The Reservoir Modeling Workflow
Learning objectives
- Explain what a reservoir model is and why we build one
- Distinguish the static model from the dynamic simulation
- Order the stages of the modeling workflow and what each produces
- Place the workflow in the asset lifecycle
What Is a Reservoir Model?
A reservoir is a body of porous rock, often kilometers underground, holding oil, gas, and water in its pore space. We can never see it directly: we sample it at a handful of wells and image it fuzzily with seismic. A reservoir model is our best quantitative guess at what fills the space between those samples, a three dimensional grid of cells, each carrying a rock type, a porosity, a permeability, and a fluid saturation. We build it to answer questions that are expensive to get wrong: how much oil is in place, how fast it can be produced, where to drill the next well, and whether a development pays.
Static and Dynamic: Two Halves of One Workflow
Every reservoir study has two halves. The static model describes the rock and fluids as they sit today: the structure, the layering, the facies, the porosity and permeability, and the volumes in place. It is geology and geostatistics. The dynamic model takes that static model, adds the physics of how fluids flow, and runs it forward in time to predict production. It is flow simulation. The two halves meet at upscaling, where the fine geological grid is coarsened into a grid the flow simulator can afford to run.
The figure traces the whole path. Follow it from the raw subsurface data on the left to a production forecast on the right, and notice the handover from the static (model-building) stages to the dynamic (flow) stages. Click any stage to see what it consumes and what it hands to the next. This same sequence is the outline of the course.
Where It Sits in the Asset Lifecycle
Models are not built once. Early in a field's life, with only seismic and a discovery well, the model is coarse and the uncertainty huge, but it still guides the decision to appraise. As appraisal wells, production data, and time-lapse seismic arrive, the model is rebuilt and history matched, and the uncertainty shrinks. A good model is a living thing, updated as the field teaches us about itself. The skill this course builds is not running one piece of software once: it is the judgment to build a model that is faithful to the data, honest about its uncertainty, and fit for the decision at hand.
References
- Ringrose, P. and Bentley, M. (2015). Reservoir Model Design. Springer.
- Cosentino, L. (2001). Integrated Reservoir Studies. Editions Technip.