A training image optimization method in multiple-point geostatistics and its application in geological modeling

TL;DR: In this paper, an adaptive spatial sampling method is introduced, and an iterative simulation strategy is adopted, in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability.

TL;DR: In this paper, the authors summarized the recent advances in the migration mechanisms and stimulation mechanisms of horizontal well high pressure forced soaking technology in the reservoirs and concluded that due to the diversity of shale mineral composition and the complexity of crude oil composition, the stimulation mechanism and effect of this technology are not clear in shale reservoir.

TL;DR: In this paper, an equivalent upscaling method of geologic model based on tortuous paths under the effects of muddy interlayer has been proposed, where single muddy interlayers in each coarse grid are tracked and identified, and the average length, width and proportion of the interlayer is determined by using the geological connectivity tracing algorithm.

TL;DR: In this article, a multi-point geostatistical modeling flowchart is presented for the construction of a fine deep-water turbidity channel model, where the Snesim method is utilized to construct the models using the sorted compatible training images with a same set of parameters.

TL;DR: Investigating stochastic models for simulation, and basic morphological concepts, and Gaussian variations: some basic notions.

TL;DR: An entirely new approach to stochastic simulation is proposed through the direct simulation of patterns, which borrows heavily from the pattern recognition literature and simulates by pasting at each visited location along a random path a pattern that is compatible with the available local data and any previously simulated patterns.

TL;DR: In this article, the authors proposed a new approach that combines the easy conditioning of pixel-based algorithms with the ability to reproduce shapes of object-based techniques, without being too time and memory demanding.

TL;DR: In this article, an aquifer modeling methodology that combines geologic-process models with object-based, multiple-point, and variogram-based geostatistics is presented.