Waterflooded Reservoir Neural Network Performance Evaluation

What is it about?

In this paper, we considered the use of neural networks in the identification and prediction of a waterflooded reservoir consisting of eight injection wells and one production well with a 40% porosity. The data used for the non-linear identification was generated from a reservoir modelled in MATLAB Reservoir Simulation Toolbox (MRST). Likewise, in this study, the effect of number of hidden neurons on the accuracy, Mean Squared Error and oil production prediction of the reservoir was investigated. The study asserted the efficacy of the neural networks as regards to its predictive capacity. For the oil production rate, a mean squared error was recorded to be minimal for 2 hidden neurons as compared to the other three cases of neuron number. For water production rate, 8 hidden neurons were observed to be optimal compared to other cases. Oil and water production rate for a peak NPV value of 3 billion US dollars was recorded to be 2000m3/day and 4500m3/day respectively. The response was optimal for all cases except for the net present value, which requires a more substantial amount of data for the neural network model

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Photo by J K on Unsplash

Photo by J K on Unsplash

Why is it important?

Reservoir waterflooding is a secondary recovery technique used in the production of oil through the use of an enhanced pressure driven state. This pressure driven state is enforced by subjecting the reservoir to a waterflood, which in turn increases the reservoir pressure forcing the trapped oil to flow out to the surface. This enhanced technique is applied when the reservoir pressure in its natural state has being depleted overtime due to early recovery. Waterflooding technique is commonly used due to its effectivity and the fact that water is easily available and cheap to implement

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