Predicting waterflood responses with decision trees

Adding new wells and new production in existing fields under Enhanced Oil Recovery (EOR) is particularly important in mature fields that are characterized by a long history of field activity. Different drilling programs, a variety of field treatments, well conversions and new injectors add many layers of complexity and uncertainty to the existing effects of geological, completion and production factors. Surveillance and prediction of responses caused by injected fluids in fields with dozens of patterns and hundreds of wells calls for computer-based systems that estimate responses based on numerical and statistical solutions. This is especially important when geological understanding is very weak (i.e. no core, no log data). This paper shows how results from EOR surveillance programs can be integrated with geological data. Furthermore, this paper shows how to build predictive models for production estimates based on injection responses and geology. These models support a two to three times more accurate selection of wells with high oil production during EOR than historically implemented selections. Included in the paper are practical tips on how to select the best model and derive solutions with decision trees that are equivalent to sets of English-based rules. Solutions from decision trees are compared with solutions from logistic regression and neural networks. This comparison deals with the statistical accuracy of model predictions, interpretation ability and assisting in applying these models to support field decisions.