1. GMS Geostatistical Realizations CMG-STARS Results
Modeling Oil Recovery in the Late Devonian Grosmont and Upper Ireton Formations of Northeastern Alberta, Canada
1James, S. C., 1Jiang, J., 1Atchley, S., 1Harlow, H., 1Leslie, C., 2Carrelli, G., and 2Bindon, W. (1Baylor University, 2GLJ Consultants)
Abstract
State-of-the-art oil recovery methods in Alberta’s bitumen reserves extract no more than 10% of the available resource with the vast majority left in the ground and recoverable only through optimized techniques. Enhanced oil recovery methods (e.g., cyclic steam stimulation, steam-assisted gravity drainage, and steam flooding) have not proven economically attractive for bitumen [1]; however, process optimization may remediate against considerable energy and efficiency losses. By controlling rates of cyclic steam and solvent injection while optimizing well spacing and control (e.g., horizontal well distances and judicious use of infill wells) as functions of reservoir properties and characteristics, additional bitumen production for the same capital and operating costs is certainly possible. However, a validated model built upon a strong reservoir characterization program is a necessary means to this end. For example, in addition to simulating heat addition through steam, a model can reveal the degree to which co-injection of CO2 solvent may reduce oil viscosity and swell the oil. Based upon the work of Atchley et al. (this conference, Stratigraphic and reservoir quality assessment: Late Devonian Grosmont and Upper Ireton, Northeastern Alberta), the Groundwater Modeling System (GMS) was used to generate equally probable stochastic reservoir realizations and to visualize reservoir properties. One realization was selected to demonstrate a five-spot steam flooding model of thermal reservoir flow using CMG-STARS. This proof-of-concept model combines data from various sources to demonstrate how to optimize the system with regard to producer well placement to maximize bitumen recovery. Additional complexity can easily be added to the model as more data become available.
GMS Geostatistical Realizations
CMG-STARS Results
Realization 1
P (kPa)
T (°C)
S (−)
Note unconformity surface
CMG-STARS results showing horizontal slices of pressure (kPa), temperature (°C), and oil saturation after 1 year.
P (kPa)
T (°C)
S (−)
Realization 2
Realization 3
CMG-STARS results showing horizontal slices of pressure (kPa), temperature (°C), and oil saturation after 20 years.
Geology
Nearly 4 km of cores were analyzed to identify with depth:
Facies type (9 identified, see Atchley et al.)
Porosities
Permeabilities
Vertical to horizontal permeability ratios
P (kPa)
T (°C)
S (−)
CMG-STARS Model and Data Inputs
Model Parameters
The grid is 600×600×35 m3 with 5×5×0.23 m3 resolution
Model top and bottom are established from borehole data
Porosities, f, and permeabilities, K, used are listed in Table 1
Reference depth: 350 m
Reference pressure: 1,555 kPa
Reservoir temperature: 11°C
Porosity reference pressure: 1,000 kPa
Formation compressibility: 3.03×10−4 kPa−1
Oil molecular weight: 550 daltons
Oil viscosity: 7° API
Thermal properties (from G. Carrelli):
Confining-unit volumetric heat capacity:2.7×106 J/(m3·°C)
Confining-unit thermal conductivity: ×105 J/(m·day·°C)
Reservoir volumetric heat capacity: ×106 J/(m3·°C)
Reservoir-rock thermal conductivity: ×105 J/(m·day·°C)
Water-phase thermal conductivity: ×104 J/(m·day·°C)
Oil-phase thermal conductivity: ×104 J/(m·day·°C)
Facies
f (−)
Kh (milliDarcies)
Kv/Kh (−) G1
0.03 3
0.12
G1 – karsted
0.34
240
0.10 G6
0.15 13
0.21
G6 – karsted
0.25 19
0.62
G2 & G3
0.11 70
0.19
CMG-STARS results showing vertical slices of pressure (kPa), temperature (°C), and oil saturation after 1 year.
Oil Production and Optimization
Well Constraints
Single injector
Specified bottom hole pressure: 5 MPa
Specified surface water rate: 250 m3/day
Injection fluid: H2O (steam)
Injection fluid temperature: 300°C
Steam quality: 0.95
Four producers
Minimum bottom hole pressure: 200 kPa
Oil production as a function of water injection (bottom axis) and enthalpy added (top axis). This shows the oil recovery over 20 years upon injection of 250 m3/day of 95% quality steam at 5 MPa and 300°C.
Cumulative steam-oil ratio (SOR) for 10 years as a function of distance between the injector and producers. The inset shows the oil produced (left axis) and oil produced per MJ of enthalpy added (right axis) as a function of well distance.
Relative permeabilities [2].
Temperature-dependent viscosity from G. Carrelli.
Geostatistical Modeling
GMS’s GSLIB T-PROGS capabilities were used.
Because T-PROGS is limited to 5 materials in GMS, the 9 facies identified were reduced to 5 (101 boreholes comprising 1,623 data points):
G1, both karsted & unkarsted
G6, both karsted & unkarsted
G2 & G3 (includes G5 and I1)
These data are used to condition geostatistical simulations to represent facies distributions.
Random-function based statistical models quantify the uncertainty associated with spatial estimation and simulation.
Geological characteristics are assigned at unsampled locations as a set of correlated random variable derived from field data.
Future Work
This work is a proof-of-concept demonstrating that steam injection EOR can be numerically optimized for Grosmont bitumen.
Only a single realization has been presented. A full stochastic analysis is required.
While the Grosmont geological data were used to develop the porosity and permeability fields, other site-specific data should be built into the model.
The model is highly sensitive to relative permeabilities and data specific to the reservoir must be acquired.
Site-specific thermo-hydrologic parameters would be useful. Specifically, thermal conductivities, heat capacities, and oil saturations for each facies are necessary model inputs.
Data for bitumen are required, in particular, density and viscosity as functions of temperature (and pressure).
Various steam-injection strategies may be investigated to optimize oil production and other well controls can be added as needed.
[1] Q. Song, Z. Chen, and S.M. Farouq Ali, Steam injection schemes for bitumen recovery from the Grosmont carbonate deposits, Society of Petroleum Engineers Canada Heavy Oil Conference, Calgary, Alberta, SPE MS, June 9-11, 2015.
[2] M.R. Tamer and I.D. Gates, Impact of Different SAGD Well Configurations (Dover SAGD Phase B Case Study), Journal of Canadian Petroleum Technology, 51(1),
f (−)
Kv (md)
CMG-STARS grid showing porosities, f.
CMG-STARS grid showing vertical permeabilities, Kv (mD).