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

Abstract

This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

A learning-from-data approach with soft clustering and path relinking to the history-matching problem

“…In a more generic version, the 'closed-loop field development' workflow (CLFD) considers, in the model updating process, not only the existing wells (as is the case of the CLRM workflow), but also optimizes the production strategy (e.g. position and type) of future wells (Shirangi and Durlofsky 2016;Schiozer et al 2019). In CLRM or CLFD workflows, history matching is just one of the steps continuously executed.…”

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

“…The obtained subset of scenarios from different techniques can then be applied in the field development and management phases during the decision-making process. Figure 1 shows the five summarized phases of the decisionmaking process extracted from the study of (Schiozer et al 2019). During phase 1, all data obtained from a variety of sources such as core descriptions, well logs, and well testing are gathered for model construction.…”

“…For instance, the selected models that are most suitable for the well placement optimization during the field development plan (phase 4) may not be appropriate models for the well control optimization (with the fixed well locations) during Fig. 1 Phases of the decision-making process (Schiozer et al 2019) field management decision (phase 5), since these procedures are very sensitive to different geological factors (Shirangi and Durlofsky 2016). Accordingly, comparative studies are essential to allow the user to select the most suitable and precise technique according to the different objectives and contexts (Mahjour et al 2020a).…”

Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China

“…They evaluated the proposed method with a benchmark of the Namorado Field, Brazil. Also, Schiozer et al [18] presented 12 steps to help in updating and production optimization under uncertainties using the UNISIM-I-D benchmark of the Namorado Field, Brazil.…”