Abstract

The appraisal phase in a petroleum field is characterized by several uncertainties, high investment and critical decisions, which are always strongly related to risk. In the past, it was usual to realize production forecast based on a deterministic simulation model. However, production forecast obtained by a probabilistic approach allows the quantification of uncertainty in the reservoir performance by numeric flow simulation of several possible models. Current hardware permits to incorporate more accurate production prediction in the decision processes. A probabilistic approach requires the definition of a methodology. The objective of this work is to develop a methodology to improve the performance of the risk analysis process, trying to get the best accuracy with the lowest number of simulation runs, using an automated process and parallel computing to accelerate the process. The methodology is based on simulation of several flow models representing possible scenarios of the reservoir, through the combination of the uncertain attributes. As simplification, sensitivity analysis is made to reduce the number of uncertain attributes. The simulation models are built through the derivative tree using only the critical attributes. To reduce the simulation time, parallel computing is also applied. After simulation of the models, astatistic treatment is used to obtain the risk curve of the production forecasts and of the net present value. Representative models are selected to integrate the analysis with economic uncertainties. The methodology is applied in petroleum fields and the advantages of the automated process and the simplified procedure are discussed.

Introduction

Petroleum field development and management are strongly related to risk due to several uncertainties that have to be considered. The most important uncertainties are related to the geological model, economic conditions and technological developments.

Typically, the impact of these uncertainties is quantified in terms of volumes in place, recovery factor and economic indicators (Ligero et al., 2003). Uncertainty on the economic conditions is always present in the petroleum industry. Usually, during the exploration phase, uncertainties related to the volumes in place have great impact. In the Appraisal and Development phases, as more information is obtained, the importance of the uncertainty on recovery factor becomes significant.

This paper deals with the quantification on uncertainties in these phases, adding new information to the methodology presented by Loschiavo et al.(2000), Steagall and Schiozer (2001), Ligero et al. (2003), Santos and Schiozer (2003), and other papers described in these references to perform risk analysis applied to petroleum field development using numerical reservoir simulation.

The methodology used here is the same methodology presented by Steagall and Schiozer (2001). Depending on the complexity of the problem, size of the reservoir and importance of the project, it is not possible to include all uncertain parameters in the analysis and simplifications are necessary to yield viability of the process.

Automation of the process, parallel computing (Ligero and Schiozer, 2002), special treatment of the geological attributes (Costa and Schiozer, 2002), treatment of production strategy (Santos and Schiozer, 2003), and fast simulation models (Ligero et al., 2003) are possible simplifications that are briefly discussed in this paper. Other approaches can be used, as presented by Salomão and Grell (2001).

All these authors have performed the risk analysis with a fixed economic model because the objectives were normally to quantify the impact of geological uncertainties in the decision making process related to field development. However, if economic condition changes, some additional considerations have tobe included in the risk analysis.

In this paper, it is used the concept of representative models to test the importance of this variation and some discussion is presented to propose amethodology to integrate this step with the risk methodology.

It is also presented a discussion about data integration among geology, reservoir engineering and economic analysis in order to reduce the amount of information necessary and time of the process. Some results are presented to show the advantages of automation and parallel computing to reduce the total time of the procedure where reservoir simulation is necessary in the reservoir performance prediction.

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