Managing academic personnel flow at universities.

Universities experience increasing difficulty in staffing their academic positions. Attracting and retaining highly qualified employees in a general problem that has received much attention in recent HRM literature. But several authors have claimed that the academic career has lost much of its attractiveness. This paper presents seven levers that universities may use to enhance their recruitment and retention power on a difficult job market. Suggestions are based on experience from innovative organizations, both universities and business organizations. Special attention is given to the creation of multiple and flexible career paths within academia. We contend that a successful application of these suggestions will require major cultural and institutional change at universities.Retention; Market;

SIRT1 and SIRT3 deacetylate homologous substrates: AceCS1,2 and HMGCS1,2.

SIRT1 and SIRT3 are NAD+-dependent protein deacetylases that are evolutionarily conserved across mammals. These proteins are located in the cytoplasm/nucleus and mitochondria, respectively. Previous reports demonstrated that human SIRT1 deacetylates Acetyl-CoA Synthase 1 (AceCS1) in the cytoplasm, whereas SIRT3 deacetylates the homologous Acetyl-CoA Synthase 2 (AceCS2) in the mitochondria. We recently showed that 3-hydroxy-3-methylglutaryl CoA synthase 2 (HMGCS2) is deacetylated by SIRT3 in mitochondria, and we demonstrate here that SIRT1 deacetylates the homologous 3-hydroxy-3-methylglutaryl CoA synthase 1 (HMGCS1) in the cytoplasm. This novel pattern of substrate homology between cytoplasmic SIRT1 and mitochondrial SIRT3 suggests that considering evolutionary relationships between the sirtuins and their substrates may help to identify and understand the functions and interactions of this gene family. In this perspective, we take a first step by characterizing the evolutionary history of the sirtuins and these substrate families

2.5-D Deep Learning Inversion of LWD and Deep-Sensing em Measurements Across Formations with Dipping Faults

Deep learning (DL) inversion of induction logging measurements is used in well geosteering for real-time imaging of the distribution of subsurface electrical conductivity. We develop a DL inversion workflow to solve 2.5-D inverse problems arising in well geosteering. The inversion workflow employs three DL modules: a 'look-around' fault detection module and two inversion modules for reconstructing anisotropic resistivity models in the presence or absence of fault planes, respectively. Our DL approach is capable of detecting and quantifying arbitrary dipping fault planes in real time. We compare inversion performance considering only short logging-while-drilling (LWD) measurements versus using both short LWD and deep-sensing measurements. The latter measurements provide enhanced depth-of-investigation while minimizing uncertainty. We also obtain improved results when using multidimensional inversion, especially nearby fault planes. This study verifies the applicability of real-time 2.5-D DL inversion across arbitrary faulted formations for well geosteering

Fast inversion of logging-while-drilling resistivity measurements acquired in multiple wells

This paper introduces a new method for the fast inversion of borehole resistivity measurements acquired in multiple wells using logging-while-drilling (LWD) instruments. There are two key novel contributions. First, we approximate general three-dimensional (3D) transversely isotropic (TI) formations with a sequence of several \stitched" one-dimensional (1D) planarly layered TI sections. This allows us to approximate the solution of rather complex 3D formations using only 1.5D simulations. Second, the developed method supports the simultaneous inversion of measurements acquired in different neighboring wells and/or with different logging instruments. Numerical experiments performed with realistic 3D synthetic formations confirm the flexibility of the method and the reliability of inversion products. The method yields relative errors below 5% on the model space, and it enables the interpretation of resistivity measurements acquired in multiple wells (e.g., an exploratory, an offset, and a geosteering well) and with any combination of co-axial and/or tri-axial commercial logging measurements acquired with known antennae configurations. Numerical results also indicate that thinly-bedded resistive formations are very sensitive to the presence of noise on the measurements and/or to possible errors on bed boundary locations, while conductive layers are only weakly sensitive to those effects

Sensitivity study of borehole-to-surface and crosswell electromagnetic measurements acquired with energized steel casing to water displacement in hydrocarbon-bearing layers

We study the theoretical response of electromagnetically energized steel casing in the presence of subsurface variations of electrical resistivity. Casing is energized with a finite-size solenoid antenna located along the axis of the borehole. Measurements consist of the azimuthal component of the electric field acquired either on the surface or in a separate well in the same hydrocarbon field. We assume two-dimensional (2D) axisymmetric variations of subsurface electrical resistivity and casing excitation. Simulations of electromagnetic (EM) fields excited by energized steel casing are performed with a goal-oriented hp-adaptive finite-element method that automatically generates a sequence of optimal grids delivering exponential convergence rates in terms of the EM fields at the receiver antennas against CPU time. This advanced finite-element method enables accu-rate modeling of problems with high conductivity contrasts inlarge computational domains. Numerical simulations quantify the measurement sensitivity to variations of frequency, distance from casing to receivers, resistivity of the target oil-bearing layer, and pistonlike radial invasion of water within a target layer initially saturated with oil. When receivers are placed in a nearby well, numerical results indicate that measurements exhibit the largest sensitivity to the target (oil-saturated) layer when the transmitter or receiver antenna is located just above the target layer, and another antenna is located below the target layer. A frequency range from 5-30 Hz provides optimal results for the detection of oil-bearing layers and estimation of radial extent of water invasion. Large horizontal distances (up to 1500 m) between transmitter and receivers and a background material with resistivity above 50 Ωm also enhance the measurement sensitivity to radial variations of water invasion. This sensitivity can be as large as 15%-20% of the measured electric field

Simulation of DC dual-laterolog measurements in complex formations: A Fourier-series approach with nonorthogonal coordinates and self-adapting finite elements

Dual laterolog (DLL) makes use of a galvanic conduction principle to focus electrical currents into rock formations, thereby minimizing shoulder and borehole effects in the measurement of formation resistivity. The tool includes two separate focusing systems: deep-sensing (LLd) and shallow-sensing modes (LLs). Laterolog current-focusing systems were designed for operation primarily in vertical boreholes penetrating horizontal layers; only recently their design has been revised for operation in deviated wells in the presence of electrical anisotropy. We simulated three-dimensional (3D) DLL measurements in dipping, invaded, and electrically anisotropic formations and appraised the corresponding effects on apparent resistivity logs. Simulations were performed by combining the use of a Fourier-series expansion in a nonorthogonal system of coordinates with an existing 2D goal-oriented, higher-order, and self-adaptive finite-element method. This numerical algorithm yields accurate solutions in limited CPU time because only a few Fourier modes are needed to simulate practical applications. For the calculation of focused currents, we introduced an embedded postprocessing method that incorporates a synthetic focusing principle to compute current intensities at each iterative step of optimal mesh refinements. Our numerical method accurately simulates 3D DLL measurements in rock formations that exhibit extreme contrasts of electrical resistivity. Simulations indicate that LLs resistivity logs are more sensitive to both invaded and anisotropic layers than LLd resistivity logs. In deviated wells, shoulder-bed effects on apparent resistivity logs increase with an increase of dip angle, and are emphasized across thin conductive layers. Electrical anisotropy effects on apparent resistivity logs increase substantially with dip angle. © 2009 Society of Exploration Geophysicists. All rights reserved

Performance of a multi-frontal parallel direct solver for hp-finite element method

In this paper we present the performance of our parallel multi-frontal direct solver when applied to solve linear systems of equations resulting from discretizations of a hp Finite Element Method (hp-FEM). The hp-FEM generates a sequence of computational meshes delivering exponential convergence of the numerical error with respect to the mesh size (number of degrees of freedom). A sequence of meshes is obtained by performing several hp refinements starting from an arbitrary initial mesh. The solver constructs initial elimination tree for an arbitrary initial mesh, and expands the elimination tree each time the mesh is refined. The solver has been tested on 3D Direct Current (DC) borehole resistivity measurement simulations problems. We compare the solver with two versions of the MUMPS parallel solver: with (1) distributed entries executed over the entire problem, and (2) the direct sub-structuring method with parallel MUMPS solver utilized to solve the interface problem. We show that by providing to the solver the knowledge about the structure of the hp-FEM, the order of elimination is obtained straightforward, and leads to a better performance than by submitting the entire matrix to the solver and executing a connectivity graph based ordering algorithm

Fast 2.5D Finite Element Simulations of Borehole Resistivity Measurements

We develop a rapid 2.5-dimensional (2.5D) finite element method for simulation of borehole resistivity measurements in transversely isotropic (TI) media. The method combines arbitrary high-order $H^1$ - and $H$ (curl)-conforming spatial discretizations. It solves problems where material properties remain constant along one spatial direction, over which we consider a Fourier series expansion and each Fourier mode is solved independently. We propose a novel a priori method to construct quasi-optimal discretizations in physical and Fourier space. This construction is based on examining the analytical (fundamental) solution of the 2.5D formulation over multiple homogeneous spaces and assuming that some of its properties still hold for the 2.5D problem over a spatially heterogeneous formation. In addition, a simple parallelization scheme over multiple measurement positions provides efficient scalability. Our method yields accurate borehole logging simulations for realistic synthetic examples, delivering simulations of borehole resistivity measurements at a rate faster than 0.05 s per measurement location along the well trajectory on a 96-core computer

Scale-up and turbulence modelling in pipes

Large diameter pipes are commonly used for oil and gas transportation. Experimental and numerical results, including turbulence properties, are often obtained for small diameter pipes. Only little information is available for pipes larger or equal to 200 mm. Results obtained with Reynolds Averaged Navier-Stokes (RANS) turbulence models for single phase flow in pipes of different sizes are presented and discussed. The use of non-dimensional data is usually assumed sufficient to present general information and is assumed valid for any size of pipe. The validity of such assumptions has been checked and the flow behaviour in small, medium and large pipes obtained with several of the most common RANS turbulence models, has been established under specific conditions via Computational Fluid Dynamics (CFD) techniques. Although difficulties were sometimes encountered to reproduce correctly the turbulence properties described in the literature with the turbulence models implemented in open source CFD codes, it is shown that a scaling-up approach is valid as the general flow pattern can be predicted by a non-dimensional strategy