Optimist, Pessimist or Engineer - Conductivity Based on Need Not Fashion

An optimist says the glass is half-full, a pessimist half-empty, whereas a good engineer says that the glass is twice as big as it needs to be. There has been much debate over the years about the relative functionality, application and even necessity of proppant in delivering effective hydraulic fractures. Often these debates have been directly linked to major changes in core frac applications, more recently in the dominant North American onshore unconventional market. However, the debates have all too often used broad or unclear brush strokes to describe shifting fracture requirements. Meanwhile, the developing oilfield in the rest of the world resides in more permeable areas of the resource triangle, great care must be taken to ensure that conventional lessons hard learned are not lost, but also that unconventional understanding develops. Over recent years there have been many debates and publications on the relative value of the use of proppant (and associated conductivity), although the true question was about appropriate fracture design in different rock/matrix qualities and environments. Certainly, the vast majority of fracturing engineers appreciate the difference between continuous proppant-pack conductivity and other techniques, such as infinite conductivity, pillar fracturing or duning designs. However, there is increasing evidence that conventional fracturing is suffering from populist attitudes, leading to ineffective fracturing. Additionally, and just as impactful, that unconventional fracturing continues to rely on the lessons learned and physics derived directly from our conventional experience but applying this in an entirely different environment. Primarily, the main concern is with the transfer of recent lessons learned and techniques utilised in one rock quality and environment, to an entirely different scenario, resulting in the misapplication, reduced IP30, poorer NPV or reduced long term EUR and IRR. Examples will be referenced where appropriate proppant selection and frac design can be the difference between success and failure. Fundamentally, we have not sufficiently developed our understanding of the role of proppant and conductivity, for application in unconventionals and thereby rely far too much on our previous conventional thinking. While at the same time we are exporting often inappropriate unconventional populist practice into very conventional environments, thereby potentially achieving the abhorrence of the worst of both worlds. This paper will describe and address scenarios where appropriate engineering selection, rather than popularity-based decision making, has resulted in a successful outcome. It will also attempt to ensure that we show the importance of studying your rock, in anticipation of engineering design, and that this should be a key consideration. The paper will also suggest that as an industry we urgently need to address our approach to consideration of conductivity, placement and importance and ensure that unconventional knowledge and learning progresses with a beneficial outcome for all.

Differences in evolutionary accessibility determine which equally effective regulatory motif evolves to generate pulses

Transcriptional regulatory networks (TRNs) are enriched for certain “motifs”. Motif usage is commonly interpreted in adaptationist terms, i.e. that the optimal motif evolves. But certain motifs can also evolve more easily than others. Here, we computationally evolved TRNs to produce a pulse of an effector protein. Two well-known motifs, type 1 incoherent feed-forward loops (I1FFLs) and negative feedback loops (NFBLs), evolved as the primary solutions. The relative rates at which these two motifs evolve depend on selection conditions, but under all conditions, either motif achieves similar performance. I1FFLs generally evolve more often than NFBLs. Selection for a tall pulse favors NFBLs, while selection for a fast response favors I1FFLs. I1FFLs are more evolutionarily accessible early on, before the effector protein evolves high expression; when NFBLs subsequently evolve, they tend to do so from a conjugated I1FFL-NFBL genotype. In the empirical S. cerevisiae TRN, output genes of NFBLs had higher expression levels than those of I1FFLs. These results suggest that evolutionary accessibility, and not relative functionality, shapes which motifs evolve in TRNs, and does so as a function of the expression levels of particular genes.

Non-adaptive factors determine which equally effective regulatory motif evolves to generate pulses

Transcriptional regulatory networks (TRNs) are enriched for certain subnetworks or “motifs”. Motif usage is commonly interpreted as the result of adaptive evolution. But network motifs can also differ in how easy it is to evolve them. Here, we simulated the de novo evolution of motifs within TRNs under selection to produce a short, sharp pulse of an effector protein. In agreement with past work in the field, two network motifs, type 1 incoherent feed-forward loops (I1FFLs) and negative feedback loops (NFBLs), evolved as the primary solutions. Different selection conditions changed the relative frequencies of the two solutions, but this was not due to the superior performance of one; under all conditions, either motif can achieve similar top performance. I1FFLs generally evolve more often than NFBLs, unless we selected for a particularly tall pulse. This result suggests that I1FFLs are evolutionary more accessible than NFBLs. When NFBLs do evolve, it is usually from a conjugate containing both I1FFL and NFBL. In contrast, I1FFLs can evolve via a greater variety of trajectories. This difference potentially explains NFBL’s lower evolutionary accessibility. To agreement with our simulation results, we found that in the real yeast TRN, output genes of NFBLs had higher expression levels than those of I1FFLs, i.e. selection for taller pulses. These results suggest that evolutionary accessibility, and not relative functionality, shape which networks motifs evolve in TRNs, and do so as a function of the expression levels of particular genes.

The Relative Functionality of Freshly Isolated and Cryopreserved Human Adipose-Derived Stromal/Stem Cells

The capability of multipotent mesenchymal stem cells to maintain cell viability, phenotype and differentiation ability upon thawing is critical if they are to be banked and used for future therapeutic purposes. In the present study, we examined the effect of 9-10 months of cryostorage on the morphology, immunophenotype, colony-forming unit (CFU) and differentiation capacity of fresh and cryopreserved human adipose-derived stromal/stem cells (ASCs) from the same donors. Cryopreservation did not reduce the CFU frequency and the expression levels of CD29, CD73, CD90 and CD105 remained unchanged with the exception of CD34 and CD45; however, the differentiation capacity of cryopreserved ASCs relative to fresh cells was significantly reduced. While our findings suggest that future studies are warranted to improve cryopreservation methods and agents, cryopreserved ASCs retain sufficient features to ensure their practical utility for both research and clinical applications.

The relative efficiency of modular and non-modular networks of different size

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.