Quantifying Uncertainty in Production Forecasts: Another Look at the PUNQ-S3 Problem, SPE 62925-MS

A synthetic reservoir model, known as the PUNQ-S3 case, is used to compare various techniques for quantification of uncertainty in future oil production when historical production data is available. Some results for this case have already been presented in an earlier paper1.

In this paper, we present some additional results for this problem, and also argue an interpretation of the results that is somewhat different from that presented in the earlier paper. The additional results are obtained with the following methods: (i) rejection sampling, (ii) history matching of multiple models using a pilot-point approach, and (iii) Markov Chain Monte Carlo (MCMC).

Introduction

It is widely recognised that the future production performance of oil and gas reservoirs cannot be predicted exactly. There will always be some uncertainty. Nowadays, more and more effort is being made to quantify this uncertainty.

The aim of the work described in this paper is to compare a number of different methods for quantifying uncertainty in future reservoir performance. In particular, it considers reservoirs where some production data (beyond well testing) is available. Such data is particularly difficult to incorporate in an uncertainty analysis because of the time consuming nature of the computations necessary to simulate fluid flow in the reservoir.

The work was carried out as part of the PUNQ-2 project2, partly funded by the European Union. PUNQ is an acronym for Production forecasting with UNcertainty Quantification. The project involved 10 European universities, research institutes and oil companies.

As part of the project, one of the participating organizations created a synthetic reservoir model known as PUNQ-S3. Eight years of production were simulated using a commercial reservoir simulator. The simulated production data were revealed to the other participants, together with some other information about the model. The participants were asked to predict the cumulative recovery after 16.5 years of production, for a given development scheme. They were also asked to quantify the uncertainty associated with their forecast.

The various participants used different techniques to answer these questions, and came up with a wide range of answers. The results obtained by several of the participants were presented in an earlier paper1.

In this paper, we will present some additional results for this problem, and also argue an interpretation of the results that is somewhat different from that presented in the earlier paper.