What did we learn from injecting over 300 tons of CO2?

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Field Injectivity Testing (Task 7.2):

Pressure Production Data and Modeling

Objectives

Field injectivity testing aimed to develop and understanding of field scale injectivity issues,

perform modeling studies of the pressure and production responses, and gather practical data on

infrastructure issues. The work began with the selection of two injection sites in depleted oil

wells; one in the Clinton sandstone and one in the Copper Ridge dolomite (Field Injectivity

Testing Task 7.1). Injection of CO2 was then performed, and pressure-production data analyzed

as the wells were shut in and produced.

Pressure Production Data and Modeling Approach

For the Turner-Doughty #1 well, a radial grid was employed in CMG-IMEX to build numerical

models representing the well of interest within its drainage area. The models had 10 layers in the

vertical direction, and 10 concentric rings in the radial direction with a geometric grid spacing

increasing radially outward from the wellbore. One of the models was a single porosity model,

and the other was a fracture-matrix (dual porosity) model. A trial-and-error approach was used

to adjust the model parameters (i.e., permeability, relative permeability relations) in order to

match: (a) primary production response (i.e., oil and gas rates or equivalently, the corresponding

cumulative production volumes), (b) average reservoir pressure prior to CO2 injection, and (c)

pressure buildup and falloff during the CO2 injection period.

Similarly, for the Brugger Brodzinski #1 well, a radial grid was employed in CMG-IMEX to

build numerical models representing the well of interest within its drainage area. The models

had 20 layers in the vertical direction, and 10 concentric rings in the radial direction with a

geometric grid spacing increasing radially outward from the wellbore. Starting with fluid and

reservoir properties from previous tasks, a trial-and-error approach was used to adjust the model

parameters (i.e., permeability, relative permeability relations) in order to match: (a) primary

production response (i.e., oil and gas rates or equivalently, the corresponding cumulative

volumes), (b) average reservoir pressure prior to CO2 injection, and (c) pressure buildup and

falloff during the CO2 injection period.

Results

Turner-Doughty #1 Well. Figure 1 shows the history match with the primary production data

for the Turner-Doughty #1 well. The cumulative oil is specified as a constraint (which should

be, and is, satisfied by both models). The behavior of gas and water production is shown here

for completeness, since no historical data is available.

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Figure 1. History match to cumulative oil production for the Turner-Doughty #1 well.

Figure 2 shows the predicted average pressure behavior for the Turner-Doughty #1 well during

the primary production period. Although new intermediate pressures between the discovery

pressure and the pre-injection pressures were available, both models are capable of matching the

observed pressure of ~200 psi prevailing prior to the CO2 injection.

Figure 2. History match to cumulative water production and average reservoir pressure for the Turner-

Doughty #1 well.

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Figure 3 shows the history match to the pressure buildup during CO2 injection and pressure

falloff during the shut-in (soak) period at the Turner-Doughty #1 injection well. Here, both

models generally match the pressure buildup amplitude, but neither is satisfactory in terms of

matching the final stabilization at ~600 psi.

Figure 3. History match to CO2 injection related pressure buildup and falloff in the Turner-Doughty #1 well.

Finally, Figure 4 shows the increase in oil recovery following CO2 injection over a period of 2

years for the Turner-Doughty #1 well. During this period, 1641 extra barrels are projected to be

produced after injection of 162 tons of CO2, corresponding to a utilization ratio of 10 STB/tons.

Figure 4. Actual (until July 2018) and forecasted oil production (based on CO2 injection in July 2018) for fractured

and un-fractured cases in the Turner-Doughty #1 well.

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Brugger Brodzinski #1 Well. Figure 5 shows the history match with the primary production

data for the Brugger Brodzinski #1 well. The cumulative oil (right panel) is specified as a

constraint (which should be, and is, satisfied by all three models). Thus, the history match is

primarily against the cumulative gas production (left panel). Model 3 performs somewhat better

compared to the other 2 models.

Figure 5. History match to cumulative gas and oil production in the Brugger Brodzinski #1 well.

Figure 6 (left panel) shows the history match with respect to cumulative water production for the

Brugger Brodzinski #1 well. Both models 2 and 3 appear to be acceptable. Finally, the

predicted average pressure behavior during the primary production period is shown in Figure 6

(right panel). Although new intermediate pressures between the discovery pressure and the pre-

injection pressures were available, all three models are capable of matching the observed

pressure of ~300 psi prevailing prior to the CO2 injection.

Figure 6. History match to cumulative water production and average reservoir pressure, Brugger Brodzinski #1l.

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Figure 7 shows the history match to the pressure buildup during CO2 injection as well as pressure

falloff during the shut-in (soak) period at Brugger Brodzinski #1 injection well. Here, model 3 is

clearly capable of better representing the observed pressure stabilization at ~1100 psi compared

to the other 2 models.

Figure 7. History match to CO2 injection related pressure buildup and falloff in the Brugger Brodzinski #1 well.

Finally, Figure 8 shows the increase in oil recovery following CO2 injection over a period of 2

years. During this period, 477 extra barrels are projected to be produced after injection of 159

tons of CO2, corresponding to a utilization ratio of 3 STB/tons.

Figure 8. Actual and forecasted oil production (based on July 2018 CO2 injection), Brugger Brodzinski #1 well.

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Significance

The significance of this work includes the following:

• Analysis of the bottom hole pressure data was useful in determining the injectivity index,

a proxy for permeability at the field-scale. The injectivity index for Copper Ridge

dolomite was found to be approximately two times higher than that for the Clinton

sandstone, which is consistent with results from reservoir characterization (Task 2).

• The pre-test production data and the bottom-hole pressure data from the CO2 injection

test were used to calibrate reservoir models of the region surrounding both wells. The

models were reasonably calibrated by adjusting parameters such as absolute permeability

and relative permeability relationships. The absolute permeability values were compared

those obtained from injectivity-index correlations and were found to agree well. The

relative permeability relationships were compared to those obtained from laboratory

measurements on small-scale core samples (Task 3 Laboratory Testing) and were also

found to generally agree well.

• Modeling results from the pressure and production response suggest that the yield in the

Morrow well will be on the order of 10 stock tank barrels (STB) of oil per ton of CO2

injected, and 3 STB per ton of CO2 for the East Canton well.

For more information, refer to: "CO2 Utilization for Enhanced Oil Recovery and Geologic

Storage in Ohio, Task 7: Field Injectivity Testing Topical Report.," OCDO Grant/Agreement

OER-CDO-D-15-08, Columbus, 2018.

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