Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: a computational investigation

Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers, which limits measurement accuracy. Here, the performance of a different method for measuring RIXS, i.e. photoelectron spectrometry for analysis of X-rays (PAX), is computationally investigated. This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of high-throughput, compact electron spectrometers. X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. A deconvolution algorithm for analysis of such PAX data is proposed. It is shown that the deconvolution algorithm works well on data recorded with ∼0.5 eV resolution. Additional simulations show the potential of PAX for estimation of RIXS features with smaller widths. For simulations using the 3d levels of Ag as a converter material, and with 105 simulated detected electrons, it is estimated that features with a few hundred meV width can be accurately estimated in a model RIXS spectrum. For simulations using a sharp Fermi edge to encode RIXS spectra, it is estimated that one can accurately distinguish 100 meV FWHM peaks separated by 45 meV with 105 simulated detected electrons that were photoemitted from within 0.4 eV of the Fermi level.

Leak location procedure based on the complex-valued FastICA blind deconvolution algorithm for water-filled branch pipe

Water pipe networks have a large number of branch joints. Branch joint shunting generates vortices in the fluid, which excite the pipe wall to produce a type of branch noise. The branch noise is coupled with the leak source signal through the pipe. Here, a novel leak location protocol based on the complex-valued FastICA method (C-FastICA) is proposed to address the leak location problem under the branch noise interference. The C-FastICA, a complex-value domain blind deconvolution algorithm, effectively extended the cost function, constraint function, and iteration rules of the instantaneous linear FastICA into the complex-valued domain. The C-FastICA method was used to realize the separation of branch noise and leak source signal. The experimental results showed that the separation efficiency of the C-FastICA was higher than that of time-domain blind convolution separation (T-BCS). Furthermore, the relative location error of the C-FastICA method to the leak point was less than 14.238%, which was significantly lower than in traditional T-BCS and direct cross-correlation (DCC) technology.

Point-spread Function Deconvolution of the IFU Data and Restoration of Galaxy Stellar Kinematics

We present a performance test of the point-spread function (PSF) deconvolution algorithm applied to astronomical integral field unit (IFU) spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy–Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with a diverse combination of surface brightness profile, signal-to-noise ratio, line-of-sight geometry, and line-of-sight velocity distribution (LOSVD). In addition, we show that the proxy of the spin parameter λ R e can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry, and λ R e measured from the deconvolved MaNGA IFU data exhibit noticeable differences compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.

Restoring Erroneous or Missing Rates in Interfering Wells Using Multiwell Deconvolution

Objectives/Scope Single well deconvolution (von Schroeter et al., 2001) has been added to the well test interpretation toolbox nearly twenty years ago. In recent years, the single well deconvolution algorithm has been extended to multiple interfering wells (Cumming et al., 2013), and further improved with the additions of constraints to account for existing a-priory knowledge on the reservoir (constrained multiwell deconvolution, Cumming et al., 2019). The main objective of multiwell deconvolution is to identify the signatures of all wells involved and the interference signals between wells, from which information can be extracted about the reservoir that may not be obtainable otherwise, e.g. heterogeneities, boundaries and compartmentalization. The single well deconvolution algorithm has also been shown to be capable of restoring erroneous or missing rates (Gringarten, 2010). As shown in this paper, the same is true with multiwell deconvolution, which is able to restore erroneous or missing rates in all the wells involved. Methods, Procedures, Process Starting with arbitrary initial guesses for the missing rates in the various wells involved, we use multiwell deconvolution to estimate these missing flow rates or correct for erroneous ones. Two methods are presented: (1) we use unconstrained multiwell deconvolution as a first step to estimate the missing/erroneous rates, then use constrained multiwell deconvolution with these rates to estimate deconvolved derivatives; and (2) we restore/correct the flow rates and derive deconvolved derivatives simultaneously using constrained multiwell deconvolution. We show that the first approach is more accurate than the second one. In both approaches, we only obtain rates that are proportional to the true flow rates. To obtain the true flow rates, we need to know either one of the actual flow rates in each well, or the corresponding permeabilities. Results, Observations, Conclusions We prove the ability of multiwell deconvolution to estimate rates on synthetic oil reservoirs and gas reservoirs with moderate average reservoir pressure depletion, that include non-interfering wells. We then apply to oil and gas field examples and compare restored vs. actually measured rates. In all cases, the agreement is very good. Novel/Additive Information Using only measured pressure data, constrained multiwell deconvolution can be used to restore unknown flow rates and/or correct for erroneous rates, in addition to estimating deconvolved derivatives of all wells. This is particularly useful in the case of allocated rates or when rates are missing in some of the interfering wells.

Waterflood Analysis in Compartmentalised Reservoir Using Multiwell Retrospective Testing and Tracers

The study field consists of four oil pays and is currently going through a waterflood trial. Due to a presence of high amplitude faulting it becomes crucially important to understand the geology of the field and reservoir connectivity prior to progressing the waterflood project. The results of the cross-well tracers have indication (some strong and some vague) of communication between a trial water injector and all oil producers in the same and adjacent compartment. Since the wells were equipped with permanent downhole pressure gauges it was possible to decipher the cross-well communication using the Multiwell Retrospective Testing (MRT) technique based on multiwell deconvolution algorithm (MDCV). The results of MRT study were showing no traceable communication between trial water injector and offset wells in adjacent compartment except one producer which showed a strong response across the fault. By correlating the MRT results with seismic profile and well completion it became possible to establish how exactly the main pay is communicating between the compartments. It also carried few learning points on how to interpret results of cross-well tracers and MRT in terms of reservoir properties.