Application of an enhanced decision tree learning approach for prediction of petroleum production

Economic evaluation of a new oil well is important for decision-making in the petroleum industry, and this evaluation is based on a good prediction on a well's production. However, it is difficult to accurately predict a well's production due to the complex subsurface conditions of reservoirs. The industrial standard approach is to use either curve-fitting methods or complex and time-consuming reservoir simulations. In this paper, an enhanced decision tree learning approach called neural-based decision tree (NDT) model is applied in an attempt to investigate its performance in predicting petroleum production. The primary strength of this model is that it can capture dependencies among attributes, and therefore, it is likely to provide an improved or more accurate prediction (Lee and Yen, 2002).This paper presents an application of the NDT model for petroleum prediction. Our models were developed based on the five most significant parameters that affect oil production: permeability, porosity, first shut-in pressure, residual oil and saturation of water. The five parameters were used as input variables, and oil production is the output variable for modeling. Four different models were generated in the modeling process, and each involves a different combination of parameters. First, an overall oil production model is developed using the three geoscience parameters of permeability, porosity and first shut-in pressure. Secondly, two different models, with different input parameters, were developed to predict production in the post-water flooding stage only. The results of the above models indicate that data-driven models may not be effective for classifying the data set. Hence, a trend model was developed in an attempt to improve the effectiveness and accuracy of the predictive model. The result shows that the trend model can provide an improved performance, and its performance is comparable to that of the artificial neural network.