Integrating Multi-Source Experiment Data and Variational Diffusion Model for Intelligent Constructing Digital Core: A Case Study of Sandstone Reservoir in the Turgay Basin Central South Kazakhstanc

Abstract Digital core technology is increasingly indispensable for the purpose of efficient development of sandstone reservoir in the Turgay basin central south Kazakhstan which could conduct multiple analyses and experiments without damaging the actual rock core, thereby enhancing efficiency, cutting costs, and minimizing resource wastage. The conventional methods for constructing the digital core mainly include high costs, while the construction cost of physical experimental methods is relatively high and the numerical reconstruction methods exhibit lower efficiency. An intelligent digital core construction framework combining multi-source experimental data with generative deep learning algorithms called MSED-GDL is proposed for quick and efficient reconstruction of the digital core. The framework begins with a U-net based autoencoder that encodes three-dimension (3D) images into a compact latent variable space, enhancing computational efficiency. This is followed by micro-seepage simulation and other processing on the 3D core database to gather multimodal data, including porosities, permeabilities, two-dimension (2D) thin section images, etc. A latent diffusion model is then employed to reconstruct high-fidelity 3D digital cores in seconds, capable of handling incomplete multimodal data inputs. To validate the MSED-GDL framework, 10000 digital core samples with the property of one typical sandstone reservoir in the Turgay basin central south Kazakhstan and a resolution of 256x256x256 were generated using Perlin noise and threshold filtering. Meanwhile, an autoencoder was adopted to compress these into a 16x16x16 tensor representation. Subsequently, the seepage simulations yielded multimodal data—comprising section images, mercury intrusion curves, phase permeability curves, permeabilities, etc. in various data forms (images, series, scalars). The latent diffusion model reconstructed the 3D digital cores using reverse diffusion and iterative denoising, achieving a reconstruction with minimal deviation from actual samples. The permeability, porosity, average throat radius, pore throat ratio, pore throat distribution, and relative permeability curve of the reconstructed digital core have strong consistency with multi-source experimental data indicating that the reconstructed three-dimensional digital core, in terms of geological, physical, and geometric, and other parameters that affect underground flow characteristics, has statistical significance similar to the original digital core and can serve as an approximate replacement for the original digital core.