Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network.

Experimental and digital investigations of heterogeneity in lower cretaceous carbonate reservoir using fractal and multifractal concepts.

Characterization and prediction of reservoir heterogeneity are crucial for hydrocarbon production. This study applies the multifractal theory using both numerical and experimental data to characterize quantitatively the heterogeneity of pore structures in Lower Cretaceous limestone reservoir from the United Arab Emirates. Fractal dimensions calculated from three dimensional digital images showed good correlation (R =  + 0.

Simulation and Validation of Porosity and Permeability of Synthetic and Real Rock Models Using Three-Dimensional Printing and Digital Rock Physics.

A standard digital rock physics workflow aims to simulate petrophysical properties of rock samples using few millimeter size subsets scanned with X-ray microtomography at a high resolution of around 1 μm. The workflow is mainly based on image analysis and simulation procedures at a subset scale leading to potential uncertainties and errors that cannot be quantified experimentally. To overcome the gap between scales, we propose to integrate three-dimensional (3D) printing technology to generate enlarged subsets at a scale where experimental measurements are feasible to validate simulated results.