Continuous reservoir simulation model updating and forecasting using a markov chain monte carlo method

摘要

Currently, effective reservoir management systems play a very important part inexploiting reservoirs. Fully exploiting all the possible events for a petroleum reservoir is achallenge because of the infinite combinations of reservoir parameters. There is muchunknown about the underlying reservoir model, which has many uncertain parameters.MCMC (Markov Chain Monte Carlo) is a more statistically rigorous sampling method,with a stronger theoretical base than other methods. The performance of the MCMCmethod on a high dimensional problem is a timely topic in the statistics field.This thesis suggests a way to quantify uncertainty for high dimensional problems byusing the MCMC sampling process under the Bayesian frame. Based on the improvedmethod, this thesis reports a new approach in the use of the continuous MCMC methodfor automatic history matching. The assimilation of the data in a continuous process isdone sequentially rather than simultaneously. In addition, by doing a continuous process,the MCMC method becomes more applicable for the industry. Long periods of time torun just one realization will no longer be a big problem during the sampling process. In addition, newly observed data will be considered once it is available, leading to a betterestimate.The PUNQ-S3 reservoir model is used to test two methods in this thesis. The methods are:STATIC (traditional) SIMULATION PROCESS and CONTINUOUS SIMULATIONPROCESS. The continuous process provides continuously updated probabilistic forecastsof well and reservoir performance, accessible at any time. It can be used to optimizelong-term reservoir performance at field scale.