A physically-modified spectral model for prediction of sand wave migration

Sand waves are rhythmic bedforms existing in shallow seas and typically induced by the interaction of currents and topography. In this study, a physically-modified spectral model for simulating small-scale sand wave migration is proposed by integrating non-linear spectral analysis, typhoon wind field and sediment transport theory. We introduce a power-average velocity to modify the spectral analysis, which gives explicit physical meanings to the morphological method and improves its stability and reliability. Then, the effects of typical and extreme weather are decoupled and the short-term migration caused by typhoons is removed from the total migration. Results show that a single typhoon will cause large sand wave migration, but the migration caused by multiple typhoons in a long period of time will cancel each other out in the measured data. Moreover, the bilateral reverse migration trend of one-group sand waves in a small scale (~3 km) is captured, which is consistent with field data and previous researches. Compared with existing researches, the proposed model is able to identify the influences of extreme conditions on sand wave migration and provide a feasible and high efficiency way to predict sand wave migration when the available hydrodynamic data is limited

A core-scale reconstructing method for shale

Characterization of shale cores with low and anisotropic permeability is complicated, due to the presence of multiscale pore structure and thin layers, and defies conventional methods. To accurately reproduce the morphology of multiscale pore structure of the shale core, a novel core-scale reconstructing method is proposed to reconstruct 3D digital-experimental models by means of the combination of SEM, EDS images, nitrogen adsorption and pressure pulse decay experiment result. In this method, the multiscale and multicomponent reconstructing algorithm is introduced to build the representative multiscale model for each layer, which can describe the complex 3D structures of nano organic pores, micro-nano inorganic pores, micro slits and several typical minerals. Especially, to reproduce the realistic morphology for shale, the optimization algorithm based on simulated annealing algorithm uses the experimental data as constrain conditions to adjust and optimize the model for each layer. To describe the bedding characteristics of the shale core, bedding fractures are constructed by analysis of the mineral distribution in the interface of two layers, and then the representative models for different layers are integrated together to obtain the final core-scale digital-experimental model. Finally, the model is validated by computing its morphological and flow properties and comparing them with those of the actual 3D shale sample. This method provide a way for systematically and continuously describe the multiscale and anisotropic pore structure (from nm-cm) of the shale core, and will be helpful for understanding the quality of the shale reservoir

A multiscale reconstructing method for shale based on SEM image and experiment data

Owing to the presence of multiscale pore structures, characterization of laminated shales is both extremely difficult and substantially different from that of conventional reservoirs, and defies conventional methodologies. In this paper, a multiscale reconstructing method for shale is proposed to generate 3D layer representative elementary volume (lREV)-scale digital-experimental models to characterize the multiscale pore structure of the shale by means of the combination of a large area SEM image, nitrogen adsorption and pressure pulse decay experiment result. In this method an improved multiscale superposition algorithm is introduced to integrate the reconstructed complex models from nanoscale to mesoscale together, and it can preserve the details and main features enormously of each typical component (nanoscale organic pores in organic matter and pyrites, micro-nano inorganic pores and micro slits) in the shale. Especially, to accurately reproduce the realistic morphology for shale, the proposed method uses the experimental pore size distribution and permeability as constrain conditions to adjust and optimize the lREV-scale digital-experimental model. Our proposed method was tested on Longmaxi and Wufeng shale samples, and the reconstructed lREV-scale digital-experimental model are proved to accurately describe the representative structure of the complex multiscale pore space of the typical layer of the shale. The success of this method provides a promising way for reconstructing more realistic model to continuously and systematically characterize the pore (slits) structure from the nanopore-scale to the lREV-scale. It can advance the understanding of the various gas transport mechanisms at different scales and will be helpful for understanding the quality of the shale reservoir

A hybrid method for reconstruction of three-dimensional heterogeneous porous media from two-dimensional images

The heterogeneous pore space of porous media strongly affects the storage and migration of oil and gas in the reservoir. To accurately reproduce complex pore structure validated with experiment data, a hybrid method which combines the CCSIM-TSS method (the cross correlation based simulation-three step sampling method) with the optimization method is proposed to reconstruct stochastically 3D models of the heterogeneous porous media based on 2D images and some constrain conditions. Moreover, the new system in the hybrid method is generated by exchanging the objective points and boundary points of pore and matrix based on two algorithms. Then the hybrid method are tested on the two typical samples (carbonate rock, tight sandstone samples with micro-nanopores). Quantitative comparison is made by computing various statistical and petrophysical properties for the reconstructed models, as well as the original samples. It was found that the hybrid method generates an ensemble of stochastic realization that honor the constrain conditions (permeability data, pore-size distributions and the connectivity). Generating realization of tight sandstones that match the experimental data based on 2D SEM image shows that this method may be then used for more accurate characterization of tight sandstones oil reservoir and analysis of their pore network

Big Data in the Big D

Presentation paper for the 2017 International Conference on Knowledge Management. This paper uses a link prediction algorithm based on Node Embedding to map disease nodes in order to discover disease associations more precisely and predict the risk of certain diseases

Identifying the comprehensive pore structure characteristics of a rock from 3D images

Characterization of pore structure in reservoirs plays a significant role in predicting properties of rocks and classifying the reservoirs. The focus is on determining comprehensive geometric and topological parameters of pore structure. X-ray computerized tomography scanning provides a 3D image of the pore structure. However, these images cannot directly generate pore structure parameters without numerical characterization of the images. A new method is developed to determine comprehensive geometric and topological parameters of pore structure from images. These parameters were determined by utilizing common mathematical morphology operations to segment the entire pore space into a series of space blocks of different radii, subsequently identifying these blocks as pores and throats based on the proposed morphological features of pore and throat. The parallel scheme was also studied to obtain the highest efficiency. Thereafter, the characteristics of pore structures and the performance of the method were assessed for various samples. The results were compared to the results calculated by other methods. Results showed that the method produced reliable pore structure characteristics for a wide variety of rocks. Additionally, in order to discuss whether our results can be used as the basic input data for other researches, an example about two-phase flow simulation was made and a comparison of simulation results and experimental data was made. Our results were a competent choice for other studies. Also, after proposing the detection criteria of pore and throat, the subsequent detecting procedure is without artificial adjustable parameters, making the method convenient to use. This method can comprehensively, efficiently, accurately, and conveniently characterize the pore structure based only on images

Characterization of pore structures and gas transport characteristics of Longmaxi shale

Multiscale pore structures of shale conjunctionally determine the gas storage and transport properties of gas shale reservoirs. To investigate the complex pore structures and gas transport in the ultra-tight porous media, a combination of petrophysical and scanning measurements is adopted in this study. Five shale samples of the main productive layers from four wells (Long 1(1) sub-members of Longmaxi Formation) in southeast Sichuan Basin, China are analyzed. Based on scanning electron microscope (SEM) image, different types of organic pores and inorganic pores are quantitatively analyzed. A method for identification of organic and inorganic pores from SEM images is developed. And a comparison between the pores identified from SEM and gas adsorption (N-2 and CO2 adsorption) is conducted. Results show that the main part of inorganic pores are mesopores, and the organic pore size distributions tend to be lognormal with the peak pore size from 10 nm to 30 nm. Results also show that the gas storage space obtained by SEM is several times to several tens of times larger than that obtained by gas adsorption method. The permeability of OM and shale cores is also compared. The permeability is strongly anisotropic and all those samples show a significant non-Darcy effect. Finally, the relationship between the pore structures and gas transport behavior is studied, and the high quality shale samples are identified considering their gas storage and transport behaviors

Building a multilingual test collection for metadata records

This paper describes the principles and processes of building a test collection that enables multilingual information retrieval for digital metadata records. The collection includes a multilingual collection of 1,005,752 metadata records, their Chinese and Spanish machine translation results, 45 topics generated through crowd- sourcing, and their relevant judgments

Big Data in the Big D

Presentation for the 2017 International Conference on Knowledge Management. This presentation describes the use of a link prediction algorithm based on node embedding to map disease nodes and reveal relationships among various diseases

Rapid Evaluation of the Permeability of Organic-Rich Shale Using the 3D Intermingled-Fractal Model

Shale possesses abundant micro/nanopores. Most micro/nanopores that exist in organic-rich shale are organic pores and mainly developed in organic matter. The pore distribution in matrix space significantly affects gas percolation and diffusion. Pore-size distribution possesses a self-similar, or fractal, property. The pore space and gas permeability of shale can be easily rebuilt and evaluated, respectively, using fractal theory. In this work, a 3D intermingled-fractal model (3D-IFM) is successfully built using scalable scanning-electron-microscopy (SEM) images of shale samples. 3D-IFM is made up of several components, including organic pores in organic matter and in pyrites, inorganic pores, slits, and matrix. An improved pore-connective-calculation method is also introduced to evaluate the apparent gas permeability of the shale model. The proposed 3D-IFM rapid-permeability-evaluation method for organic-rich shale is valid and useful and considers the main components of shale. This method can rapidly evaluate apparent gas permeability and simplify the apparent-gaspermeability-calculation process. Thus, the method provides a promising means of rapidly evaluating apparent gas permeability