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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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

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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

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Completion cat 2

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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

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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

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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

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Model Predicted - 18 FracTreatments

Model Predicted - AsCompleted, 26 Frac

Treatments

Actual - 26 Frac TreatmentsPe

ak O

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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.

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Actual Model Predicted - 21 FracTreatments

Model Predicted - 11 FracTreatments

Peak

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Data Driven Modeling Predictions and Analysis

Wild Rose Area New Completion C

Well producing as if treated with less than 21 frac treatments

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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.