Reservoir horizontal principle stress prediction using intelligent fusion model based on physical model constraints: a case study of Daji Block, Eastern Ordos Basin, North China

Horizontal principal stress is a fundamental parameter for reservoir reconstruction. For improving single well productivity, accurate evaluation of reservoir stress characteristics is of great importance. One of the main challenges in predicting the magnitude of the in situ stress is how to obtain the rock mechanical parameters accurately. An intelligent fusion model was proposed to predict rock mechanical parameters to address the issue that traditional approaches are not very reliable at predicting the rock mechanical parameters of complex lithology reservoirs, using transitional shale reservoir rocks as the research object. Machine learning algorithms such as nearest neighbor regression, support vector machine, and random forest were selected to construct intelligent fusion models of different rock mechanics parameters based on the laboratory test data. Finally, the logging profile of transitional shale reservoir horizontal principal stress in the study area was obtained under the constraints of the empirical physical model and measured in situ stress data. The results showed that the fusion models outperformed the single model on rock mechanics parameters and had higher accuracy in both training and test sets, meeting the engineering requirements for predicting the horizontal principal stress in the study area.