Conceptual uncertainty in the interpretation of geological data: statistical analysis of factors influencing interpetation and associated risk

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.