Unsupervised Learning Based on Prior Knowledge Enhancement for the Classification and Evaluation of Geological–Engineering Sweet Spots

Summary The accurate evaluation and prediction of sweet spots are important for realizing scale-effective development of shale oil and gas. Sweet-spot evaluation methods can be categorized into physical model–based methods and data-driven methods. The construction of physical models is usually associated with high labor and material costs, and, coupled with the considerable nonhomogeneity of reservoirs and the involvement of numerous evaluation elements, obtaining physical models with sufficient resolution and accuracy to satisfy actual requirements is challenging. Commonly used labeled data-driven sweet-spot evaluation methods rely heavily on the subjectivity and consistency of user judgment, and most methods require large-scale, high-quality sample training data sets for constrained training, which are rarely applied in geological sweet-spot (GSS)–engineering sweet-spot (ESS) evaluation without labeled samples. In this paper, on the basis of an unsupervised machine learning framework, we developed an integrated GSS-ESS evaluation method for downscaling, clustering, and grading shale horizontal wells via the integration of a priori knowledge and standardized variable-distance evaluation. First, evaluation indices were selected according to the characteristics of the mine data, and the ideal point labels of the evaluation indices were obtained via a priori knowledge and then fused to generate an unsupervised integrated evaluation data set. A standardized variable-distance unsupervised learning clustering model was introduced to calculate the proximity distance of the evaluation indices of GSSs-ESSs to the ideal points of horizontal wells to compress and downscale the evaluation indices into 1D proximity degrees. The evaluation of the proximity distance facilitates the hierarchical assessment of horizontal well sweet spots. A comprehensive evaluation of the sweet spot of fractured horizontal well sections is realized with unsupervised data sets, and the accuracy can reach 86.43% of that obtained by the supervised fuzzy comprehensive evaluation. The results of applying the comprehensive sweet-spot rating to the fracturing segment selection cluster design show that the higher the rating, the better the test production, which indicates the accuracy of this method and provides a new way of thinking for the prediction of sweet spots of unconventional oil and gas resources.