Abstract
Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.
Highlights
Steam flooding is an effective way for heavy oil development
In order to obtain accurate performance prediction of steam flooding reservoir, many scholars have established a large number of analytical, empirical models and numerical method in the past decades
Under the condition of 500 prior models, the average relative error of history matching and prediction results of oil production rate is less than 5%, and the average relative error of cumulative oil production is less than 1%
Summary
The effective and efficient history matching and production performance prediction can help to judge the development stage and carry out phased adjustment for more oil recovery of steam flooding reservoir. In the past few decades, many effective algorithms including Kalman and parameterization algorithms have been developed for historical matching, which have achieved good results These algorithms did not work well when applied into steam flooding reservoir. Shortening the number of numerical simulation or finding a surrogate prediction model is the main direction to improve the efficiency of history matching for steam flooding reservoirs. Similar to proxy model, the machine learning can improve greatly the prediction of the production performance Such methods ignore the physic of reservoir and fail either by generating “nonphysical” output solutions or when presented with data not anticipated in the training data set.
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