Abstract
It is becoming increasingly important to quantify uncertainty in reservoir performance. History matching is carried out to improve field knowledge and simulation reliability by ensuring that simulations match observed production. In real field cases at most a few reservoir scenarios are used to estimate production performance uncertainty and this leads to a lack of reliability of the uncertain forecasts.
The Neighbourhood Algorithm (NA), originally developed for earthquake seismology, provides a framework for generating history matched models and assessing uncertainty in production forecasting. It is a stochastic approach approximating the structure of the likelihood function by Voronoi tessellation and using all information previously obtained about the model space.
In this paper the first application of NA on a real oil reservoir is described. The case is the Rigel field, an under-saturated black-oil model producing since July 2001 with 6 producers and 2 water injection wells. The history-matched production data are static and flowing bottom-hole pressures and water cut.
A previous manual history match had obtained good response at field level and acceptable responses at well by well level.
We applied NA, using the same approach (varying fault transmissibilities) that had been used in the manual history match. We generated 640 Eclipse models that spanned the parameter space and obtained 396 matches that were better than those with the traditional history match. Those results have been achieved in a significant reduction of time.
To correctly quantify the uncertainty in reservoir forecast, we have to obtain the posterior probability of each model - that is the probability of the model given the history data. This is accomplished with a separate code (NAB) running Markov Chain Monte Carlo.
This paper will describe how we obtained the history match, the formulation and choice of misfit definition which is critical to obtain accurate predictions of uncertainty ranges and the use of NAB code to uncertainty estimation.