dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-characterisation of the storage site used publicly available existing data acquired during hydrocarbon exploration and production. A summary of the technical aspects of characterisation of the prospective site and implications to risk reduction are presented. Full and detailed technical characterisation is reported and available from the project web site (www. sitechar-co2. eu). The outcomes of previous assessments of the prospective storage site are reviewed in Section 1. The prospective storage site is described in Section 2, including selection of the hydrocarbon field component, the site geology and the storage scenario. Section 3 presents risk assessment-led characterisation and the required outputs, the risk assessment process in SiteChar and a summary of risk reduction investigations for selected risks.
It meets acceptable geological characteristics as indicated by  Chadwick et al. (2008)  and has sufficient potential storage capacity. The Captain Sandstone has an estimated static CO 2 storage capacity ranging from 36 Mt (applying a storage efficiency of 0. 2%) to 363 Mt (applying a storage efficiency of 2%)  (SCCS, 2009). Characterisation and calculation of storage capacity by dynamic modeling of CO 2 injection endorses a potential capacity of more than 360 Mt for the Captain Sandstone saline aquifer with a storage efficiency of more than 0. 6% by Scottish Carbon Capture and Storage  (SCCS, 2011). The Captain Sandstone was selected for research assessment as an example of a commercial-scale CO 2 storage asset for Scotland following an examination of three sandstones. Each was appraised on its proximity to existing onshore CO 2 sources, estimated storage capacity, access to existing oil and gas pipelines, whether known from oil and gas exploration data and having sufficient data accessible to a research project  (SCCS, 2011).
Selected 2D seismic survey and well data were interpreted to map and characterise the site and a basin-scale model was constructed and attributed with petrophysical characteristics  (SCCS, 2011). Dynamic simulation of CO 2 calculated the potential storage capacity to be at least as much as the previously estimated upper value and possibly much greater  (Jin et al. , 2012). Further assessment of the Captain Sandstone as a potential CO 2 store was considered as justified  (SCCS, 2011). The basin-scale model of Scottish Carbon Capture and Storage  (SCCS, 2011)  was also used for the SiteChar UK northern North Sea storage site (Fig. 1 ). The SiteChar research benefited from publicly available assessments of the Goldeneye Gas Field for CO 2 storage  (Shell, 2011a, b). The Goldeneye Field is also within the Captain Sandstone and lies 30 kilometres to the east of the SiteChar UK site study area.
Risks Addressed The technical investigations in SiteChar were targeted to inform reduction of risks to the subsurface containment of stored CO 2 , where site technical performance might be less than expected, associated with monitoring and regulatory issues or have a potentially adverse effect on other resources (Tab. 2). Investigation of risks to the prospective storage site is, of necessity, confined to those risks associated with technical matters as appropriate for a research project. Risks identified by SiteChar experts associated with economic, financial and environmental issues were not addressed. Technical investigations were directed to reduce risks and provide information needed for a storage permit application. During the course of the investigations additional data were acquired or reviewed.
Risk: Primary Cap Rock Thin or Absent The top of the Captain Sandstone is generally sealed by overlying low permeability mudstone but in some places the mudstone is thin and it may be directly overlain by the unconformable Base Chalk surface. Some wells, e. g. Well 13/22a-28 in the Captain Oil Field, prove Chalk resting directly on the Captain Sandstone. Chalk cap rock may not be as efficient a seal as the mudstone. The 3D seismic survey data interpreted for the SiteChar detailed study area does not resolve where the mudstone cap rock is absent but indicates where it is most likely to be thin (less than 10 metres). Although geological mapping shows the Captain Sandstone is expected to be overlain by Lower Cretaceous mudstone across the extent of the storage complex, an area where the cap rock is interpreted to be thin (<10 m) lies in the north-west part of the Blake Field within the Flank facies area  (Quinn et al.
(2013). Fracture data for the cap rock and overburden from well log and core data sources from within and in the vicinity of the storage site were used to inform estimation of a percolating fracture network. The computed percolation probability is strongly influenced by cap rock thickness, proportion of open (conducting) fractures and the number of fractures over a specified vertical distance (line density). For the two scenarios with the lowest proportion of open fractures (0. 1) and the thickest cap rock (227 m), the percolation probability is negligible. Note here that natural gas and oil has been contained in the Blake Field and other hydrocarbon fields over the geological time scale which is evidence that should preclude the existence of a natural gas leakage pathway in the cap rock. It thus can be argued that it is unlikely that a percolating open fracture network exists in the cap rock.
The probability, and so the risk, that 'standard' seismic monitoring methods would be ineffective were reduced by the site characterisation investigations (Tab. 3). However, an optimal alternative of seismic data acquisition from a permanent ocean bottom cables system is recommended to achieve higher signal-to-noise ratios, ensure repeatability and acquisition of additional seismic property data  (Hannis et al. , 2013c). A cost-benefit analysis would be required to indicate if the increased certainty of monitoring is justified by the additional cost. The EC requires that CO 2 storage operations should not present a risk to groundwater, in the offshore setting of the SiteChar UK North Sea site this is not a concern. However, the UK Government requires that CO 2 storage operations should not interfere with hydrocarbon field operations.
Risk reduction and mitigation measures are proposed to further reduce risks, decrease uncertainties, and increase certainty in the prospective storage site. Abandoned wells remain the most likely fluid escape pathways. However, the mass of CO 2 migrating within the storage complex via a leaking wellbore is modeled as a maximum of 20 tonnes per 100 million tonnes injected, despite assuming very pessimistic conditions. The impact of CO 2 reacting with the reservoir rock during the period of injection is predicted to be negligible by geochemical modeling with a modest net increase in porosity over a period of one million years. The potential for salt precipitation and the effects of microbial activity should also be assessed. Rock physics studies and seismic modeling indicate the injected CO 2 should be detectable using time-lapse surface 3D seismic survey data acquired using standard methods.
ACKNOWLEDGMENTS The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement No. 256705  as well as from  Enel ,  Statoil ,  Vattenfall ,  Véolia Environnement , and the  Scottish Government. The support and contribution of the  Scottish Government  in the conduct of the SiteChar research and funding of the underpinning, preceding Scottish CCS studies by SCCS is gratefully acknowledged. Two anonymous reviewers are thanked for their constructive reviews which greatly improved an earlier version of this paper.
Introduction More and more spatial data representing the same "real world" co-exists. Entities are differently represented (e. g. , a river may be represented by its axis or its full extent) and are intended to be used in different applications (e. g. , topographic maps, route planning, risk analysis). Representations are produced using different rules by public or private organizations, or by crowdsourcing, such as with OpenStreetMap  [35]. Thus datasets are independently produced and managed. This presents difficulties for data producers as well as data users when processes such as integration, data quality estimation, or updates need to be performed. In this context a solution is to define links between features from different datasets representing the same object in the real world. This process is known in the literature as data matching  [50]  and is illustrated in Figure  1.
Data matching: A multi-criteria issue Data matching relies on the concept of similarity, e. g. , two features A and B belonging to two different datasets are matched if they are similar. Generally, similarity measures for data matching are based on four types of information: geometric (the position and implicitly the shape); semantic (i. e. , features types, such as road, summit, and building); attributes (i. e. , the different characteristics of features, such as number of lanes, name, number of levels, etc. ); and topological relations between features (e. g. , meet, inside). Matching processes for spatial data rely primarily on the comparison of geometric positions and relations between features. For isolated points, geometry matching can be based simply on distance. For more complex data like spatial networks, many geometric criteria can be defined, such as the comparison of orientations, lengths, levels of sinuosity.
Topological relationships between features of the same dataset  [31, 50] , or between features belonging to two different datasets  [42]  can also be used. These relationships are used to compare nodes (e. g. , number of incidents arcs, angles between incident arcs)  [7, 49]  or edges (e. g. , orientation, length, sinuosity)  [29, 51] , or to guide the matching sequence. For example when nodes are first matched, subsequent arcs connected to those nodes are matched  [31]. For data represented by polygons, criteria based on surface distance and shape comparisons are used  [3, 5]. The comparison of the semantic information is usually based on either string comparison, semantic distances between concepts defined by ontologies  [1, 38] , or lattice approaches  [16]. A detailed review on semantic measures is provided by Ballatore et al.
[4]. In the literature, the approaches typically focus on only one type of geometry at a time. As a consequence, there exist specific approaches to match points  [5] , networks  [31, [49] [50] [51]  or polygons  [6]. In general, for point and polygon features matching can be applied to data sets having the same or different scales. In contrast, approaches for matching networks depend on whether the datasets to be matched are at comparable scales. For example comparing the number of incoming and outgoing arcs of two nodes is relevant only when the data sets have the same scale. At different scales, the cardinality of links in the network changes, making matching networks at different scales meaningless.
Data matching: The global steps to make a decision The matching process consists of five steps: pre-processing, candidate selection, criteria combination and decision, and finally evaluation. Some pre-processing is usually performed on datasets, such as rubber-sheeting to reduce geometric deviation  [7, 49] ; splitting edges using matched nodes to reduce cardinalities to 1:1  [7, 49] ; removing nodes having only two arcs connected  [46] ; or the simplification of the most detailed dataset, if data scales are different  [51]. Candidate selection is a first and usually rough analysis to restrict the number of potentially identical features, and thus to reduce computation time. Generally for point and linear features, the selection of candidates is based on geometry using a maximum distance  [6, 14, 31, 49]  or a buffer  [29, 46, 50, 51].
Discussion: Towards a more generic data matching approach It is obvious that the geometry, scale, and heterogeneity of data have an important influence on the matching strategy, making the approaches more or less complex. We observe that specific data matching approaches are generally defined according to the geometry of features and/or the scale of datasets to be matched. Nevertheless, most of these processes do not holistically consider both spatial and non-spatial properties. The main consequence is that the data matching is an ad-hoc process, adapted to particular cases. Despite the emergence of a number of papers exploring the use of semantic information to quantify semantic similarity between concepts  [12, 21] , only a few approaches are using it to match features, for example, in land use change studies  [12].
On the other hand there are some cases where analyzing criteria in sequence is not efficient. It can lead either to overmatching or under-matching results, when isolated criteria are either not strict enough or www. josis. org too strict (e. g. , two features may be matched if, simultaneously, they have more or less the same attribute values, are relatively close and have similar shapes). One key issue when matching relates to imperfection in data, and in particular how to manage imperfect positions, imperfect classifications, errors, or missing data. Imperfections are inherent in vector data. The complexity of the real world leads to different interpretations, and thus errors, imprecision, and missing data when represented in a database. Features that appear in a high quality dataset may not exist in a lower quality dataset.
Features may also appear in the lower quality dataset that do not exist in the higher quality dataset. Different scales may lead to different link cardinalities (1:1, 1:n, and n:m). A vector database is always a compromise between cost and quality. Although the issue of imperfections induced by the mapping process has been studied extensively in the field of GIS  [20] , there is still no widely accepted classification of the different aspects of quality. The different terms being employed include: uncertainty, imprecision, fuzzy, error, and ambiguity. Reasoning with imperfect knowledge is a topic in artificial intelligence, where imperfections are generally classified in three types: imprecision (regarding the difficulty of clearly and precisely defining a state of reality by a proposal), uncertainty (doubts about the validity of knowledge), and incompleteness (refers to the complete or partial absence of knowledge)  [8, 43].
Step 1: Selection of candidates The first step consists of defining the frame of discernment. For each feature F 1 in DataSet1, we look for close features in DataSet2, according to a distance criterion. These features are the candidates {C i } i=1,. ,N for matching with F 1. The frame of discernment is then defined as follows: Θ F 1 = {appC 1 , appC 2 ,. , appC i ,. , appC N , NM} (4) The frame of discernment is the set of hypotheses appC i expressing that "candidate C i represents the same real world object than the feature F 1. " Note that in some cases real objects are represented only in one datasets. This case occurs for example when datasets have different actualities (e. g. , a new real world object is mapped DataSet1 but not yet in DataSet2) or different mapping specifications. To take into account the case where a feature from DataSet1 may have not identical feature in DataSet2, the hypothesis, NM, standing for "feature F 1 is not matched to any features in DataSet2," is added.
Thus, the NM hypothesis has three main purposes: i) it models cases when real objects are not represented in both datasets; ii) the frame of discernment becomes exhaustive by adding the NM hypothesis; and iii) conflicts generated by the fusion of criteria are less important, since the solution necessarily belongs to the frame of discernment. Example Let us suppose a feature F 1 in DataSet1 and two candidates C 1 and C 2. The frame of discernment is then defined as: Θ F1 = {appC i , appC 2 , NM}, meaning that F 1 can be matched with C 1 or C 2 or not-matched at all.
Position criterion This criterion is based on the distance between the positions of two features. This criterion has already been proven to be efficient when based on Euclidean distance between isolated points  [33] , or based on Hausdorff distance between edges of roads networks  [34]. We strongly believe that any position criterion could be used with a similar modeling of masses of belief for other distances, and that thresholds are related to the known accuracy of datasets. The following example shows how our knowledge about geographic data can be modeled as belief assignment functions for this criterion: (1) The closer the features are, the more we believe they should be matched together, and the less we believe they should not be matched. This is translated in the globally decreasing (resp.
increasing) shape of function for the hypothesis appC i (resp. www. josis. org ¬appC i ). This knowledge is "continuous" in the sense that there is no natural distance threshold beyond which the belief should drastically change. More generally, most functions are continuous functions in our approach to avoid thresholds effects. (2) If the distance between features (F 1 and candidate C i ) is below the accuracy of the less detailed dataset (threshold T 1 ), there is no reason to believe that the two features could not be matched, whatever the distance is. This is reflected by the fact that the mass of belief of hypothesis ¬appC i is zero in this case. (3) In general, beyond a certain distance (threshold T 2 ), two features should not be matched together. Therefore, the hypothesis appC i becomes in this case equally improbable, whatever the distance is, while hypothesis ¬appC i becomes equally credible.
Knowledge about linear data can be translated into functions using the following rules: (1) Colinear features are more likely identical than perpendicular ones. This explains the global shape of functions for hypotheses appC i and ¬appC i. (2) However, the orientation criterion may not be sufficient to make a decision, even if it gives some clues for the final decision. Especially when datasets have different levels of detail, many identical features may not have the same orientation. Consequently, ignorance has a significant weight. In the example in Figure  2 , the mass of belief assigned to ignorance has the same value whatever the value of the angle is. This is a typical way of easily giving more or less importance to some criteria.
Knowledge based on semantic information To compare the different semantics of features, a semantic distance, d S is needed. Semantic distance may be computed using Wu and Palmer distance  [4]. In our experiments this distance is computed using a geographic ontology, obtained by automatic extraction from textual specifications of the two datasets  [1]. From a semantic point of view, if the semantic distance is close to 0, then two features have similar natures (e. g. , peak and summit). If the semantic distance is close to 1, these features are very different (e. g. , river and summit). Semantic criteria are typical "necessary but not sufficient" criteria. Two features may only be matched if they have close natures; but all features having the same nature are not necessarily matched. Figure  3  illustrates how to model such a semantic criterion: (1) The "not sufficient" part is modeled by sharing the mass of belief between the hypothesis appC i and ignorance when features have close natures.
Knowledge based on names Some attributes representing the name of the features (e. g. , toponym or postcode) or the name of groups of features (e. g. , road number) may be considered as pseudo-identifiers. However in some cases, these names may be only sparsely filled in the database (e. g. , names of features may be only filled in for important objects). Moreover, in some cases vernacular names can co-exist, such as with places. To model that, let us describe two criteria to compare names. The Complete Name Criterion is especially adapted to name attributes filled in, but possibly imprecise, such as toponyms. The Incomplete Name Criterion is adapted to incomplete but precise data such us road name). Both criteria are based on string comparison using Levenshtein distance, noted d T  [27].
Incomplete Name Criterion The fourth column in Figure  3  shows the mass of belief assignments for the Incomplete Name Criterion. For this criterion, datasets reflecting close points of view (i. e. , features may be simultaneously thought of as "important" or "not important") are considered. Hence, we believe that names of identical features should be simultaneously filled in or not. Another hypothesis would lead to a different definition of belief. Four cases are considered when feature F 1 and candidate C i are compared: (1) Case A: The attribute is not filled in both datasets. In this case, a decision cannot be made, assigning a major mass of belief to ignorance. The complement of ignorance is divided into hypotheses appC i and ¬appC i. More credibility may be given to appC i than to ¬appC i , as the fact that the name is not filled in both datasets may reflect that the two features have a similar level of importance.
(2) Case B: Only one feature has a name value filled in. It is not impossible that the two features are identical, so we assign to the hypothesis ¬appC i a relevant mass of belief, with a low ignorance. (3) Case C: Features have different names. In this case we believe that C i is not an identical feature, and a significant mass of belief is assigned to ¬appC i , with a low ignorance. To manage cases in which vernacular features co-exist, the hypothesis appC i is not completely rejected. Instead a low, but not null, mass of belief being assigned to it. (4) Case D: Features have the same names. It is highly probable that the features are identical. However, there are cases when an object of the real world is represented by many feature in datasets (e. g. , roads are represented by line-segments).