Probabilistic Performance Forecasting in History Matching of Low Permeability Reservoirs

摘要

Since conventional history matching aimed at fractured horizontal wells in low permeability oil reservoirs is affected by a number of factors, such as permeability, fracture half-length, conductivity and so on, there often exists ambiguity in production matching. That is to say, when showing the same curves and results, we cannot make a definite decision to judge which parameter displays the matching results. In this paper, Markov Chain Monte Carlo (MCMC) and AM algorithm are presented to improve history matching and then to obtain more accurate probabilistic production forecasting using actual decline production data. First, the AM algorithm, having an advantage of updating parameters simultaneously and constituting a proposal distribution at each new iteration according to the covariance matrix of the previous iterations over the Metropolis-Hasting (MH) algorithm, is employed to gather field production decline data. Furthermore, MCMC method is utilized to develop a Markov Chain, a stochastic process with a series of various parameters and later value usually only related with the most-recent value. Finally, based on this, the history matching is improved and further probabilistic production prediction P10, P50 and P90 are achieved. The results indicate that compared with MH algorithm, the AM algorithm can get a greater acceptance ratio. The Markov Chain for production decline data parameters shows a satisfying mixing. The probabilistic cumulative oil production of P10, P50, and P90 is established for target oilfield in this paper. The curves of production rate versus time and cumulative oil production versus time show that the well-established Markov Chain can successfully match the production decline data and then perfectly predict probabilistic production. The novel point in this paper is that a much more effective AM algorithm substituting for the MH algorithm is adopted to form the Markov Chain to improve history matching. The results manifest that MCMC method has the ability to enlarge the reliability of production forecasts, which has a significant influence on reservoir understanding and management.