Josue Fonseca | Searcharxiv

arXiv2026

Synthetic Well Log Generation with Preserved Multivariate Correlations and Vertical Facies Stacking Patterns

We present a novel procedure for generating synthetic well logs that simultaneously preserves multivariate correlations among petrophysical properties (Density, P-Sonic, S-Sonic) and vertical stacking patterns of electrofacies. The methodology integrates Markov chain models, autoencoder-based dimensionality reduction, and Markov chain Monte Carlo (MCMC) sampling in latent space. Application to a real turbidite reservoir dataset demonstrates that the framework successfully sustains fundamental rock physics relationships and generates geologically realistic vertical heterogeneity consistent with actual well log measurements. This technique addresses critical data scarcity in machine learning applications for seismic interpretation while enabling credible synthetic seismogram generation for scenario testing and uncertainty quantification in petroleum exploration and field development.

physics.geo-ph↗

arXiv2023

Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction

Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models that integrate the geo-history of a basin, rock physics definitions, well log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After data assimilation, the accepted set of posterior sub-surface models yields uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic traveltimes criteria as computationally faster proxies for analyzing the seismic data likelihood when employing BGBM. The proposed surrogate schemes weigh the prior basin model results with the available seismic data with no need to perform expensive seismic depth-migration procedures for each Monte Carlo realization. Furthermore, we apply BGBM in a real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematics criteria. BGBM implementation with the novel seismic data assimilation proxies is compared with a computationally expensive benchmark approach. Moreover, we validate and compare the outcomes for predicted pore pressure with mud-weight data from a blind well. The fast proxy of analyzing the depth-positioning of seismic horizons proposed in this work yields similar uncertainty quantification results in pore pressure prediction compared to the benchmark. These fast proxies make the BGBM methodology efficient and practical.

physics.geo-ph↗