A5029725235- Reservoir Engineering and Simulation Methods
- Geological Modeling and Analysis
- Hydraulic Fracturing and Reservoir Analysis
- Seismic Imaging and Inversion Techniques
- Soil Geostatistics and Mapping
- Seismic Waves and Analysis
- Hydrocarbon exploration and reservoir analysis
- Spectroscopy and Chemometric Analyses
- Geochemistry and Geologic Mapping
- Advanced Computational Techniques and Applications
- Underwater Acoustics Research
- Geographic Information Systems Studies
- Enhanced Oil Recovery Techniques
- Geomagnetism and Paleomagnetism Studies
- Gaussian Processes and Bayesian Inference
- Drilling and Well Engineering
- Semantic Web and Ontologies
- Neural Networks and Applications
- Mineral Processing and Grinding
- Time Series Analysis and Forecasting
- Face and Expression Recognition
Petrobras (Brazil)
2011-2023
Accurate prediction of the spatial distribution subsurface permeability is a fundamental task in reservoir characterization and monitoring studies for hydrocarbon production CO 2 geologic storage. Predicting over large areas challenging, due to their high variability anisotropy. Common approaches modeling generally involve deterministic calculations from porosity using precalibrated rock-physics models (RPMs) or geostatistical cosimulation methods that reproduce observed experimental...
Geostatistical seismic rock physics amplitude-versus-angle (AVA) inversion allows the joint prediction of and fluid properties from reflection data. In these methods, model perturbation update occur iteratively in petrophysical domain. A facies-dependent precalibrated is applied to simulated calculate elastic properties. Synthetic data are computed models. The models calibrated at well locations act as link between domains, remaining unchanged during procedure: convergence geological...
Analyzing data with latent spatial and/or temporal structure is a challenge for machine learning. In this paper, we propose novel nonlinear model studying dependence structure. It successfully combines the concepts of Markov random fields, transductive learning, and regression, making heavy use notion joint feature maps. Our conditional field regression able to infer states by combining limited labeled high precision unlabeled containing measurement uncertainty. manner, can propagate...
Abnormal pore pressures can result in drilling problems such as borehole instability, stuck pipe, circulation loss, kicks, and blowouts. Gradient pressure prediction is of great importance for risk evaluation planning new wells early stages development production oil reservoirs. In this paper, a stochastic simulation with point distributions method presented to integrate uncertain data cube characterization. The consists the use direct sequential distributions. Wells data, case, are...
Flow in a reservoir is controlled predominantly by connectivity of permeability extremes, such as those associated with clear sand channels and shale layers. These elements usually feature complex spatial patterns which are difficult to describe two-point statistics. Furthermore, specific relationships between the facies often an important factor geology, requiring use simulation methods capable reproducing these associations order generate reliable models. In this work, we were able bestow...
O objetivo deste trabalho é de analisar a tecnologia dos metadados como ferramenta deGestão do Conhecimento. É apresentado, exemplo, um modelo dados georeferenciadode metadados, e aplicativo, denominado Mapoteca Digital, que visa o gerenciamento econtrole documentos gráficos utilizados nas áreas exploração produção petróleo daPetrobras.Palavras-chave: Datamining,
Summary Geostatistical cosimulation algorithms provide values for multiple oil reservoir features at unknown locations. Cosimulation can consider primary data (as sampled in wells) and secondary readings from seismic) combined running the simulation. Seismic less precise information than those acquired wells. However, first is exhaustively available (at all grid node locations) along study region. In order to generate more accurate simulations data, it necessary transform histogram...
Summary Accurate predictions of the spatial distribution permeability in subsurface is fundamental reservoir characterization for several tasks (e.g., CO2 injection and storage monitoring or natural resources characterization). Nonetheless, modelling particularly challenging, due to its strong variability, anisotropy dependency on factors. The most common approaches involve deterministic estimations from rocks’ porosity, using pre-calibrated rock physics models, data-driven stochastic...