Are fractures present in the reservoirs? Reservoir ... · sandstone and five wells producing from the Copper Ridge dolomite have both fracture and 1 Bhattacharya, S. Mishra, S. 2018

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Are fractures present in the reservoirs?

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Reservoir Characterization (Task 2.2):

Fracture Mapping of the Clinton sandstone and Copper Ridge dolomite

Objectives

The goal of this subtask is to investigate natural fractures in two formations of interest: (a)

Clinton sandstone near the East Canton Consolidated (ECOF) and Gore Consolidated (GCOF)

Oilfields in eastern Ohio, and (b) Copper Ridge dolomite near the Morrow Consolidated Oilfield

(MCOF) in central Ohio. Natural fractures play a potentially important role in the production of

oil and gas and the storage of CO2 in a geologic reservoir. An understanding of natural fractures

is also crucial when conducting CO2-Enhanced Oil Recovery (CO2-EOR). The results from this

task ultimately supported modeling of reservoirs with natural fractures (Task 4.2) and field

injectivity testing (Task 7.0) in subsequent tasks.

Study Area and Data Sources

logs (i.e., image logs and fracture identification logs) obtained from the Ohio Department of

Natural Resources (ODNR), Division of the Geological Survey. A simplified spreadsheet was

created to capture information on a foot-by-foot basis. Fractures were identified on a foot-by-foot

basis, and a spreadsheet identifying fracture presence, orientation, and whether the fracture was

filled was created using the codes listed in Table 1.

Table 1. Codes used to identify fracture presence, confidence in picks, fracture orientation, and fracture filling.

Presence Confidence Orientation Frac filled

Code Meaning Code Meaning Code Meaning Code Meaning

1 Present 1 Confident 1 Vertical (65-90) 1 Yes

0 Not Present 0 Possible 0.5 Subvertical (25-65) 0.5 Partial

-1 No Data -1 No Data 0 Horizontal (0-25) 0 No

-0.5 Uncertain -0.5 Uncertain

-1 No Data -1 No Data

Once the fracture database was generated, it was used to complete the following work:

• Fracture density maps and cross-sections were created to show the spatial and vertical

distribution of fractures in the formations of interest.

• The prevalence of fractures was compared to oil production in the formations of interest.

• A machine learning process was used to identify presence of fractures using commonly

acquired wireline logs.

Reservoir Characterization (Task 2.2): Fracture Mapping of the Clinton sandstone and Copper Ridge dolomite

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Fracture Mapping Results

The fracture density map of the Clinton sandstone is shown in Figure 1. In addition, the fractured

columns and log signatures are shown in the southwest-to-northeast and northwest-to-southeast

trending cross-sections in Figure 2. In general, the wells in the southwestern portion of the study

area, near the Gore Consolidated Oilfield (GCOF) are less fractured than those in the central and

eastern portions of the study area, particularly near the East Canton Consolidated Oilfield

(ECOF). The boreholes in the Clinton sandstone in the western part of the study area contain

relatively thin and isolated zones of fractures. The boreholes in the eastern part of the study area,

however, have more complex fracture systems.

The fracture density map of the Copper Ridge dolomite is shown in Figure 3. In addition, the

fractured columns and log signatures are shown in the west-to-east and north-to-south trending

cross-sections in Figure 4. There is a high prevalence of fractures in the Copper Ridge dolomite

at most of the wells near the Morrow Consolidated Oilfield (MCOF). The percentage of the

interval that is fractured in each well is variable and appears to be somewhat random, ranging

from less than 5% of the interval with data to more than 40% of the interval with data within a

few miles.

Table 2. Total footage of fractured intervals, non-fractured intervals, and intervals without data for the Clinton

sandstone in eastern Ohio and the Copper Ridge dolomite near the MCOF in central Ohio. Fractured intervals are

also indicated as open, partially open, closed, or not indicated as closed or open.

Formation

Fractured Not

Fractured

Total with Data

No Data Open

Partially Open

Closed Not

Indicated ALL

Clinton 29 12 43 30 114 1357 1471 246

Copper Ridge 71 10 67 177 325 1751 2076 2069

The area around the ECOF, in the northeastern corner of the study area, has the largest

concentration of high-producing wells, and is the area with the highest prevalence of fractures

indicated within the study area. The area near the GCOF, in the southwestern corner of the study

area, does not have as large of a concentration of high producing wells. Fewer wells with high oil

production may be because the field was discovered in the early 20th Century, meaning the

highest producing wells may not be captured in the database. The cumulative oil production of

the major oilfields producing from the Clinton sandstone provide further evidence to this point;

the GCOF has the second highest cumulative oil production of the major oilfields producing

from the Clinton sandstone in Ohio, second only to the ECOF.

The oil production is highly variable across the oilfield, possibly because production in the

MCOF is from disconnected erosional remnants. While most wells in the dataset have some

amount of fracturing, the wells with high density of fractures in the northwestern portion of the

field are near areas with some of the highest production in the MCOF. In addition, the four wells

without fractures near the field are in areas with low production.


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Additional areas with high producing wells are found in the southern and eastern portions of the

MCOF. These discrete areas are separated by relatively unproductive areas.

Figure 1. Clinton sandstone fracture density map.


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Figure 2A. Cross-section A-A’, which covers a horizontal distance of 178 miles (vertical exaggeration – 2,835x).


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Figure 2B. Cross-section B-B’, which covers a horizontal distance of 52 miles (vertical exaggeration – 830x).


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Figure 3. Copper Ridge dolomite fracture density map.


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Figure 4A. Cross-section A-A’, which covers a horizontal distance of 178 miles (vertical exaggeration – 2,835x).


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Figure 4B. Cross-section B-B’, which covers a horizontal distance of 52 miles (vertical exaggeration – 830x).


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Fracture Prediction by Machine Learning

Fractures were predicted based on common logs using machine learning. As explained in

Bhattacharya and Mishra (2018)1 the motivation is to utilize the more readily available common

log data when more detailed information such as core samples and/or advanced logs are not

available. The methodology for fracture prediction involves three steps:

1. Identify fractures from core and high-resolution sonic and image logs (this provides the

training data)

2. Train the predictive model using different machine learning algorithms

3. Test the model using either blind-well test or cross-validation techniques

After core and image-log-based fracture identification, different machine learning algorithms,

such as Bayesian Network (BN), Random Forest (RF), Support Vector Machine (SVM), and

Artificial Neural Network (ANN) are applied to learn the petrophysical data pattern associated

with the presence of fractures and predict their distribution using Delta_CALI, RHOB, and GR

logs. All the models are cross-validated ten-fold, to assess quality of the results. Numerous

experiments were designed to investigate the optimal parameters for each of the techniques. Six

wells from the Clinton sandstone and ten wells from the Copper Ridge dolomite contained

commonly available logs, such as caliper (CALI), gamma (GR), and bulk density (RHOB).

Other common logs, such as resistivity and neutron porosity were not available in all wells, so

they were not used.

Application of BN, RF, SVM, and ANN with optimal parameters produced different results. The

BN method was found to be the best classifier for the Clinton Formation (accuracy 82%),

whereas the RF method was the best classifier for the Copper Ridge Formation (88%), followed

by SVM and ANN (Figure 5). This may be due to different data patterns in two geologically

different formations, as one of them is sandstone and the other one is carbonate. The BN method

shows accuracy of 82% and 87% for fracture prediction for the Clinton and Copper Ridge

formations, respectively. In addition, BN shows causality of input-output relationships in the

form of a Directed Acyclic Graph (DAG) (Figure 6). It shows that Delta_CALI and RHOB logs

are directly connected via a DAG, which indicates Delta_CALI and RHOB logs are strongly

interrelated for fracture prediction. This observation makes geologic sense, because Delta_CALI

and RHOB logs are highly sensitive to hole size, compared to the GR log. Any change in hole

size (due to either fractures or porous formations) will cause change in Delta_CALI and RHOB

log signatures.

The correlation between the presence of fractures and hydrocarbon production was investigated

using a limited number of wells with the required data. Ten wells producing from the Clinton

sandstone and five wells producing from the Copper Ridge dolomite have both fracture and

1 Bhattacharya, S. Mishra, S. 2018. Applications of machine learning for facies and fracture prediction using Bayesian

Network Theory and Random Forest: Case studies from the Appalachian basin, USA. Journal of Petroleum Science and Engineering 170, pp. 1005-1017.


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production data. The number of fractures within the formation of interest was calculated from

each well. Cumulative hydrocarbon production (through 2014) for each formation was plotted

against the footage of fractures for each well (Figure 7).

No direct relation between total number of fractures and hydrocarbon production was found. The

high hydrocarbon production found in wells with less fracturing may be attributed to other

geologic and petrophysical parameters.

Figure 5. Accuracy of fracture prediction using different machine learning algorithms for the Clinton sandstone and

Copper Ridge carbonate formation.

Figure 6. Bayesian Network-derived input-output relationships, indicating that fractures are dependent on the

caliper log deltas, bulk density, and gamma ray logs, bulk density is dependent on the caliper log deltas, and gamma

ray is dependent on the bulk density.

BN RF SVM ANN

Clinton 82 79 76 74

Copper Ridge 87 88 86 86

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Figure 7. Scatter plot between total number of fractures and cumulative hydrocarbon production for the Clinton

sandstone (left) and Copper Ridge dolomite (right).

Significance

The significance of the work includes the following:

• In general, fractures in the Clinton sandstone were more prevalent in the eastern part of

the study area where the formation was found at depths between 4,000 and 6,000 feet

below ground surface; however, the density of fractures may be attributable to known

faults and structures in these areas rather than depth. Oil production from the Clinton

sandstone was highest near the ECOF, the area with the highest density of fractures.

• Fractures in the Copper Ridge dolomite near the MCOF in central Ohio do not follow as

discernible of a pattern as those in the Clinton sandstone. The total thickness of fractured

intervals in each well was highly variable throughout the field. Most wells (all but four)

in the dataset had at least some fractures. The wells with the highest density of fractures

were found in the northwestern portion of the MCOF where oil production was also high.

• The machine learning process developed to determine if basic wireline logs could be used

to predict fractures proved successful. One method, Bayesian Network, predicted the

fracture presence/absence at a rate of 82% for the Clinton sandstone and 87% for the

Copper Ridge dolomite. Additional data could help to refine and increase the accuracy of

the method; however, the accuracy of the current model is a good initial step. The

investigation into the relationship between fracture density and hydrocarbon production

did not show a strong correlation. This may be because only a few wells (eight producing

from the Clinton sandstone and five producing from the Copper Ridge dolomite) could be

included in the analysis because the necessary data (annual hydrocarbon production data

and fracture identification data) were relatively rare.

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

Geologic Storage in Ohio, Task 2: Reservoir Characterization Topical Report.," OCDO

Grant/Agreement OER-CDO-D-15-08, Columbus, 2017.

Documents

Are fractures present in the reservoirs? Reservoir ... · sandstone and five wells producing from the Copper Ridge dolomite have both fracture and 1 Bhattacharya, S. Mishra, S. 2018