Application of the combinatorial generalization ability estimates in planning tracer testing studies in oil and gas fields

The article discusses the limitations of using interference tests to construct a tracer testing program as a list of injection-production wells pairs. The decision tree classifier proposed in earlier works is considered as more preferred method for this task. The disadvantages of the existing tree learning algorithm is that it tends to overfit, especially in conditions of small data sets. In this work, we suggest to use techniques from combinatorial theory of overfitting, namely the complete cross-validation and the expected overfitting, as splitting criteria in decision tree nodes to enhance the algorithm's generalization ability. The approach is tested on two fields in Western Siberia, resulting in a statistically significant improvement in the quality of the decision tree and reduced overfitting, leading to more accurate constructing the plan of tracer testing for assessing the presence of hydraulic connectivity between injection and production wells. The application of combinatorial theory of overfitting to decision tree classifiers offers a promising avenue for enhancing the effectiveness of tracer testing in the oil and gas industry.