A ”reservoir model” is a mathematical representation of a specific volume of rock incorporating all the “characteristics” of the reservoir under study. It can be considered as a conceptual 3D construction of a single reservoir or in some cases of an oil/gas field.
Data derived from various sources are integrated by deterministic or geostatistical methods, or a combination of both, to construct the model. Its setting up, however, is a dynamic process, since a reservoir model must be continuously up-to-dated and revised when new data become available or inconsistencies between the predicted and real reservoir behavior are found.
The reservoir model is, therefore, the result of studies whose main objective is to understand and describe the dynamic behavior of a hydrocarbon reservoir in order to predict its future performance under different development and production strategies.
From practical point of view, the integrated reservoir modeling represents now the most valuable technical approach for estimating the oil/gas reserves and computing the future production profiles, reducing the uncertainties always associated with the static and dynamic reservoir descriptions.
Workflows for integrated reservoir modeling
There are several reasons why an integrated reservoir modeling has found a strong and rapid development:
The integrated reservoir modeling finds application in different stages and phases of the reservoir life cycle.
In the case of field development it is used for:
In the case of producing field it is used for:
Dynamic reservoir model for production forecasts
In addition, the 3D integrated reservoir modeling:
A static reservoir model is the one incorporating all the geological features (i.e. structural, sedimentological, petrophysical, etc.) of an underground volume of rock that can store fluids (hydrocarbons and/or water) and can allow their movement.
In general, the static model of a reservoir is the final integrated product of the structural, stratigraphic and lithological modeling activities, where each of these steps is developed according to its specific workflow.
A static reservoir study typically proceeds through four main stages.
1. Structural modeling
A structural model showing faults and layering
2. Stratigraphic modelling
Example of a 3D Stratigraphic Model – L. Cosentino
3. Lithological modeling
Example of a stochastic model of facies – L. Cosentino
4. Petrophysical modeling
A petrophysical model showing porosity distribution and values
The results of these different stages are integrated in a two (2D) or three-dimensional (3D) context, to build an integrated geological model of the reservoir.
This model represents the reference frame for calculating the quantity of hydrocarbons in place, and on the other, forms the basis for the initialization of the dynamic model.
The dynamic model combines the static model, pressure- and saturation-dependent properties, well locations and geometries, as well as the facilities layout to calculate the pressure/saturation distribution into the reservoir, and the production profiles vs. time.
A dynamic model can be used to simulate several times the entire life of a reservoir, considering different exploitation schemes and operating conditions to optimize its depletion plan.
Integrated reservoir modeling and simulation
Reservoir simulation is a branch of petroleum engineering developed for predicting reservoir performance using computer programs that through sophisticated algorithms numerically solve the equations governing the complex physical processes occurring during the production of an oil/gas reservoir.
Basically, a reservoir simulation study involves five steps:
Factors to consider in selecting the simulation model are
Different types of simulators are used to represent the drive mechanisms of different types of reservoirs, and the selection depends on the type and behavior of the original reservoir ﬂuids and on the predominant process controlling the reservoir production and hydrocarbon recovery:
Upscaling of the geological models is key issue in the reservoir simulation. It is basically a process by which a very heterogenous region of the reservoir rock described with a huge amount of “fine grid cells” is replaced by an equivalent less heterogeneous region made up of a number of single coarse-grid cells. The “upscaled geological model” must, however, maintain the same storage and transport properties of the reservoir rock described with detail by the “fine geological model”. The upscaling process, therefore, is essentially an averaging procedure in which the static and dynamic characteristics of a fine-scale model are approximated by those of a coarse-scale model.
Conceptual illustration of the upscaling process
In a numerical simulation study historical production/injection data (oil, gas, and water rates) must be supplied to the mathematical model. Of course, good quality production/injection data are essential for a reliable simulation study, in terms of direct input data and reference data to evaluate the accuracy of the history match phase.
Past history matching is the most practical method for testing a reservoir model’s validity and for calibrating the geological model. Basically history matching is a process of reservoir parameter adjustment in such a way that the simulated reservoir behavior reproduces the actual reservoir behavior.
History matching process should also help to identify possible points of weaknesses in the initial reservoir model, and should help to find and evaluate the most efficient ways to overcome them.
Two possible approach to History Matching
Once calibrated, the simulation models are then used to compute the production forecasts considering various hypotheses for the reservoir exploitation.
In simple cases, this prediction phase can be performed in a few days, while the in more complex cases it can take several months depending on:
A general sequence for running the prediction phase can be summarized as follows:
Schematic of a 3D uncertainty workflow.
It shows the most common inputs, stages in the 3D modelling process and corresponding outputs