Fast Reservoir Performance Evaluation Under Uncertainty: Opening New Opportunities
Authors: Torrado Ruben Rodríguez , Ciaurri David Echeverría , Mello Ulisses T. , Droz Sonia Embid 
Published: September 2013

Abstract
Decision-making under uncertainty can be quite challenging, especially when complex numerical simulations are considered in the workflow and the decision has to be made relatively fast (e.g. in hours). This is the case when one needs to rank a given field portfolio within a limited budget and with acquisition constraints. If the ranking measure associated with each field is properly and rapidly evaluated, new prospect opportunities, that may lead to a favorable strategic position, can be readily identified.
In this paper, we propose an efficient methodology for computing a ‘production potential’ measure that can be used to rank field portfolios in the presence of geological uncertainty, quantifying both uncertainty and risk propagation. Next, we briefly describe the basics of the method proposed. In the first place, uncertainty in sedimentary variability and flow behavior has to be characterized by a number of representative geological realizations. Sampling techniques are used to significantly reduce the number of realizations while preserving accuracy in the description and uncertainty propagation. Thereafter, multiple and varied field development plans, based on primary/secondary recovery mechanisms, are automatically generated whilst accounting for key parameters related to the number, drilling locations and drilling sequence of wells. In these plans the reservoir is clustered by areas with similar production/injection potential, and the well locations and drilling schedules are obtained accordingly. The well controls are determined through estimations of the field recovery factor. By means of experimental-design techniques a relative small number of field development plans are selected to capture the most significant production profiles. Each of these development plans is simulated for the realizations sampled previously, and the production potential measure [e.g. average net present value (NPV) over all sampled realizations] is computed for all the plans. The highest of these measures (i.e. the best development plan) can be used for ranking the field in the portfolio. Response surface procedures are considered to perform additional analysis computations within iterative optimization procedures. It is important to note that other statistics related to the exploitation potential (e.g. standard deviation of the NPV) can also be used to complement the ranking, thereby mitigating the decision-makers risk tolerance. The methodology has been tested on the Brugge field benchmark which presents 104 realizations of multiple geological parameters. The benchmark has been modified in order to simulate a green-field scenario. The ranking measure is the (discounted) NPV averaged over the 104 realizations. The proposed workflow yields a ranking measure of $5.43 billion, and the computational cost is around 1,900 simulations (performed in a parallel computing environment). This NPV is somewhat higher than those found for the Brugge benchmark (with similar modified settings) by other researchers. In order to validate the results, we performed more exhaustive checking using around 17,000 simulations, and the ranking measure found was $5.51 billion.
The new workflow presented allows one to efficiently, and in a sufficiently accurate manner, support decision making in field portfolio evaluation. Fast reservoir performance evaluation engines open new prospect opportunities that, with traditional decision-making techniques, may be frequently lost.