As the world accelerates toward decarbonization, technology dedicated to carbon capture, transportation, utilization, and storage (CCTUS) stands as one of the most practical pathways to mitigate greenhouse gas emissions. Yet achieving this goal depends not only on capturing CO₂ efficiently, but also on safely transporting and injecting it into storage formations.
Behind every CO₂ pipeline or injection well lies a fundamental materials challenge:
How can we ensure that the chosen alloys will withstand the aggressive chemical environments created by CO₂ and its impurities?
At OLI, this question is addressed through autoclave modeling grounded in thermodynamics — a powerful synergy that bridges laboratory experimentation with predictive simulation.
Understanding the Challenge: The Chemistry Behind CO2 Transportation and Injection
CO₂ Transport
During CO₂ transport, the goal is to define operating conditions that prevent formation of corrosive acids or solid phases that can threaten pipeline and equipment integrity. While pure CO₂ is relatively benign, the presence of trace impurities such as water, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), oxygen (O₂), and others can drastically alter its behavior. Even at low concentrations, these components can lead to acid formation, phase separation, or precipitation of solids — each with implications for corrosion and safety.
CO₂ Injection
The situation becomes more complex during CO₂ injection into saline aquifers. Once CO₂ comes into contact with formation brine, a dynamic system develops where pH decreases, salt concentrations rise, and aggressive ions (particularly chlorides) exacerbate corrosion processes. Impurities in the injected gas can react and form strong acids such as sulfuric and nitric acids, further intensifying the corrosive environment. Understanding these evolving chemical conditions is key to selecting materials capable of maintaining long-term integrity.
The Role of Autoclave Testing and OLI Modeling
Autoclave experiments are essential tools for replicating these environments under controlled conditions. However, designing accurate and efficient autoclave experiments requires precise knowledge of the chemical interactions, speciation, and equilibria expected to occur within the vessel.
This is where OLI provides a distinct advantage. OLI offers a user-interface platform where users can leverage OLI’s Mixed Solvent Electrolyte (MSE) thermodynamic framework. The model incorporates decades of validated thermodynamic data for CCTUS chemistries and can simulate the charging process of an autoclave step by step. The table below highlights the breadth of chemistries investigated over several decades (see Table 1). For emerging chemistries, OLI has collaborated with the Norwegian Institute for Energy Technology (IFE) to generate new experimental data and validate predictive models.
Table 1. Redox States and Ion Interactions Available in OLI’s MSE Thermodynamic Framework (as of OLI Version 12.5)
The result is a chemistry- and thermodynamics-driven experimental recipe that minimizes uncertainty and reduces costly trial-and-error in the laboratory.
Autoclave charging can be applied to both CO₂ transport and CO₂ injection cases. Note that OLI is not limited to these two autoclave scenarios; it can also be applied to other types of autoclave testing, including oil and gas upstream material selection, geothermal systems, and metals and mining processes.
Essential Simulation Parameters
From a simulation perspective, it is important to have a clear understanding of the final test conditions — including temperature, pressure, and available vessel volume. Note that available volume is not simply the total vessel volume, but the volume remaining after accounting for any equipment, fixtures, or coupons inside the autoclave.
It is also essential to determine the final phase state of the CO₂ stream — whether it will exist as a vapor, liquid, or dense phase — as this guides how the simulation should be configured. If this is not known, the software can help identify likely phase behavior under the specified conditions.
Finally, understanding the autoclave loading procedure is critical: which gases are introduced first, how many impurities are present, and at what concentrations.
At the end of the setup process, you should be able to define a matrix that includes the following parameters:
- Final temperature
- Final pressure
- Available vessel volume (after accounting for coupons or internal fixtures)
- Number of impurities to be analyzed
- Concentration levels of impurities at final conditions
CO₂ TRANSPORT CASES
Model Configuration in OLI Flowsheet: ESP
Consider an autoclave experiment designed to represent CO₂ pipeline conditions at 20 °C and 200 psia, with a total vessel volume of 3 liters. The test includes five impurities — H₂O, NO₂, H₂S, SO₂, and O₂ — each with a target value at final conditions summarized in Table 2 below.
Table 2. Example CO₂ pipeline conditions for CO₂ transport application
Figure 1. Example CO₂ Transport autoclave model in OLI Flowsheet: ESP
Using OLI Flowsheet: ESP, the stepwise addition of gases is modeled to determine the equilibrium pressure after each loading stage. The results indicate the incremental pressures one would observe during the actual autoclave charging procedure.
The autoclave loading was assumed to occur at ambient conditions for each individual gas addition. After loading, the autoclave was cooled to 20 °C, and a final boost of CO₂ was added to reach the target pressure.
The software then calculates the total mass (or volume) of each mixed gas that must be added to the system. It also reports the equilibrium pressure at each step, corresponding to the pressure you would read during the experiment once equilibration is reached. The pressures measured for each loading step are summarized in Table 2.
You can use this approach with fewer or more impurities, but adding more impurities will increase calculation time. When recreating CO₂ transport scenarios, we can evaluate combinations of critical impurities. However, the more impurities included, the more complex both the simulation and experimental procedure become.
This tool can help you evaluate autoclave charging using a pre-mixed tank, or assist you in designing the gas mixture needed to achieve your final target composition.
CO₂ INJECTION CASES
These CO₂ injection cases represent injection into saline aquifers.
The CO₂ injection cases are more elaborate than the CO₂ transport cases because they include an additional liquid phase to represent formation brine. Ideally, the autoclave should represent the conditions that occur when mixing the CO₂ stream and formation water. In this environment, fluid chemistry evolves dramatically.
Several factors come into play: interaction between CO₂ and formation brine, high partial pressure of CO₂ (which lowers pH), and high salt content — particularly chloride ions, which are known to accelerate corrosion. Buffering capacity of the brine must also be considered. Furthermore, impurities such as SO₂, NO₂, and O₂ can react to form strong acids that condense and further decrease pH, intensifying the corrosive environment.
The worst-case scenario often occurs during shut-in conditions, when flowback fluids stagnate and remain in prolonged contact with injection tubing, increasing the likelihood of corrosion damage. Therefore, it is critical to properly design this scenario.
One approach is to assume that the water surrounding the injection tubing material is in equilibrium with the CO₂ injectate. This assumption provides a worst-case scenario. From a testing standpoint, it is important to account for this. If the volumes of CO₂ and brine change, the corrosive conditions may also change, so this should be considered during experimental design.
We can start with the simplest case::
Case 1: CO₂ Saturation of Brine
Using OLI Flowsheet: ESP, you can evaluate how much CO₂ is required to saturate the brine, as shown in Figure 2 .
This experimental setup shows how CO₂ dissolved in brine can be configured. The total autoclave volume is 10 L, with 8 L of brine. The final conditions are 130 °F and 3,000 psia.
Step 1 represents bubbling CO₂ through the brine to remove N₂ and O₂ from the air. In this case, the brine contains NaCl to represent salinity, with a chloride concentration of 77,000 mg/L (though the solution can be more complex). Additional CO₂ fills the headspace. The software calculates how much CO₂ must be added to fill the headspace and accounts for CO₂ dissolution into the brine at ambient conditions. The calculated pH at this stage is 3.7.
After equilibrium is reached, Step 2 involves heating to 130 °F and increasing total pressure to 3,000 psia. To achieve the higher pressure, additional CO₂ must be added, as represented by the second controller labeled “total pressure.” The final brine pH is calculated to be 2.8. This low pH highlights the importance of considering buffering capacity, which leads to the next approach.
Figure 2. Model configuration in OLI Flowsheet: ESP to determine amount of CO₂ required to saturate the formation brine
Case 2: Adjusting pH with Buffering Capacity
Adjusting the pH at ambient conditions by adding NaHCO₃ provides buffering capacity to achieve a pH of 4.5 at final conditions.
This example illustrates the importance of buffering. The software back-calculates the NaHCO₃ required to obtain a pH of 4.5 at final (bottom-hole) conditions. Alkalinity is a critical variable because formation fluids are often in equilibrium with calcite rock, which provides alkalinity.
Figure 3. Accounting for buffering capacity for final condition autoclave – CO₂ injection
Stage 3: Adding Key Impurities
The simple autoclave case above can be extended by adding key impurities to study their impact on final solution chemistry.
Impurities are added to simulate CO₂ injectate composition and assess their effect on formation water chemistry. The image below includes six impurities: NO₂, SO₂, O₂, H₂S, CH₄, and C₂H₆.
Figure 4. Adding key impurities to reach final condition autoclave – CO₂ injection
A summary of the results of the simulation case #3 is provided in Table 3.:
Table 3. Full model summary for CO2 injection case with buffering capacity and additional impurities
Conclusions
These autoclave loading cases demonstrate how OLI Flowsheet: ESP helps experimentalists set up autoclave recipes for both CO₂ transport and CO₂ injection. A few notes are outlined here:
- These loadings are based on thermodynamic predictions, so we cannot determine how long the experiment will take to reach equilibrium.
- The software assumes that reactions will not occur during the loading process, which highlights the importance of loading the autoclave in the correct order. However, the software can help determine whether a given mixture will lead to acid formation or solid precipitation.
- The software provides a recipe at time-zero conditions, which would be difficult to determine without a rigorous thermodynamic framework.
- The more impurities added to the model, the longer the software will take to converge. It is recommended to focus on impurities that strongly impact phase behavior, or reactive impurities that can form acids or solids.
The case files used in this blog are availble to download by reaching out to OLI, please contact us if you have any questions.