Synthetic Graphic Well Log Generation Using an Enhanced Deep Learning Workflow: Imbalanced Multiclass Data, Sample Size, and Scalability Challenges

Abstract

Summary The present study introduces an enhanced deep learning (DL) workflow based on transfer learning (TL) for producing high-resolution synthetic graphic well logs (SGWLs). To examine the scalability of the proposed workflow, a carbonate reservoir with a high geological heterogeneity has been chosen as the case study, and the developed workflow is evaluated on unseen data (i.e., blind well). Data sources include conventional well logs and graphical well logs (GWLs) from neighboring wells. During drilling operations, GWLs are standard practice for collecting data. GWL provides a rapid visual representation of subsurface lithofacies to establish geological correlations. This investigation examines five wells in a southwest Iranian oil field. Due to subsurface geological heterogeneities, the primary challenge of this research lies in addressing the imbalanced facies distribution. The traditional artificial intelligence strategies that manage imbalanced data [e.g., the modified synthetic minority oversampling technique (M-SMOTE) and Tomek link (TKL)] are mainly designed to solve binary problems. However, to adapt these methods to the upcoming imbalanced multiclass situation, one-vs.-one (OVO) and one-vs.-all (OVA) decomposition strategies and ad-hoc techniques are used. Well-known VGG16-1D and ResNet18-1D are used as adaptive very-deep algorithms. Additionally, to highlight the robustness and efficiency of these algorithms, shallow learning approaches of support vector machine (SVM) and random forest (RF) as conventional facies classification methods are also used. The other main challenge is the need for enough data points to train the very deep algorithms, resolved through TL. After identifying a blind well, the other four wells’ data are entered for model training. The average kappa statistic and F-measure, as appropriate imbalance data evaluation metrics, are implemented to assess the designed workflows’ performance. The numerical and visual comparison analysis shows that the VGG16-1D TL model performs better on the blind well data set when combined with the OVA scheme as a decomposition technique and TKL as a binary imbalance data combat tactic. An average kappa statistic of 86.33% and a mean F-measure of 92.09% demonstrate designed workflow superiority. Considering the prevalence of different imbalanced facies distributions, the developed scalable workflow can be efficient and productive for generating SGWL.