An artificial neural network approach to optimize the water flooding of Bach Ho oilfield, offshore Vietnam
Abstract
The predominant oil production offshore Vietnam originates from the Bach Ho basement reservoir, where the flow regime is highly complicated due to the heterogeneous spatial distribution of petrophysical properties such as porosity, permeability, and water saturation.
Therefore, the conventional reservoir-simulation-based methods for forecasting oil production often yield limited accuracy or require substantial time and effort to optimize the dynamic parameters. Recent advances in machine learning (ML) algorithms enable more rapid and accurate prediction of oil production rates from water injection rates at individual injection wells. Once the oil rate prediction is achieved using ML approach, the waterflooding optimization can then be achieved by any suitable optimization algorithm.
In this research, an artificial neural network (ANN) algorithm is used, yielding very good results: the correlation coefficients between the predicted and actual values are 0.98 and 0.95 for training and testing datasets, respectively. Subsequently, the Gauss-Newton optimization algorithm is applied to determine the optimal water injection rates for each injection well, aiming to enhance oil productivity. The results show that the newly optimized injection schemes yield an average oil production increase of 1.5%.
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