In the oil and gas industry, reservoir flow simulations are routinely used to estimate how much oil or gas they are likely to produce, or to evaluate different production scenarios. This includes the simulation of gas injection to improve oil recovery. The codes developed for simulation of fluid flow in oil and gas reservoirs are readily adapted to simulate CO2 injection into saline aquifers. The basic requirements for fluid flow simulation are:
• A geological model
• Rock properties
• Fluid properties
• Well information
Geological model
The basic framework for the geological model consists of interfaces (horizons and faults) between different types of rock. This has been described in Chapter 3. For flow simulation, the model is subdivided into grid cells, which are assigned different rock properties, depending on the geology.
The simulation code tracks the fluid masses in each grid cell as a function of time. A description of how the boundaries of the model are defined is included in the Glossary.
Rock properties
The key properties for flow simulation are porosity and permeability. Porosity is the fraction of the bulk volume which is pore space, and therefore determines how much fluid may be stored in a rock. Permeability determines how fast a fluid can flow under a given pressure gradient. The rock compressibility is also important, because this determines the change in the porosity as the fluid pressure changes (see the Glossary for a full definition of these and other terms used in this chapter).
Fluid properties
For reservoir simulation, we need to know the density and viscosity (i.e. the resistance to motion) of each fluid. These quantities depend on temperature and pressure. In addition, we need to know what phase (liquid, gas or supercritical) a fluid is in: this also depends on temperature and pressure.
For a saline aquifer these properties are also a function of the salinity. Carbon dioxide is soluble in brine, and so we need to know the solubility of CO2 in brine as a function of temperature and pressure. All of these fluid properties have been measured in laboratories, and the required data may be entered as inputs in the simulation software used for CO2 flow modelling, or indeed may already be included within the software’s database.
When two fluid phases (e.g. gas and water) are flowing in a rock, they interfere with each other, so that the total flow rate is less than the rate for a single phase. This is taken into account using relative permeabilities, which are dependent on the fraction of the pore space occupied by that phase – commonly termed fluid saturation (see Glossary). The concept of relative permeability is critical in calculations of CO2 injectivity and migration. Another factor to be taken into account is that, because of capillary forces at the pore-scale, the pressures in the two fluids are different, the difference being known as capillary pressure (see Glossary). As an illustration, if an open straw is put into a bowl of fluid, the fluid may rise up the straw due to capillary pressure; the narrower the straw, the further the fluid will rise. Porous rock contains millions of interconnected narrow capillaries, and so capillary pressure is very important. Quantities such as relative permeability and capillary pressure are measured in laboratories. However, there is always a lack of data, since only a few samples are usually measured and, typically, reservoirs are very heterogeneous systems. The results of reservoir simulation calculations have a degree of uncertainty associated with them due to the issues that arise from this lack of data. However, to identify these uncertainties and minimise their impact, multiple realisations may be run that test the dependency on the input assumptions, as discussed in Chapter 6.
Well information
In a reservoir simulation model, we can specify the location of wells and how they are connected to the grid cells. We can also specify the injection rate (m3/day) and a maximum allowed bottom hole pressure – which will be determined by the requirement not to fracture the rock.
CASSEM
4.3 WORKFLOW AND TOOLS Workflow design
As with the other aspects of an aquifer site evaluation, there are different stages at which decisions need to be made, and dynamic flow modelling may be used to contribute data to this decision-making process. Typically, as the project plans develop, more data becomes available, and hence more complex modelling activities may be undertaken. Some decisions around suitability of sites will be taken before any modelling work begins: it will be evident from available data whether sites may or may not be possible storage locations just by considering basic volumetric and geographical information, without conducting any flow simulations. This type of decision making at the start of the process is described in Chapter 3.
Once a candidate or candidate sites have been identified, we define three stages of flow modelling, which we term ‘phases’. The basic idea is to start with a very simple model at minimal cost (in terms of data, time and money), and then move through in stages to more complex levels of modelling, using more complex simulation tools and techniques, and acquiring more site-specific data to better constrain the modelling. The objective is that by the third stage a detailed model will be available that may be used to provide reliable input for the risking process, as described in Chapter 6.
The Phase 1 modelling considers primarily volumetric and simple rock property data. This will give an indication of whether adequate storage volume exists, generally assuming the rock type is suitable.
We then proceed to an intermediate phase, which includes the Level 1 geological model (Chapter 3).
This second phase simulation activity will include more complex geomechanical and geochemical processes, although site-specific data may not yet be ready to provide as input, and so values from analogue sites may be required at this stage. The second phase simulation will tell us whether there may be sufficient injectivity and what the likely migration path of the CO2 would be. This calculation can then be compared with the migration path identified using MPath, which is conducted on a much finer resolution model, but which does not account for the effect of dissolution.
This is then followed by a third and final phase of modelling, which includes laboratory data derived from site-specific samples (from tests conducted concurrently with the second phase of reservoir flow calculations), and incorporates the most advanced and final geological model (developed during the third level of geological model building). From this third phase of modelling we identify the impact of site-specific data, such as relative permeability, mechanical rock strength and mineralogy, and brine composition. Combined, these factors are used to calculate storage capacity for the site.
These models should be used as the most accurate input for the uncertainty analysis (as described in Chapter 6).
Figure 4.2 summarises this approach of using three phases of simulation. The results of the three stages may then be compared to determine the level of detail required and the cost effectiveness of each activity. When following through the process, there is a stage gate after each phase of activity, in which a decision is taken whether to invest in moving to the next stage, or to put the project on hold, or indeed stop it.
E/DG1
Figure 4.2 The three phases of modelling used for reservoir simulation.
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Examples of how this three-stage approach works in practice will now be presented, with specific reference to the two project sites introduced in Chapter 1, in Lincolnshire and under the Firth of Forth.
Not only did the two sites have differing geographical and geological settings, there were also substantial differences in the data available: from the outset there was greater resolution of geological information for the Lincolnshire site and rock core samples from wells were available, whereas seismic data were much more limited for the Firth of Forth Site and only outcrop rock samples were available, no well having been drilled into the target formation. The impact of this data availability on the experimental work and reservoir simulation activity will be apparent throughout the rest of this chapter.
Workflow tools
As is the case for geological model building, there is a wide range of commercial software tools available to perform dynamic flow simulations. Some operating companies also use their own in-house software. The preliminary step is to discretise the geological model, dividing the system into discrete grid blocks, each being assigned petrophysical properties such as permeability and porosity, derived from the geological model. Petrel (Schlumberger) was used to discretise the models for all three phases of dynamic simulation.
The flow calculations were performed using the compositional simulator ECLIPSE 300 (Schlumberger) with the CO2STORE option for CO2 storage in saline aquifers. This enables calculation of the injection of CO2, its displacement through the rock away from the well, its buoyant rise and trapping under the cap rock, residual trapping and the dissolution of the CO2 in brine. The dissolution calculation uses the Spycher and Pruess model for CO2 solubility in brine, which may be applied to chloride/brine mixtures in the temperature range 12–100oC and up to 600 bar pressure (Spycher and Pruess, 2005). The calculated solubilities tend to give a close match with the best experimentally derived solubility data, such as by Duan and Sun (2003), as reported in Gundogan et al. (2010).
Geomechanical modelling was carried out using VISAGE (Schlumberger). This is an integrated package of pre- and post-processing programs developed for coupled reservoir simulation. It is primarily designed for use in the oil industry with the black oil ECLIPSE 100 simulator, but was used here with the CO2STORE module of ECLIPSE 300. VISAGE Modeller is used to condition the ECLIPSE flow model by setting up suitable mechanical embedding with appropriate boundary conditions, initial stress conditions and geomechanical properties. Geochemical modelling was carried out using GEM-GHG (Computer Modelling Group), which models the free CO2 phase with an equation of state, and the CO2 solubility in the brine phase using Henry’s law, corrected to take account of pressure and salinity effects. GEM has an internal database of equilibrium constants for the reactions involving chemical species in the aqueous phase and the primary and secondary minerals. Activity corrections are based on the B-dot model.
Data requirements for simulation
The framework for the geological models was supplied by BGS (see Chapter 3) and the rock properties for the grid cells were based on data from boreholes, where available. The properties for individual fluids (pure CO2 and brine) were already coded into the simulation software. Some relative permeability and capillary pressure data were taken from the literature (Bennion and Bachu, 2005). Before the CASSEM project started, it was recognised that there is a general lack of data for simulating CO2 storage in general, and with respect to the two specific sites in particular, and it was decided to include laboratory measurements within the scope of the project. Four types of measurements were made:
• Petrophysical properties: to determine permeability and porosity of supplied core plugs.
• Rock mechanics: to determine impact of stress changes induced by pressure changes arising when CO2 is injected.
• Geochemistry: to determine impact of disturbing the chemical equilibrium that existed before CO2 injection began, and impact of changes in brine composition on CO2 interactions.
• Relative permeability: to determine impact of rock-fluid behaviour on CO2 migration and retention.