Brett Freeman

Our notion of reality in seismic interpretation and structural geology usually follows a series of careful observations and ideas that eventually crystallize into a best-case model. In most other branches of science the strength or reality of such models (or hypotheses) is increased by the number of robust tests that either refine or fail to disprove the original idea. However, geological models in the Exploration and Production (E&P) sector differ because the starting point for testing is usually an interpretation of seismic or other remote measurements, rather than the direct observation of an effect. This means that whatever tests we are able to apply have, in themselves, significant margins for error and are further compounded by the intellectual issue of constructing a 3D view or model of the perceived geologic reality. This model building is an early stage of the E&P process, but errors and uncertainty at this point propagate throughout the subsequent workflow and arguably, amount to the single biggest factor affecting perceived value and in particular drilling risk.

Modern subsurface exploration uses a three-dimensional (3-D) approach to interpret faults and layer boundaries on seismic reflection data. Three-dimensional seismic reflection datasets comprise a continuous volume of reflection samples with a vertical and horizontal resolution, of typically <15 m (c.50 ft). Increasing computer power has allowed the development of interpretation software that allows for direct mapping within an on-screen 3-D representation of the data. The seismic data volume can be processed to emphasize reflections from continuous layers, or to emphasize the discontinuities (e.g., faults) affecting those layers. A key workflow is to build the interpretation of horizons (layer boundaries) and faults into a consistent framework model, where all the stratigraphic and structural surfaces fit together in a geologically realistic way. Such a model is the necessary starting point for structural geological studies and for assessing trap geometry and viability. The framework model also provides the starting geometry for building an engineering model for simulating fluid flow in hydrocarbon reservoirs.

Our notion of reality in seismic interpretation and structural geology usually follows a series of careful observations and ideas that eventually crystallize into a best-case model. In most other branches of science the strength or reality of such models, or hypotheses, is increased by the number of robust tests that either refine or fail to disprove the original idea. However, geological models in the hydrocarbon exploration and production sector differ because the starting point for testing a hypothesis is usually an interpretation of seismic data or other remote measurements, rather than the direct observation of an effect. The scientific method of prediction tested by observation is a key part of mapping three-dimensional (3-D) structures in the field and geological training. An analogous, rule-based approach also applies to the accurate creation of 3-D subsurface structural models. A defensible structural model must embody more than fault and horizon surfaces. It must also honor the rules of structural geology. Some simple rules are outlined in this chapter. These can be applied iteratively throughout the life of the seismic interpretation. Failure to honor structural rules leads to poor interpretations that may be compounded by a lack of appreciation of the importance of 3-D perspective. In this chapter, we also briefly explore the historical use and understanding of perspective. Those in the exploration and production industry need to think carefully about how to leverage the 3-D interpretation and modeling process. Most importantly, since it is managers who control the exploration and production workflow, they above all need to be informed about the advantages of using a structurally qualified 3-D model in future projects.

Geologists as a group have and use above-average spatial thinking skills to interpret and communicate complex geologic structures. Interpretation challenges, especially with petroleum industry subsurface targets, come from abundant but still ambiguous data volumes, challenging geologic forms, powerful but difficult-to-learn software, and under prepared staff. In June 2013, 70 participants met in Reno to discuss these and related issues and to explore how spatial cognitive science can help us better understand and develop geologic interpretation skills, software tools, and education strategies. Industry interpreters and trainers, academic structural geologists, software developers, and cognitive scientists brought complementary perspectives to three days of presentations, posters, and discussions, plus a field day with interactive interpretation modules. This Hedberg conference provided new shared insights to the interpretation process, ideas for improving skill development, and abundant opportunities for further collaboration.