ANN Powered Virtual Well Testing

Abstract

Abstract Due to the drying up of old oil fields throughout the globe, the age of easy oil is over and the newly discovered fields have reservoirs with complex heterogeneous media. The reservoir parameters are identified indirectly by correctly interpreting well test model which is recognized by the feature of pressure derivative curves. Well testing involves creation of disturbance in fluid flow by injecting liquids and simultaneously recording the pressure transient data. Lost production, equipment and personnel costs turn well testing as highly cost intensive job making it difficult to cover all the important wells in a particular field. But with the advent of artificial neural networks (ANN) it is now possible to generate synthetic pressure transient data. This technique provides a basis to leach out detailed information from the available pressure transient data and it doesn't eradicate the need for actual well tests. This technique can also prove to be very vital in cases where equipment breakdown may have taken place and full set of data couldn't be availed. This simulated well testing involves training of a neural network from pressure transient data obtained from designated wells in the field, which has the potential to generate pressure transient responses at other well sites where no well test has been conducted. In this paper a 3 layer multi-layer perceptron (MLP) Time Delay Neural Network - NARX model has been designed working on resilient backpopagation algorithm for training. Cubic Spline Interpolation has been used from enriching the data before feeding it to NARX model. A simulated example which highlights the efficiency of NARX model in attaining accurate synthetic pressure transient data has been discussed. The neural network is successful in predicting well test interpretation model. The ANN thus produces expeditious and reliable synthetic data which has the potential to revamp the industry.