Total organic carbon in the Bowland-Hodder Unit of the southern Widmerpool Gulf: a discussion

This review of the article by Kenomore et al. (2017) on the total organic carbon (TOC) evaluation of the Bowland Shale Formation in the Widmerpool Gulf sub-basin (southern Pennine Basin, UK) reveals a number of deficiencies, rooted mostly in an inadequate appreciation of the local Carboniferous stratigraphy. Kenomore et al. use the ΔLog R, the ‘Passey’ method after Passey et al. (1990), to evaluate the TOC content in two boreholes in the Widmerpool Gulf: Rempstone 1 and Old Dalby 1. We show here that Kenomore and co-authors used maturity data, published by Andrews (2013), from different formations to calibrate their TOC models of the Bowland Shale Formation (Late Mississippian–Early Pennsylvanian); the Morridge Formation in Rempstone 1 and the Widmerpool Formation in Old Dalby 1. We contest that this gives viable TOC estimates for the Bowland Shale Formation and that because of the location of the boreholes these TOC models are not representative over the whole of the Widmerpool Gulf. The pyrite content of the mudstones in the Widmerpool Gulf also surpasses the threshold where it becomes an influence on geophysical well logs. Aside from these stratigraphic and lithologic issues, some methodological weaknesses were not adequately resolved by Kenomore and co-authors. No lithological information is available for the Rock-Eval samples used for the maturity calibration, which because of the interbedded nature of the source formations has implications for the modelling exercise. We recommend that more geochemical data from a larger array of boreholes covering a wider area, proximal and distal, of the basin are collected before any inferences on TOC are made. This is necessary in the complex Bowland Shale system where lithological changes occur on a centimetre scale and correlations between the different sub basins are not well understood

Shallow Gas Offshore Netherlands - the role of faulting and implications for CO2 storage

The presence of shallow gas within Pliocene–Pleistocene sediments in the North Sea is well known,though there is still some debate regarding its origins. Many of the shallow gas accumulations are coincident with faults developed over salt structures, leading to speculation that faults may have acted as conduits for upward migration of hydrocarbons from greater depths. The role of faults in charging of the Pliocene–Pleistocene reservoirs is investigated for several of the gas accumulations through interpretation of 3D seismic reflection data, revealing the relationship between faults and seismic indications of gas saturated sediments such as bright spots and gas chimneys. In order to invoke the faults as migration conduits for the gas, they must form part of the migration pathway between the gas-charged sediments and thermogenic source rocks. For the accumulations studied, such migration routes exist with salt-withdrawal beneath mini-basins allowing Carboniferous-sourced gas to migrate to the Triassic, and subsequent vertical migration along faults and fractures associated with diapirism. The faults in question are near critically stressed,and have been active in the recent geological past. The observation of shallow gas seemingly associated with such features may have implications for the sequestration of carbon dioxide in formations affected by similar features

Assessing carbon dioxide storage integrity of an extensive saline aquifer formation: East Irish Sea Basin, UK

Accurately determining the contemporary pore pressure and in situ stress conditions is critical to the safe planning and development of subsurface operations such as CO2 storage. According to the UK storage capacity atlas, CO2STORED (Bentham et al. 2014), the East Irish Sea Basin (EISB) has a significant storage capacity of nearly 4 Gt (P50) within saline aquifer parts of the Triassic-aged Ormskirk Sandstone Formation (OSF). The OSF is present over a significant part of the EISB, and where buried deeply enough to be considered for CO2 storage is overlain by the Mercia Mudstone Group (MMG), a thick sequence comprising up to 3200 m of interbedded mudstones, siltstones and evaporites. As a result of Tertiary inversion, the Jurassic and younger succession is absent over most of the basin, and so the MMG represents the vast majority of the overburden succession. The presence of numerous gas accumulations, including the Morecambe South Gas Field with its ~400 m gas column, is testament to the sealing capacity of the MMG. Where halite formations within the MMG directly overly the OSF, the sealing capacity of the MMG is significantly increased

Using small MUSes to explain how to solve pen and paper puzzles

Pen and paper puzzles like Sudoku, Futoshiki and Skyscrapers are hugely popular. Solving such puzzles can be a trivial task for modern AI systems. However, most AI systems solve problems using a form of backtracking, while people try to avoid backtracking as much as possible. This means that existing AI systems do not output explanations about their reasoning that are meaningful to people. We present Demystify, a tool which allows puzzles to be expressed in a high-level constraint programming language and uses MUSes to allow us to produce descriptions of steps in the puzzle solving. We give several improvements to the existing techniques for solving puzzles with MUSes, which allow us to solve a range of significantly more complex puzzles and give higher quality explanations. We demonstrate the effectiveness and generality of Demystify by comparing its results to documented strategies for solving a range of pen and paper puzzles by hand, showing that our technique can find many of the same explanations.Publisher PD

Engineering the Future: A Workshop for High School Teachers

The framework guiding the development of Next Generation Science Standards (NGSS) identifies eight science and engineering principles essential for all students to learn. The Engineering the Future workshop, offered by South Dakota State University (SDSU) in the summer of 2012, focused on helping teachers better understand those principles and how to employ them effectively in their classrooms. Each day of the week-long workshop, teachers participated in a variety of engineering-related activities, accessed low and high-end instrumentation, took tours of engineering-related facilities in the region, and developed lesson plans to incorporate what they learned into their science classrooms. We used pre- and postworkshop surveys to assess the participants’ understanding and attitudes regarding science and engineering. Results of the survey showed participants had a narrow view of engineering prior to the workshop but by the end of the workshop, they were more aware of the nature of engineering, the various types of engineering, and they better understood how they could incorporate engineering principles into their current curriculum

CONJURE: automatic generation of constraint models from problem specifications

Funding: Engineering and Physical Sciences Research Council (EP/V027182/1, EP/P015638/1), Royal Society (URF/R/180015).When solving a combinatorial problem, the formulation or model of the problem is critical tothe efficiency of the solver. Automating the modelling process has long been of interest because of the expertise and time required to produce an effective model of a given problem. We describe a method to automatically produce constraint models from a problem specification written in the abstract constraint specification language Essence. Our approach is to incrementally refine the specification into a concrete model by applying a chosen refinement rule at each step. Any nontrivial specification may be refined in multiple ways, creating a space of models to choose from. The handling of symmetries is a particularly important aspect of automated modelling. Many combinatorial optimisation problems contain symmetry, which can lead to redundant search. If a partial assignment is shown to be invalid, we are wasting time if we ever consider a symmetric equivalent of it. A particularly important class of symmetries are those introduced by the constraint modelling process: modelling symmetries. We show how modelling symmetries may be broken automatically as they enter a model during refinement, obviating the need for an expensive symmetry detection step following model formulation. Our approach is implemented in a system called Conjure. We compare the models producedby Conjure to constraint models from the literature that are known to be effective. Our empirical results confirm that Conjure can reproduce successfully the kernels of the constraint models of 42 benchmark problems found in the literature.Publisher PDFPeer reviewe

Towards generic explanations for pen and paper puzzles with MUSes

This research was supported by the Royal Society URF\R\180015 .Pen and paper puzzles like Sudoku, Futoshiki and Star Battle are hugely popular. Solving such puzzles can be a trivial task for modern AI systems. However, most AI systems solve problems using a form of backtracking, while people try to avoid backtracking as much as possible. This means that existing AI systems do not output explanations about their reasoning that are meaningful to people. We present Demystify, a tool which allows puzzles to be expressed in a high-level constraint programming language and uses MUSes to allow us to produce descriptions of steps in the puzzle solving. We give several improvements to the existing techniques for solving puzzles with MUSes, which allow us to solve a range of significantly more complex puzzles and give higher quality explanations. We demonstrate the effectiveness and generality of Demystify by comparing its results to documented strategies for solving a range of pen and paper puzzles by hand, showing that our technique can find many of the same explanations.Publisher PD

Using Small MUSes to Explain How to Solve Pen and Paper Puzzles

In this paper, we present Demystify, a general tool for creating human-interpretable step-by-step explanations of how to solve a wide range of pen and paper puzzles from a high-level logical description. Demystify is based on Minimal Unsatisfiable Subsets (MUSes), which allow Demystify to solve puzzles as a series of logical deductions by identifying which parts of the puzzle are required to progress. This paper makes three contributions over previous work. First, we provide a generic input language, based on the Essence constraint language, which allows us to easily use MUSes to solve a much wider range of pen and paper puzzles. Second, we demonstrate that the explanations that Demystify produces match those provided by humans by comparing our results with those provided independently by puzzle experts on a range of puzzles. We compare Demystify to published guides for solving a range of different pen and paper puzzles and show that by using MUSes, Demystify produces solving strategies which closely match human-produced guides to solving those same puzzles (on average 89% of the time). Finally, we introduce a new randomised algorithm to find MUSes for more difficult puzzles. This algorithm is focused on optimised search for individual small MUSes

Complexity of n-Queens completion (extended abstract)

The n-Queens problem is to place n chess queens on an n by n chessboard so that no two queens are on the same row, column or diagonal. The n-Queens Completion problem is a variant, dating to 1850, in which some queens are already placed and the solver is asked to place the rest, if possible. We show that n-Queens Completion is both NP-Complete and #P-Complete. A corollary is that any non-attacking arrangement of queens can be included as a part of a solution to a larger n-Queens problem. We introduce generators of random instances for n-Queens Completion and the closely related Blocked n-Queens and Excluded Diagonals Problem. We describe three solvers for these problems, and empirically analyse the hardness of randomly generated instances. For Blocked n-Queens and the Excluded Diagonals Problem, we show the existence of a phase transition associated with hard instances as has been seen in other NP-Complete problems, but a natural generator for n-Queens Completion did not generate consistently hard instances. The significance of this work is that the n-Queens problem has been very widely used as a benchmark in Artificial Intelligence, but conclusions on it are often disputable because of the simple complexity of the decision problem. Our results give alternative benchmarks which are hard theoretically and empirically, but for which solving techniques designed for n-Queens need minimal or no change