optimizing horizontal completion/frac design with data driven engineering and modeling
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Optimizing Horizontal Completion/Frac Design with Data Driven Engineering
and Modeling
Bob Shelley, P. E. StrataGen Engineering
Challenges of Fractured Horizontal
Completions
Pay zone may not be completely penetrated.
Open hole logging is expensive and takes time.
Need to increase efficiency to reduce cost.
Need for optimization when permeability is unknown.
Frac cost exceeds 50% of well cost.
Complex completion methodology.
Minimal opportunity to run production logs.
Bakken Production
Bakken Development
Timeline
First Horizontal Well
Horizontal Drilling
Parshall Area Development
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16000
Year
Aver
age
Bes
t Mon
th O
il B
BL
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Wel
l Cou
nt
Average of Best Month Oil Total Well Count Horizontal Well Count
Highly Compartmentalized Fracturing, 20 + Fracs
Elm Coulee Development
Two Very Different Modeling Processes
Discrete Data Driven Make Assumptions
Apply Engineering Principles
Develop Well Model
Evaluate Well Opportunities
Gather & Integrate Data
Develop Many Models
Use Process Knowledge to Select Best Model
Evaluate Opportunities
Bakken Data Analysis
Bakken Numeric Simulator Reservoir
Modeling, SPE 133985
Vertical Vertical Fraced Horizontal
Horizontal Axial Frac Horizontal 5
Transv. Fracs
Horizontal 11 Transv. Fracs Incr
Lf
5
0.5
0.05
0.005
0%
5%
10%
15%
20%
25%
30%
Recovery at 10 Years (Cum/OOIP)
Well Type
Permeability md
Relationship between Mud Log Gas Shows &
Post-Frac Production for Wells w/Similar
Compl. Approach SPE 133985
Scatter Plot
I H S
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Color by
Completion cat 2
No frac
Avg TG
Bes
t Mon
th O
il C
um B
BL
Bakken Data Driven Model
R² = 0.8919
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D_Peak OilR² = 0.9427
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D_EUR BBL
Predictors DescriptionMud Weight lb/gFraction_C1 FractionFraction_C4 FractionAverage TG Gas units, 100 units = 1% EMALateral Length MD ftNo of Fracture Treatments CountFrac Staging MethodologyPerforated Length MD ftTreatment Fluid TypeTreatment Volume GalTotal Proppant Weight lbsAverage Proppant Conductivity Avg Prop Conductivity at Closure (md-ft)OutputsPeak Oil Best Month Cumulative Oil BOEstimated Oil Recovery EUR BO w/ Qa=4 BOPDWOR Fraction
Predictor Significance
Ranking
Controllable Completion and Frac ParametersNon-Controllable Reservoir Related Parameters
Parameter Influence on Peak Oil Influence on Oil Recovery
Gas Index 2 24.12% 17.70%No of Fracture Treatment 14.45% 13.25%
Reservoir Index 1 6.13% 6.82%Proppant 3.86% 5.22%
Gas Index 1 -3.04% -3.50%Staging Method & Perforating 3.92% 1.73%
Treatment Type 2.31% 3.19%Lateral Length 3.15% 1.05%
Treatment Volume 2.49% 2.03%Reservoir Index 2 0.42% 0.48%
Truax Area New Completion A
• Well and Reservoir – 7” shoe – 11456 ft, TD – 19100 ft, Length – 7644 ft– Mud Type – Saltwater, 9.56 lb/g, vis 29.– Upper Bakken TVD – 11,323 ft.– ROP; Avg – 0.92 min/ft, Median – 0.62 min/ft.– TG; Avg – 531, Median – 513. – Alkane Fraction; C1- 0.31, C2- 0.23, C3 – 0.1, C4 – 0.36 – GR; Avg – 87, Median – 86.
• Completion and Frac– 4.5” liner with 18 swell packers. Liner top 10,374 ft.– 18 frac stages; 7 stim sleeves, 11 Plug & Perf.– 1,845,000 g X-link Fluid. – 1,548,000 lb 20/40 Ceramic.– 35 to 40 BPM.
2,876
15,319
17,108
21,983
0
5,000
10,000
15,000
20,000
25,000
Model Predicted - X-Link, NoFrac Compartmentilization
Model Predicted - AsCompleted, 18 Fracs
Actual - 18 Fracs Model Predicted - 25 FracCompartments, X-Link,
Intermediate PropB
est M
onth
Oil
BB
L
Data Driven Model Predictions
530 % Increase in production for 50% increase in well cost
• Well and Reservoir– 4.5” liner, Shoe – 19,795, TOL – 10,527 – Mud Type – Saltwater, 9.5 lb/g (ST 9.9 lb/g), vis 28 (ST 31)– Upper Bakken – 11,058 ft tvd– TG; Avg – 857 (ST 1005) – Alkane Fraction; C1- 0.43, C2- 0.23, C3 – 0.18, C4 – 0.16 – GR; Avg – 90 (ST 83)– Lateral Azimuth – Northwest (345 deg )
• Completion and Frac– 26 Fracs – 18 Stim Sleeve followed by 8 Plug and Perf. – Proppant Totals - 2,975,800 lb 20/40 Ceramic.– Fluid Totals - 1,547,000 gal. x-linked gel and 909,000 gal. linear gel.
15,839
21,95420,958
0
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10,000
15,000
20,000
25,000
Model Predicted - 18 FracTreatments
Model Predicted - AsCompleted, 26 Frac
Treatments
Actual - 26 Frac TreatmentsPe
ak O
il M
BBL
Data Driven Model Predictions
Truax Area New Completion B
38 % Increase in production for 15% increase in well cost
• Well and reservoir– 7” intermediate set at – 9980 ft. Lateral TD – 17750 ft, – Mud Type – Saltwater, 9.65 lb/g, vis 29.– ROP; Avg – 0.927 min/ft, Median – 0.750 min/ft.– TG; Avg – 218, Median – 202. – Alkane Fraction; C1- 0.58, C2- 0.24, C3 – 0.09, C4 – 0.09 – GR; Avg – 92.5, Median – 93.– Lateral Azimuth – Northwest (327 Deg)
• Completion and Frac - The 4.5” liner could not be run to TD and was set at 16423 ft. TOL – 7716 ft, Cased Length – 6443 ft. – 21 Fracs – 10 Stim Sleeve, 11 P&P. – Proppant Totals - 1,411,900 lb 20/40 sand, 701,300 lb 20/40 Ceramic .– Fluid Totals - 2,130,000 gal. x-linked gel and 231,000 gal. linear gel.
5,726
11,259
6,681
0
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12,000
Actual Model Predicted - 21 FracTreatments
Model Predicted - 11 FracTreatments
Peak
Oil
MBB
L
Data Driven Modeling Predictions and Analysis
Wild Rose Area New Completion C
Well producing as if treated with less than 21 frac treatments
B
A
C
B
A
C
Comparison of Data Driven Model
Estimated vs. Actual Production
Summary The data driven approach to completion and hydraulic fracture design can compliment a factory mode of well completion operations.
Data Driven Modeling is useful for: Determine best practices Quickly estimate production for various completion
and frac methods Estimate well potential in the case of sub optimal completion/frac Economic optimization of well completion and fracs Prospect evaluation Provide direction for engineering efforts
Data driven and discrete well modeling are not exclusive. Ideally results from the two approaches should complement and support each other.