection and optimization.…”

“…Techniques to select representative models were the focus of recent research (Jiang et al, 2016;Meira et al, 2016Meira et al, , 2017Shirangi and Durlofsky, 2016). This work applies a method developed in related research (Meira et al, 2016(Meira et al, , 2017 to reduce the number of scenarios for production strategy selection and optimization. The method combines a mathematical function that captures the representativeness of a set of models with a metaheuristic optimization algorithm, to ensure full representation of the variability of system inputs (uncertain attributes) and outputs (production and economic forecasts).…”

MODEL-BASED OPTIMIZATION OF ICVs’ CONTROL FOR PRODUCERS, INJECTORS, AND WAG-CO2 CYCLES SIZE IN A CARBONATE RESERVOIR UNDER UNCERTAINTY

“…To select the RMs, we used the procedure proposed by Meira et al (2016) and Meira et al (2017), based on multiple system inputs (probability distribution and range of uncertain attributes) and outputs (production, injection, and economic forecasts), and it was implemented using in-house software. The method receives the full set of data-assimilated simulation models and returns a smaller subset of models (the RMs) that properly represent the variability of the uncertainties of the original set.…”