Reservoir Modeling

What Is Reservoir Modeling?

Reservoir modeling involves the construction of a computer model of an oil or gas reservoir. It is used to improve the estimation of reserves, support informed decision-making regarding field development, predict future production, determine optimal well placement and evaluate alternative reservoir management scenarios.

A reservoir model represents the physical space of the reservoir through an array of discrete cells, each associated with attributes such as porosity, permeability and water saturation. Reservoir models fall into two categories: geological models, which provide a static description of the reservoir prior to production, and reservoir simulation models, which simulate the flow of fluids within the reservoir over its lifetime. These models can be integrated into a "shared earth model," or developed independently with the simulation model using a coarser grid.

Why is Reservoir Modeling Important?

Reservoir modeling and simulation are, together with petrophysical analysis, powerful tools that allow us to interpret, visualize, and analyze oil and gas reservoirs. With the ongoing advancements in technology, reservoir modeling and simulation performance have improved significantly in terms of computation speed and accuracy.

Data-driven reservoir modeling bridges the gap between measured geodata and the geological model, enabling operators to explore uncertainties and minimize exploration risks. Reservoir simulation integrates the physics of fluid flow in porous media, building relationships between geological information, fluid flow and field constraints to create unparalleled clarity in subsurface interpretation and visualization.

How is Reservoir Modeling Done?

The process of reservoir modeling begins with understanding the reservoir's properties, including porosity, permeability and saturation, and fluid properties such as pressure, volume and temperature data. Reservoir geometry, well completion and aquifer models also play a crucial role. The reservoir model is then built using deterministic modeling or stochastic modeling, the former being based on single values for input parameters and the latter relying on a range of values that represent uncertainty in model inputs.

The model is validated through a rigorous process involving history matching, sensitivity analysis and uncertainty quantification, utilizing production data, pressure transient tests and other reliable sources to improve model predictions.

Obstacles in Accurate Reservoir Modeling

Reservoir modeling presents several challenges. The complexity of subsurface data, which can vary significantly in size and intricacy, requires a robust and flexible tool to manage and interpret it effectively. Uncertainties in data and interpretations pose another challenge, necessitating a system that can capture and include these uncertainties across the workflow.

Complex geologies and the need to leverage seismic data further complicate the process. Additionally, the need for efficient and repeatable workflows, from seismic to flow simulation, calls for automation and seamless integration of processes. Lastly, the requirement to quantify geological risk across the entire value chain for faster, better decision-making adds another layer of complexity to reservoir modeling.

Reservoir Modeling and Simulation with Aspen RMS™, Aspen SKUA™ and Aspen Tempest™

AspenTech offers two advanced 3D reservoir modeling suites that provide geologists and reservoir modelers with the workflows, tools and flexibility needed to efficiently and accurately represent the subsurface and perform petrophysical analysis.  Aspen RMS and Aspen SKUA enable faster, more robust and reliable production models, improved decision-making, enhanced field performance and higher recovery rates in even the most complex reservoirs.


Our modeling software allows users to capture and propagate uncertainties in both data and interpretations across the workflow, leading to more justifiable investments. A uniquely integrated environment facilitates inter-disciplinary workflows, empowering geoscientists to investigate alternative scenarios, integrate data from different sources, and efficiently manage subsurface exploration and production data.


For reservoir engineering and simulation, the Aspen Tempest integrated software suite provides a full range of tools in a single, consistent interface, ensuring reliable, accurate reservoir predictions.



What are the basic concepts of reservoir modeling and simulations?

Reservoir modeling involves the construction of a computer model of a hydrocarbon (or other) reservoir to improve estimates of reserves and make informed decisions regarding field development. The model represents the reservoir's physical space as an array of discrete cells. Each cell has associated values for attributes such as porosity, permeability and water saturation. The main elements of a reservoir model include reservoir properties, fluid properties, well completion and reservoir geometry and aquifer models.

What is the difference between deterministic modeling and stochastic modeling in reservoir modeling?


Deterministic models in reservoir modeling are based on single values for input parameters, providing a certain outcome. However, stochastic models rely on a range of values representing uncertainty in model inputs. Stochastic models typically provide more realistic representations of subsurface heterogeneities.


Why is software important for reservoir modeling?


Reservoir modeling software plays a crucial role by providing the necessary computational power and analytical capabilities to build, simulate and analyze reservoir models. It helps to integrate information from various sources, perform interpretations and build models where uncertainties can be captured and propagated across the workflow. Advanced software solutions also leverage machine learning to enhance computation speed and accuracy, improving the performance of reservoir modeling and simulation.


How are reservoir models validated?


Reservoir model validation is a critical process that involves history matching, sensitivity analysis and uncertainty quantification. It is important to utilize production data, pressure transient tests and other reliable sources to improve model predictions. This ensures that the model is a credible representation of the actual reservoir, enhancing the reliability of future production predictions and decision-making processes.