This project will research and develop new methods to enable the assessment of risk arising from the bias introduced by alternative interpretational concepts and paradigms to the same geological data set. A crucial task for the petroleum industry is assessing the risk associated with estimates of hydrocarbons. This is of increasing importance for maximising recovery in basins at or past peak production, such as the North Sea. The identification of new reserves in more complex geological situations must also occur within an acceptable commercial risk envelope. Uncertainty in data measurement, collection and processing can be accounted for, at least crudely. A more fundamental difficulty, however, is correctly assigning risk due to interpretational bias. Models of sub-surface geology are created from data sets such as 3D seismic data, well bore geophysical logs, and remote sensing data. These data sample a limited volume of the subsurface and at a limited resolution in time and space, therefore the final model is highly dependent on the interpreter's conceptual framework. It is often the case that interpreters from different educational backgrounds, or with experience in different oil field settings, can come up with very different results for the same data. Models of sub-surface geology are data-under-constrained natural systems and 'diagnostic skill' and statistical uncertainty have significant social and economic impact. This problem will be examined by determining the variability of models derived from synthetic seismic data sets using a number of interpretational concepts. Synthetic seismic data will be created from a fully defined geological model created in Midland Valley's software. The data sets will then be subject to interpretation, and the concepts applied to the data without prior information will be compared to control groups, given prior information. The results will be analysed for variation from the 'real' geology, and for variability between the control and non-control groups. Quantification of the differences will be used to assess the degree of uncertainty due to interpretational bias. The work on synthetic seismic data will be complimented by a field based study, naturally limited in 3D by exposure. Mapped fault networks will be used to create multiple structural models based on different concepts for geometrical fault linkages, in an area that has natural leakage of CO2. Midland Valley's newly developed software 4DMove will be used to validate and assess the uncertainties related to the different interpretational concepts collected, both for the synthetic seismic and the field data. Analysis in 4DMove will be supported by polytomous regression analysis of information captured from participants in questionnaires to assess influences from other factors such as: experience, education and training. Compartmentalisation, and hence hydrocarbon reservoir or CO2 storage potential will be assessed in the software package TrapTester and quantified for different structural models, to highlight the critical impact of sub-surface structural frameworks on reservoir connectivity and hence potential. The current success rate of wells in the North Sea stands at between 35-40%, and with the cost per well c. 10 million dollars, increasing to c.50 million dollars in ultra deep water, erroneous well positioning is a waste of oil company resources. Any tool that reduces geological concept uncertainty will have a large impact within the industry. Similar arguments, for social and economic impact can be made for sub-surface waste disposal and CO2 storage. The projects objectives are: -to develop techniques and methodologies to assess factors influencing conceptual uncertainty -to quantify interpretational error -to quantify the impact on prospectivity of different models (using 4DMove) -to create a process to reduce the uncertainty associated with the structural model in petroleum exploration and waste storage.

Sandstones are important reservoirs for hydrocarbons and are likely to provide most of the opportunities for underground carbon storage. Many reservoirs are hosted in folds formed by compressional tectonics. However, the successful exploitation of subsurface sandstone reservoirs demands understanding of the fractures patterns that form as a consequence of the compression - they can greatly alter the reservoir performance. Significant uncertainty exists - can models of the structure on a large scale predict the distribution, concentration and orientation of fracture damage through this structure? It is this question that this CASE studentship is designed to answer, using an example study from outcrop. This project partners the University of Aberdeen with Midland Valley Exploration Ltd (MVE), world leaders in the provision of structural geology software to the subsurface geoscience industries. The partnership will provide the student with excellent training in commercially-relevant structural geology. The results will immediately benefit MVE and their extensive client base. MVE will meet the standard CASE requirements of a stipend supplement and internship, together with the entire cost of fieldwork and will provide their Move software with technical support without charge. The project involves several components: field structural mapping; 3D model-building; structural restoration; forward modelling; computation of stress, strain and fracture patterns; combination of multiple model outputs; and testing model predictions against natural fracture patterns. The student will therefore be exposed to the entire workflow followed by industrial structural geologists. All of these activities are supported within MVE's Move software. The field case study is located in the southern the Moine Thrust Belt, NW Scotland, where well-exposed and accessible fold-thrust structures, developed in Torridonian sandstones, provide excellent analogues (scale and rock type) for structures in the subsurface. The student will map these digitally in the field using by MVE's Move software. Mapping will be facilitated by using geological and terrain data available through collaboration with BGS. The mapping provides the foundation for creating a 3D model (in Move) for the structure of selected folds. The models in turn will be restored to their undeformed (unfolded) state and then forward-modelled in Move to form evolutionary histories. The history of folding will then be used to model the stress evolution of the constituent beds using Move's mass spring solver. When populated with theoretical rock properties, different loading conditions and fracture criteria, an array of model predictions for fracture patterns will be created that can be combined to form fracture probability maps. These predictions will be tested against direct measurements of fracture patterns made in the field. Existing predictions of fractures through large-scale fold structures use in situ examples with point measurements from subsurface wells and drill holes up-scaled and interpolated to surrounding rocks. Yet up-scaling is fraught with uncertainty - commonly the predictions are not supported by subsequent drilling campaigns. By using an outcrop-based test the student will resolve fundamental questions such as whether fracture patterns correlate with final (observed) geometry of the hosting large-scale fold (e.g. curvature) or reflect the total strain history of the structure. The approach will also delineate which parts of structures are particularly sensitive to model input parameters (rheology, imposed fracture criteria, loading conditions) thereby risking predictions of fracture patterns. Training will be provided in all relevant aspects of structural geology both in the field and through modelling, both in Aberdeen and within MVE offices in Glasgow. The student will complete their PhD well-placed to follow a career in industry or to continue in research.

This project will research and develop new methods to enable the assessment of risk arising from the bias introduced by alternative interpretational concepts and paradigms to the same geological data set. A crucial task for the petroleum industry is assessing the risk associated with estimates of hydrocarbons. This is of increasing importance for maximising recovery in basins at or past peak production, such as the North Sea. The identification of new reserves in more complex geological situations must also occur within an acceptable commercial risk envelope. Uncertainty in data measurement, collection and processing can be accounted for, at least crudely. A more fundamental difficulty, however, is correctly assigning risk due to interpretational bias. Models of sub-surface geology are created from data sets such as 3D seismic data, well bore geophysical logs, and remote sensing data. These data sample a limited volume of the subsurface and at a limited resolution in time and space, therefore the final model is highly dependent on the interpreter's conceptual framework. It is often the case that interpreters from different educational backgrounds, or with experience in different oil field settings, can come up with very different results for the same data. Models of sub-surface geology are data-under-constrained natural systems and 'diagnostic skill' and statistical uncertainty have significant social and economic impact. This problem will be examined by determining the variability of models derived from synthetic seismic data sets using a number of interpretational concepts. Synthetic seismic data will be created from a fully defined geological model created in Midland Valley's software. The data sets will then be subject to interpretation, and the concepts applied to the data without prior information will be compared to control groups, given prior information. The results will be analysed for variation from the 'real' geology, and for variability between the control and non-control groups. Quantification of the differences will be used to assess the degree of uncertainty due to interpretational bias. The work on synthetic seismic data will be complimented by a field based study, naturally limited in 3D by exposure. Mapped fault networks will be used to create multiple structural models based on different concepts for geometrical fault linkages, in an area that has natural leakage of CO2. Midland Valley's newly developed software 4DMove will be used to validate and assess the uncertainties related to the different interpretational concepts collected, both for the synthetic seismic and the field data. Analysis in 4DMove will be supported by polytomous regression analysis of information captured from participants in questionnaires to assess influences from other factors such as: experience, education and training. Compartmentalisation, and hence hydrocarbon reservoir or CO2 storage potential will be assessed in the software package TrapTester and quantified for different structural models, to highlight the critical impact of sub-surface structural frameworks on reservoir connectivity and hence potential. The current success rate of wells in the North Sea stands at between 35-40%, and with the cost per well c. 10 million dollars, increasing to c.50 million dollars in ultra deep water, erroneous well positioning is a waste of oil company resources. Any tool that reduces geological concept uncertainty will have a large impact within the industry. Similar arguments, for social and economic impact can be made for sub-surface waste disposal and CO2 storage. The projects objectives are: -to develop techniques and methodologies to assess factors influencing conceptual uncertainty -to quantify interpretational error -to quantify the impact on prospectivity of different models (using 4DMove) -to create a process to reduce the uncertainty associated with the structural model in petroleum exploration and waste storage.