Model Regeneration Scheme using a Deep Learning Algorithm for Reliable Uncertainty Quantification of Channel Reservoirs

Reservoir characterization is one of the essential procedures for decision makings. However, conventional inversion methods of history matching have several inevitable issues of losing geological information and poor performances when it is applied to channel reservoirs. Therefore, we propose a model regeneration scheme for reliable uncertainty quantification of channel reservoirs without conventional model inversion methods. The proposed method consists of three parts: feature extraction, model selection, and model generation. In the feature extraction part, drainage area localization and discrete cosine transform are adopted for channel feature extraction in near-wellbore area. In the model selection part, K-means clustering and an ensemble ranking method are utilized to select models that have similar characteristics to a true reservoir. In the last part, deep convolutional generative adversarial networks (DCGAN) and transfer learning are applied to generate new models similar to the selected models. After the generation, we repeat the model selection process to select final models from the selected and the generated models. We utilize these final models to quantify uncertainty of a channel reservoir by predicting their future productions. After appling the proposed scheme to 3 different channel fields, it provides reliable models for production forecasts with reduced uncertainty. The analyses show that the scheme can effectively characterize channel features and increase a probability of existence of models similar to a true model.

Tomotok: python package for tomography of tokamak plasma radiation

A python package, called Tomotok, focused on performing tomographic inversion of tokamak plasma radiation is being developed at the Institute of Plasma Physics of the Czech Academy of Sciences. It aims at providing multiple inversion algorithms with an user friendly interface. In order to enable and ease performing tomographic inversion on different devices worldwide, it is planned to publish this software as open source in the near future. In this contribution, the package structure allowing an easy implementation of various tokamak and diagnostic geometries is described and an overview of the package contents is given. Apart from inversion methods, overview of Tomotok auxiliary content is given. The package provides tools for creating simple synthetic diagnostic system. These can be used for testing and benchmarking the code. This includes tools for building geometry matrices that describe the view of detectors using single line of sight approximation and artificial data generators capable of creating simple or hollow Gaussian profiles. The implemented inversion methods cover the minimum Fisher regularisation, biorthogonal decomposition and linear algebraic methods. The implementation of each method is explained, example results obtained by inverting phantom models are presented and discussed. The computation speed of implemented algorithms is benchmarked and compared.

Quantifying CO emission rates of industrial point sources from TROPOMI observations

This work demonstrates for the first time the capability of Tropospheric Monitoring Instrument (TROPOMI) routine operations to quantify CO emission rates down to industrial point sources. We have quantified CO emission rates of four industrial point sources in Asia (i.e., Qianlishan industrial park (39.9°N, 106.9°E), Jiuyuan industrial park (40.7°N, 109.7°E) and Botian industrial park (42.2°N, 125.2°E) in China, and Jindal Factory (15.2°N, 76.7°E) in India) with TROPOMI CO observations from 2017 to 2020. The Qianlishan industrial park is a missing source in emission inventory and we quantify it to be ~14.0 kg/s. Our estimates for other three sources vary over 14.4 to 34.3 kg/s, which are within 37–69% of the inventory values. The plume inversion methods are presented in a manner that can be easily used to other fine-scale emission plumes observed from space. Though only a small number of CO plumes per year for any given industrial point source can be observed in conditions suitable for emission rates estimation, there are many industrial point sources can be captured by a good TROPOMI footprint. This work affirms that a constellation of future CO satellites could monitor individual CO point source emissions to support environment policy.

Regularized inversion of aerosol hygroscopic growth factor probability density function: Application to humidity-controlled fast integrated mobility spectrometer measurements

Aerosol hygroscopic growth plays an important role in atmospheric particle chemistry and the effects of aerosol on radiation and hence climate. The hygroscopic growth is often characterized by a growth factor probability density function (GF-PDF), where the growth factor is defined as the ratio of the particle size at a specified relative humidity to its dry size. Parametric, least-square methods are the most widely used algorithms for inverting the GF-PDF from measurements of humidified tandem differential mobility analyzers (HTDMA) and have been recently applied to the GF-PDF inversion from measurements of the humidity-controlled fast integrated mobility spectrometer (HFIMS). However, these least square methods suffer from noise amplification due to the lack of regularization in solving the ill-posed problem, resulting in significant fluctuations in the retrieved GF-PDF and even occasional failures of convergence. In this study, we introduce nonparametric, regularized methods to invert aerosol GF-PDF and apply them to HFIMS measurements. Based on the HFIMS kernel function, the forward convolution is transformed into a matrix-based form, which facilitates the application of the nonparametric inversion methods with regularizations, including Tikhonov regularization and Twomey’s iterative regularization. Inversions of the GF-PDF using the nonparameteric methods with regularization are demonstrated using HFIMS measurements simulated from representative GF-PDFs of ambient aerosols. The characteristics of reconstructed GF-PDFs resulting from different inversion methods, including previously developed least-square methods, are quantitively compared. The result shows that Twomey’s method generally outperforms other inversion methods. The capabilities of the Twomey’s method in reconstructing the pre-defined GF-PDFs and recovering the mode parameters are validated.