Application of the Neural Decision Tree approach for prediction of petroleum production

作者：Li X; Chan C W^*; Nguyen H H

来源：Journal of Petroleum Science and Engineering, 2013, 104: 11-16.

DOI：10.1016/j.petro1.2013.03.018

• artificial neural network
• attribute dependency
• data mining
• decision tree
• petroleum production prediction

Accurate predictions of oil production from wells are important for cost-effective operations in the petroleum industry. Such a prediction can assist petroleum engineers in project design, facilities construction scheduling, economic forecasting, and environment management. However, it is difficult to obtain accurate predictions of oil production due to the complex subsurface conditions of reservoirs. Reservoir engineers typically employ curve fitting techniques for predicting primary production of wells based on existing production data. Instead of using this approach, application of some artificial intelligence techniques for production prediction is explored in this paper. The artificial neural network (ANN) approach is a mathematical modeling technique inspired by biological neural networks. The ANN consists of an interconnected group of artificial neurons, which process information via a learning phase between inputs and outputs so as to find patterns in data. A decision tree learning algorithm such as C4.5 usually considers one variable at a time and ignores interdependencies among input attributes, which reduces its model accuracy. On the other hand, an enhanced decision tree learning approach called Neural Decision Tree (NDT) takes interdependencies among input attributes into consideration and generates a.decision tree for prediction of petroleum production. This paper presents a comparison of prediction results produced from the three machine intelligence approaches of C4.5 model, NDT model and the ANN model. The results show that the NDT model can significantly improve upon the classification accuracy of the C4.5 algorithm. When compared to the ANN approach, the NDT model has a lower classification rate in general but is better able to describe classes with low number of instances.

• 出版日期2013-4