Demonstrating Conformance with Regulations

Evidence Support Logic, as implemented in TESLA, can provide a structured approach to demonstrating regulatory compliance against subjective criteria when judging multiple lines of evidence that are uncertain and/or conflicting.

Demonstrating to regulators and other stakeholders that a particular activity conforms to regulations usually requires multiple arguments to be presented, supported by various kinds of evidence. Confidence among the stakeholders will be increased if it can be shown that multiple lines of evidence support the assertion that regulations have been complied with. Where there is uncertainty connected with a particular piece of evidence, this uncertainty must be shown not to call into question conformance with the regulations. Furthermore, there is a need to provide an audit trail, to record how each regulation has been met.

In many cases regulations are necessarily framed in imprecise terms, recognizing subjectivity in judgements about conformance. For example a regulation might require the impact of some activity to be ‘as low as reasonably achievable’ (i.e. the principle of ALARA to be followed), but there is no precise definition of ‘reasonably’. In such cases there is a need for a conversation among stakeholders, including regulators, leading to development of a consensus about the criteria by which it is judged whether regulations have been met.

Evidence Support Logic (ESL) is a valuable tool that can help meet these goals. It can be used to:

  • provide a graphical representation of judgements about conformance to regulations;
  • relate different judgements to one another and to the evidence that underpins them;
  • analyse the level of confidence that can be placed in these judgements;
  • provide an approach for structuring and recording conversations among stakeholders leading to the development of a consensus about subjective criteria that are used to judge conformance to regulations.

In such applications, a hypothesis that an overall regulatory requirement is met (e.g. ‘The system will be safe’) is broken down into two or more supporting ‘child’ hypotheses, each of which correspond to supporting regulatory requirements (e.g. ‘The system has been implemented in conformance with quality standards’ and ‘The components of the system conform to materials quality standards’). Each of these supporting hypotheses may be broken down further, and their child hypotheses may be broken down in turn. This process of breaking down hypotheses is continued until a decision tree has been constructed at an appropriate level of detail. This level of detail will need to represent all the arguments and evidence that underpin the decision at the top level. Depending upon the nature of the system being evaluated and the regulations that apply to it, child hypotheses at intermediate positions in the tree may also correspond to regulations. Indeed, it is possible that hypotheses at the lowest level of the tree may also correspond to regulations. However, generally, hypotheses at the lower levels of a tree will correspond to judgements on evidence that could support (or refute) the judgements on the regulations, rather than regulations themselves.

Once a tree has been constructed, the degree of confidence that each hypothesis at the lowest level of the tree is true or false is judged independently and represented numerically (on a scale of 0-1), based on the available evidence. The confidence for and against these hypotheses is then propagated through the tree, depending upon carefully selected logical operators and parameters akin to weights. An important point is that the separate assessments of evidence ‘for’ and ‘against’ propositions result in honesty about what is not known or is uncertain.

The application of ESL for demonstrating conformance to regulations can be illustrated by a tree developed during the CO2ReMoVe project, which was funded by the European Commission (EC) and an industrial consortium consisting of BP, Statoil, Wintershall, TOTAL, Schlumberger, DNV, ExxonMobil, ConocoPhillips, Vattenfall and Vector (Metcalfe et al., 2013). The tree is structured to reflect the general regulatory requirements for the underground storage of CO2, as outlined in the EC Storage Directive (2009/31/EC). The top level hypothesis reflects Article 1 of the Directive, which requires that: ‘The CO2 volume planned to be stored will be completely and ‘permanently’ contained’. There is no precise definition of ‘completely and permanently’ in the Directive and literal conformance to these terms is not technically achievable; it will be impossible to give complete assurance that no leaks will occur under any circumstances. Consequently in applying the tree there is a need to agree with regulators how these terms should be treated and to reflect the outcome in the specification of confidence measures. There will need to be similar discussions about other subjective criteria against which many hypotheses need to be judged. For example, the definitions of ‘significant’ and ‘plausible’ are subjective.

Beneath the top-level hypothesis, the tree’s structure reflects two general kinds of judgment:

  • whether or not the root hypothesis is supported by the site’s observed behaviour;
  • whether or not existing understanding of the site’s characteristics and behaviour is supportive of an approach to long-term stability following operations.

Also reflecting the EC Storage Directive, the tree’s structure embeds several judgments of the likelihood that different generalised scenarios for a site’s future evolution will call into question long-term evolution towards stability:

  • the ‘normal’ or ‘expected’ evolution scenario; and
  • four hypothetical ‘alternative evolution’ scenarios:
  • a ‘tectonic processes’ scenario (covering active faulting, seismic pumping, subsidence and uplift);
  • a ‘well-seal failure’ scenario;
  • a ‘human intrusion’ scenario; and
  • an ‘overfilling scenario’.

Each scenario is represented by a separate sub-branch of the tree.

The four hypothetical alternative evolution scenarios are evaluated primarily to establish their potential consequences in the hypothetical cases that they should occur. The tree reflects judgments of:

  • the likelihood that the scenario will occur; and
  • the hypothetical impact of the scenario in the event that it does occur.

The figure below shows the top four levels of the tree, after Metcalfe et al. (2013). This figure shows how the tree was applied to the Krechba CO2 storage site in Algeria, using information available at the end of 2011. The tree shows that this information gives great confidence favouring long-term CO2 containment at the site, with only small remaining uncertainties and evidence against (represented by the white and red fields respectively).

European Parliament and Council of the European Union (EC), 2009. Directive 2009/31/EC of the EC of 23 April 2009 on the Geological Storage of Carbon Dioxide.

Metcalfe R, Paulley A, Suckling PM, and Watson CE, 2013. A tool for integrating and communicating performance-relevant information in CO2 storage projects: description and application to In Salah. Energy Procedia 37 (2013) 4741 – 4748.