Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section

Abstract

Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and algorithms are constantly created and analyzed. In geophysics, both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation. In well-logging, ML algorithms are well-suited for lithologic reconstruction problems, once there is no analytical expressions for computing well-log data produced by a particular rock unit. Additionally, supervised ML methods are strongly dependent on a accurate-labeled training data-set, which is not a simple task to achieve, due to data absences or corruption. Once an adequate supervision is performed, the classification outputs tend to be more accurate than unsupervised methods. This work presents a supervised version of a Self-Organizing Map, named as SSOM, to solve a lithologic reconstruction problem from well-log data. Firstly, we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section. We then define two specific training data-sets composed by density (RHOB), sonic (DT), spontaneous potential (SP) and gamma-ray (GR) logs, all simulated through a Gaussian distribution function per lithology. Once the training data-set is created, we simulate a particular pseudo-well, referred to as classification well, for defining controlled tests. First one comprises a training data-set with no labeled log data of the simulated fault zone. In the second test, we intentionally improve the training data-set with the fault. To bespeak the obtained results for each test, we analyze confusion matrices, logplots, accuracy and precision. Apart from very thin layer misclassifications, the SSOM provides reasonable lithologic reconstructions, especially when the improved training data-set is considered for supervision. The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction, especially to recover lithotypes that are weakly-sampled in the training log-data. On the other hand, some misclassifications are also observed when the cortex could not group the slightly different lithologies.