Rock typing of diagenetically induced heterogeneities – A case study from a deeply-buried clastic Rotliegend reservoir of the Northern German Basin

Reservoir quality of sandstones is mainly derived from their permeability and porosity. As a result, porosity-reducing processes need to be understood in order to evaluate and model reservoir quality in sandstones. This case study from a Rotliegend gas reservoir in the Northern German Basin utilizes petrophysical measurements in conjunction with petrography in order to assess reservoir qualities and define rock types. The most significant diagenetic factors influencing the development of the IGV (intergranular volume) are quartz cementation due to low illite grain coating coverages on grain to IGV interfaces and chemical compaction due to pronounced illite grain coating coverages on grain to grain interfaces. Where large proportions of the interface between adjacent grains are coated by illite, stronger chemical compaction (pressure dissolution) was observed to occur. This chemical compaction reduces the IGV, and thus open pore space. Permeabilities measured under decreasing confining pressures from 50 to 2 MPa were used to determine the pressure sensitivities of permeability (David et al., 1994), which ranged from 0.005 to 0.22 MPa−1. The pressure sensitivity of permeability, porosity and permeability were linked to the petrographic texture, implying three different major rock types: Type A is characterized by an uncemented petrographic texture with high porosities (avg.: 9.8%), high permeabilities (avg.: 126 mD), and low pressure sensitivities of permeability (avg.: 0.019 MPa−1). Type B is intensely cemented with reduced porosities (avg.: 4.0%), reduced permeabilities (avg.: 0.59 mD), and increased pressure sensitivities of permeability (avg.: 0.073 MPa−1). Type C is characterized by intense chemical compaction leading to the lowest porosities (avg.: 1.8%) and permeabilities (avg.: 0.037 mD) in concert with the highest pressure sensitivity of permeability (avg.: 0.12 MPa−1). The heterogeneity induced by diagenesis will have an impact on recoverable resources and flow rates in both hydrocarbon and geothermal projects in similar siliciclastic reservoirs

Coprophagous features in carnivorous Nepenthes plants: a task for ureases

Most terrestrial carnivorous plants are specialized on insect prey digestion to obtain additional nutrients. Few species of the genus Nepenthes developed mutualistic relationships with mammals for nitrogen supplementation. Whether dietary changes require certain enzymatic composition to utilize new sources of nutrients has rarely been tested. Here, we investigated the role of urease for Nepenthes hemsleyana that gains nitrogen from the bat Kerivoula hardwickii while it roosts inside the pitchers. We hypothesized that N. hemsleyana is able to use urea from the bats’ excrements. In fact, we demonstrate that 15N-enriched urea provided to Nepenthes pitchers is metabolized and its nitrogen is distributed within the plant. As ureases are necessary to degrade urea, these hydrolytic enzymes should be involved. We proved the presence and enzymatic activity of a urease for Nepenthes plant tissues. The corresponding urease cDNA from N. hemsleyana was isolated and functionally expressed. A comprehensive phylogenetic analysis for eukaryotic ureases, including Nepenthes and five other carnivorous plants’ taxa, identified them as canonical ureases and reflects the plant phylogeny. Hence, this study reveals ureases as an emblematic example for an efficient, low-cost but high adaptive plasticity in plants while developing a further specialized lifestyle from carnivory to coprophagy

Universal neural field computation

Turing machines and G\"odel numbers are important pillars of the theory of computation. Thus, any computational architecture needs to show how it could relate to Turing machines and how stable implementations of Turing computation are possible. In this chapter, we implement universal Turing computation in a neural field environment. To this end, we employ the canonical symbologram representation of a Turing machine obtained from a G\"odel encoding of its symbolic repertoire and generalized shifts. The resulting nonlinear dynamical automaton (NDA) is a piecewise affine-linear map acting on the unit square that is partitioned into rectangular domains. Instead of looking at point dynamics in phase space, we then consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. We call the resulting representation \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with arXiv:1204.546

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

The Problem of Signal and Symbol Integration: A Study of Cooperative Mobile Autonomous Agent Behaviors

This paper explores and reasons about the interplay between symbolic and continuous representations. We first provide some historical perspective on signal and symbol integration as viewed by the Artificial Intelligence (AI), Robotics and Computer Vision communities. The domain of autonomous robotic agents residing in dynamically changing environments anchors well different aspects of this integration and allows us to look at the problem in its entirety. Models of reasoning, sensing and control actions of such agents determine three different dimensions for discretization of the agent-world behavioral state space. The design and modeling of robotic agents, where these three aspects have to be closely tied together, provide a good experimental platform for addressing the signal-to-symbol transformation problem. We present some experimental results from the domain of cooperating mobile agents involved in tasks of navigation and manipulation