Seismic attribute optimization with unsupervised machine learning techniques for deepwater seismic facies interpretation: users vs machines

Machine learning (ML) has many applications within the geosciences, from predicting seismic facies, to automatic fault detection. A variety of machine learning algorithms are commonly employed, among these principal component analysis (PCA) and self-organized maps (SOMs), which provide a fast organization of data into groups that aid in geological interpretation. It is, nevertheless, interesting to note that parametrization choices during algorithm initiation could create a range of reasonable output model responses. The goal of PCA is to reduce a multivariate space down to a computationally more manageable size of variables. But this method relies primarily on the mathematically calculated eigenvectors and does not consider the a priori knowledge of the interpreter. The main motivation of this research is to investigate the impact of a user-controlled selection of attributes to perform SOM for facies classification versus a machine- derived result. Looking at a reflection seismic data of deepwater channel systems in the Taranaki Basin, a variety of attribute classes are systematically examined, including geometric, instantaneous, and textural attributes, in mixed combinations with one another, to understand how input variability alters the resultant SOM classification for deepwater architectural elements and facies characterization. The findings reveal that an appropriate combination of attributes with a clear interpretation objective enhance the SOMs results and facilitates the interpreter understanding of the output classes especially if attributes are previously tested. On the other hand, PCA provides insightful information regarding the contribution of attributes that may not have been initially considered by the interpreter. This study reveals that while ML techniques are a powerful tool for geological interpretation, user control on initial input attributes and to validate output results remain necessary for an optimal interpretation, at least in unsupervised ML methods

Optimization of deepwater channel seismic reservoir characterization using seismic attributes and machine learning

Accurate subsurface reservoir mapping is essential for resource exploration. In uncalibrated basins, seismic data, often limited by resolution, frequency, quality, etc., algorithms become the primary information source due to the unavailability of well logs and core data. Seismic attributes, while integral for understanding subsurface structures, visually limit interpreters to working with only three of them at once. Conversely, machine learning, though capable of handling numerous attributes, is often seen as inscrutable "black boxes," complicating the interpretation of their predictions and uncertainties. To address these challenges, a comprehensive approach was undertaken, involving a detailed 3D model from Chilean Patagonia's Tres Pasos Formation with synthetic seismic data. The synthetic data served as a benchmark for conducting sensitivity analysis on seismic attributes, offering insights for parameter and workflow optimization. The study also evaluated the uncertainty in unsupervised and supervised machine learning for deepwater facies prediction through qualitative and quantitative assessments. Study key findings include: 1) High-frequency data and smaller analysis windows provide clearer channel images, while low-frequency data and larger windows create composite appearances, particularly in small stratigraphic features. 2) GTM and SOM exhibited similar performance, with error rates around 2% for predominant facies but significantly higher for individual channel-related facies. This suggests that unbalanced data results in higher errors for minor facies and that a reduction in clusters or a simplified model may better represent reservoir versus non-reservoir facies. 3) Resolution and data distribution significantly impact predictability, leading to non-uniqueness in cluster generation, which applies to supervised models as well. Strengthening the argument that understanding the limitations of seismic data is crucial. 4) Uncertainty in seismic facies prediction is influenced by factors such as training attribute selection, original facies proportions (e.g., imbalanced data, variable errors, and data quality). While optimized random forests achieved an 80% accuracy rate, validation accuracy was lower, emphasizing the need to address uncertainties and their role in interpretation. Overall, the utilization of ground truth seismic data derived from outcrops offers valuable insights into the strengths and challenges of machine learning in subsurface applications, where accurate predictions are critical for decision-making and safety in the energy sector