Unlocking Subsurface Insights: A Journey in a Data-Driven Workflow

The past almost eight years have been an exhilarating journey into the world of geomodeling. Imagine sifting through layers of complex geological data and seismic surveys – like piecing together a captivating geological puzzle. It's a privilege to share these learnings with others as I continue to explore the depths of this field. While I wouldn't consider myself an expert yet, my passion lies in unlocking the full potential of this data. This passion for data translates directly into my goal: uncovering valuable insights and developing innovative approaches that streamline my geomodeling workflow.

Building a reliable geomodel hinges on three key principles: Representativity, Purpose, and Predictability.

Representativity ensures the model captures the important heterogeneities, within the subsurface field it represents. These heterogeneities are crucial for making the model predictive, allowing it to anticipate future behavior. However, achieving representativity must be balanced with maintaining a clear purpose. The model shouldn't become overly complex or lose sight of its original objective. This balance is what allows a geomodel to be both informative and effective.

Let's delve deeper into the concept of representativity. Building a representative geomodel starts with a strong foundation in geological knowledge. This knowledge, informed by all available data, acts as a guiding framework. While the model itself is a hypothetical representation, it should capture the significant heterogeneities, observed in the field. These heterogeneities are then translated into data assigned to individual grid cells within the model. This data serves as the raw material for further processing, often through statistical techniques, to create a geomodel that is both geologically sound and quantitatively robust.

For example, consider a thin reservoir within a deltaic environment. Here, we're primarily interested in a static volumetric assessment. In this case, we would determine the size of the grid cells by balancing the total number of cells in the 3D model with the specific thickness of the reservoir that needs to be represented. By identifying the key reservoir layers that require detailed capture, we can strategically assign cell sizes. This allows us to focus resolution on the important zones while potentially using larger cells in less critical, thinner sections. This approach ensures an efficient model that captures the essential geological features without unnecessary complexity.

The purpose of a geomodel, however, can vary greatly. Geomodels can serve a variety of purposes, ranging from static assessments of resource potential to dynamic simulations of fluid flow within a reservoir. While I believe the ultimate goal often leans towards understanding flow behavior at the reservoir scale, it's crucial to define the specific objective before any construction begins. This initial question – "What do I need this geomodel for?" – shapes the entire process. Whether it's a static snapshot of the reservoir or a dynamic tool for predicting production, a clear objective ensures the final geomodel is built with the right level of detail and incorporates the most relevant data.

This focus on purpose becomes even more crucial when considering the inherent uncertainties in geomodels. The ultimate test of a geomodel lies in its ability to predict. Since geomodels are built on statistical analysis, inherent uncertainties exist. To navigate this, a thorough uncertainty and sensitivity analysis is crucial. This analysis helps us understand how the model behaves when input data changes, revealing whether it leans towards overly optimistic or pessimistic outcomes. Ultimately, it answers the key question: – "Does the geomodel fulfill the purpose for which it was built?" – The analysis ensures the model respects all available data while honoring the geological assumptions and hypotheses that guided its construction. This process helps us refine the model and build confidence in its predictive power.

While some might view geologists as psychics predicting hidden resources, a more fitting analogy is that of a detective. Geomodels are the tools that allow us to meticulously analyze clues – geological data, seismic surveys, well logs – to uncover the secrets of the subsurface. A good prediction, such as accurately estimating the top of a reservoir or its total volume, is then validated against real-world data like production records. This ongoing comparison between model and reality allows us to continuously improve our understanding and refine our predictions over time.

The final product, then, isn't a perfectly 'true' model, but a 'true' tool. While the concept of a "true model" may be elusive, a geomodel emerges as a powerful tool at the end of the process. It serves as a communication device, facilitating clear understanding of the subsurface among team members. It offers predictive capabilities, allowing informed decisions about future behavior. Ultimately, this translates into practical applications for field development planning, guiding strategies for resource extraction and management.