From Data to a Conceptual Model
Learning objectives
- Explain how multiple datasets combine into a conceptual model
- Identify what each data type contributes
- Describe the role of the conceptual model in the workflow
- Recognize the risk of building without a conceptual model
No Single Dataset Is Enough
You have now met the main data. Deposition tells you the geometry to expect, traps and seals tell you where hydrocarbons sit, logs read the rock in detail at the wells, and seismic frames the structure across the field. The craft of reservoir modeling is fusing them into one coherent conceptual model, a clear statement of what the reservoir is and how it is arranged, before any cell is filled.
What Each Source Contributes
Toggle the data sources in the widget and watch the model build. Seismic alone gives you the structural envelope but nothing about the rock inside it. Adding logs pins down the layering, but only at the wells. Adding the depositional analog supplies the geometry between the wells, the part no instrument measures directly. Only with all three is the picture complete.
Why the Concept Comes First
The conceptual model is the single most important and most underrated step. Everything downstream, the variogram ranges, the object shapes, the trends, the layering scheme, is a way of expressing the conceptual model in numbers a computer can simulate. If the concept is wrong, for example modeling a channel system as if it were sheet sands, the geostatistics will faithfully build the wrong reservoir, and no amount of data conditioning will save it. Spend the time to get the story right first. The rest of this course is how to turn that story into a quantitative model and then flow it.