查看更多>>摘要：In this work, a data-driven surrogate to high-fidelity numerical simulations is developed to replace the numerical simulations of porous media., This model can accurately predict flow fields for new sets of simulation runs by learning the communications among grid cells in the numerical domain. Because of the many possible random arrangements of particles and their orientation to each other, generalization of permeability with high accuracy is not trivial - nor is it practical using conventional means. Furthermore, building a comprehensive database for different grain/pore arrangements is impossible because of the cost of running numerical simulations to generate the database that represents all possible arrangements. The objective is to predict grid-level flow fields in porous media as a priori to determine the permeability of porous media. This work is a continuation of our previous research. The rationale is that once the detailed grid-level dynamics can be accurately predicted using a data-driven approach, for any configuration/topology of the porous media, the detailed dynamics could be predicted without any need for new expensive new numerical simulation runs. In this work, we improved previous work by accurately predicting permeability of the porous media, irrespective of the grain density, pore/grain shape, with a significant reduction in computational time as opposed to previous work, which was limited to a unique grain shape/size. The surrogate model is developed by employing a deep learning technique using high-fidelity numerical simulations for two-dimensional porous media consisting of circular grains, generated by varying the number and size of the circular solid grains. The robustness of the developed model is then tested for numerous variations of porous media - generated by changing the number and size of the solid grain angularity and elongation - which have not been used for developing the model. The deep convolutional neural network employed in this work combines deep U-Net and ResNet structures to capture context and enable precise localization while avoiding issues in training caused by vanishing gradients.
查看更多>>摘要：Bleaching of red beds, a type of hydrocarbon-induced alteration, is generally attributed to redox reactions between ferric iron minerals and hydrocarbon-bearing solutions. Herein, we report sandstone bleaching occurs interbedded with the coal- and dark mudstone-bearing strata at shallow depths below two unconformity surfaces separating sandstone formations of Triassic-Jurassic age in the Ordos Basin, China. Field observations, petrography, and geochemistry suggest that uplift events controlled the formation of red beds via supergene alteration and bleaching via hydrocarbon circulation. The color of sandstones below the unconformities grade from red, yellow, and white colors at shallow depths (few meters to tens of meters) to dark yellow, gray-green and gray colors at deeper depths. Organic matter (carbonaceous plant debris) and pyrite in the unaltered sandstone gave rise to the gray color. The red/yellow sandstones are characterized by the presence of extensive iron oxide/ hydroxide grain coatings, exhibit intense dissolution and extensive kaolinization of detrital feldspar and biotite and lithics and are mainly composed of detrital quartz. The white, bleached sandstone presents similar petrographic characteristics as the unbleached sandstone except for a lack of iron oxide/hydroxide cements. 818OVSMOW (9.8%, to 15.8%,) and 8DVSMOW (- 103%, to -119%,) values of kaolinite, and chemical indices of alteration of sandstones indicate a weathering origin for the kaolinite and the dissolution of labile minerals in the red and yellow sandstones. The original color of the bleached sandstone was gray during very early diagenesis and shifted to red/yellow due to the oxidation of pyrite and ferromagnesian silicate minerals (e.g., biotite) into hematite or goethite cements by the meteoric water circulation during regional uplift following the deposition of each formation. Supergene alteration associated with unconformities also created significant secondary porosity, and allowed later hydrocarbons to flow along the unconformities. The lithological properties of the weathered rocks below unconformities are highly heterogeneous both vertically and laterally and have a significant influence on fluid flow. This study provides direct evidence for hydrocarbon migration along unconformities and improves understanding of fluid-rock interaction in subsurface reservoirs.
Santos, AndersonScanavini, Helena F. A.Pedrini, HelioSchiozer, Denis Jose...
11页
查看更多>>摘要：The upscaling of geological properties is a fundamental requirement to construct a suitable simulation model since the geological model typically contains millions of cells, which makes it computationally difficult to simulate it on such a scale. The process consists of a scale transfer that adapts the petrophysical properties of a high-resolution grid to a coarser grid. Nevertheless, this is not a trivial operation, especially for absolute permeability, which is a non-additive property. One of the most reliable methods is to perform a flow simulation on fine-scale cells that correspond to the coarse block and derive the single permeability value that reflects the same flow value. Still, this is a very time-consuming method and depends on the imposed boundary conditions. This work aims to take advantage of recent advances in artificial intelligence to produce results with similar or better quality of flow-based methods, and more adaptive. It handles models with multiple geological realizations by employing machine learning to capture patterns from a subset of scenarios and use it to generalize for all others. The methodology is divided into two stages - local and global. In the first one, a deep neural network is trained with a fraction of geological realizations using the flow-based results as a reference. In the second stage, clustering analysis is employed along with a neural network optimization to learn an adjustment procedure for the coarse simulation model concerning the cumulative field production predicted by the fine model simulation. Afterward, the trained network performs the upscaling of remaining realizations, but more efficiently in terms of computational time and providing better output in terms of production and injection. The method was applied to a benchmark model and the experimental results demonstrate that the local technique was capable of reproducing very similar values to the flow-based upscaling using only one scenario in training and at the global stage the coarse model was improved even further matching the field oil production forecast of the fine model. Different scenarios were used for training and testing and the results were consistent showing no bias towards a specific configuration and capability of generalization to different scenarios. Ultimately, the proposed artificial intelligence approach performed an accurate upscaling, surpassing the reference approach with forecast production similar to the fine model, and also fast to compute when considering multiple geological realizations, since it reduces the required numerical simulations to a fraction of the total.
查看更多>>摘要：The formation water evaporates seriously because of the drastic pressure change in near-well zone during the exploitation of deep high temperature gas reservoir. The crystallization salt precipitates in the formation when the salt concentration of formation water increases to solubility limit, which results in the decrease of reservoir porosity and permeability. At present, there is a lack of the prediction and process design of salting out in deep high temperature gas reservoirs. The evaporation salting out experiment of formation water was carried out firstly in this paper, and the volume model of salting out zone was established based on the water content model of natural gas, the porosity and permeability model of reservoir, and the kinetics model of salting out. And the characterization method of predicting porosity and permeability of high temperature gas reservoir after salting out was established by fitting and modifying the model finally, which could provide a theoretical basis for salting out plugging formation in depressurization production of gas reservoirs and laid a foundation for the efficient development of deep high temperature gas reservoirs.
NSTL
Elsevier