Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques

Abstract

This study investigates the effects of selected petrophysical properties on predicting flowing well bottomhole pressure. To efficiently situate the essence of this investigation, genetic, imperialist competitive and whale optimization algorithms were used in predicting the bottomhole pressure of a reservoir using production data and some selected petrophysical properties as independent input variables. A total of 15,633 data sets were collected from Volvo field in Norway, and after screening the data, a total of 9161 data sets were used to develop apt computational intelligence models. The data were randomly divided into three different groups: training, validation, and testing data. Two case scenarios were considered in this study. The first scenario involved the prediction of flowing bottomhole pressure using only eleven independent variables, while the second scenario bothered on the prediction of the same flowing bottomhole pressure using the same independent variables and two selected petrophysical properties (porosity and permeability). Each of the two scenarios involved as implied in the first scenario, the use of three (3) heuristic search optimizers to determine optimal model architectures. The optimizers were allowed to choose the optimal number of layers (between 1 and 10), the optimal number of nodal points (between 10 and 100) for each layer and the optimal learning rate required per task/operation. the results, showed that the models were able to learn the problems well with the learning rate fixed from 0.001 to 0.0001, although this became successively slower as the leaning rate decreased. With the chosen model configuration, the results suggest that a moderate learning rate of 0.0001 results in good model performance on the trained and tested data sets. Comparing the three heuristic search optimizers based on minimum MSE, RMSE, MAE and highest coefficient of determination (R²) for the actual and predicted values, shows that the imperialist competitive algorithm optimizer predicted the flowing bottomhole pressure most accurately relative to the genetic and whale optimization algorithm optimizers.