The SPE Foundation through member donations and a contribution from Offshore Europe

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1 Primary funding is provided by The SPE Foundation through member donations and a contribution from Offshore Europe The Society is grateful to those companies that allow their professionals to serve as lecturers Additional support provided by AIME Society of Petroleum Engineers Distinguished Lecturer Program

2 Optimization of Multi-Fractured Horizontal Completions; A New Industry Challenge Robert F. Shelley, P.E Society of Petroleum Engineers Distinguished Lecturer Program

3 Multi-Fractured Horizontal Completions Complex well type. Expensive, HF 50% total well cost. Recovery is dependent on hydraulic fracturing. Justifying the appropriate level of investment in completion/frac is problematic. 3

4 Cumulative Oil Production BBL Completion/Frac Design Optimization Requires a Predictive Model Frac Modeling Well Architecture Reservoir Modeling Forecast Production Producing Time Days 4

5 Reservoir and Completion Modeling Bakken Fm. SPE % 25% 20% Oil Recovery (Cum/OOIP) 15% 10% 5% 5 0% Vertical Vertical Fraced Well Type Horizontal Horizontal Axial Frac Horizontal Trans.Frac 500 ft. Horizontal Trans.Frac 200 ft Permeability md 5

6 Data Requirements Modeling Data Requirements Single Well (Engineering) Neural Multiple Regression Multivariate Analysis Cluster Analysis Predictive Model 1s 10s 100s 1000s Data Quantity (# Wells) 6

7 2007 Bakken Production 500 BOPD 30 BOPD 7

8 Actual Best Month Oil (BBL) Actual (BBL) Bakken Neural Network Modeling (ANN) 40 Wells 29 Train, 11 Test Geology & Drilling Computers & Geosciences 26 (2000) ANN Modeling Techniques (ANN) Model Selection Model Validation Completion & Frac Predictive Bakken Model 30,000 Best Month Oil Cumulative 700,000 Oil Recovery 25, ,000 20, , ,000 15, ,000 10, ,000 5, , ,000 10,000 15,000 20,000 25,000 30,000 Model Predicted Best Month Oil (BBL) , , , , , , ,000 Model Predicted (BBL) 11

9 Bakken Predictive Model (ANN) SPE Parameter Influence on Best Month Oil Influence on Oil Recovery Butane 24.12% 17.70% No of Fracture Treatment 14.45% 13.25% Total Gas 6.13% 6.82% Proppant 3.86% 5.22% Methane -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% Drilling Mud Weight 0.42% 0.48% Controllable Completion and Frac Parameters Non-Controllable Reservoir Related Parameters 12

10 Best Month Avg Rate BOPD Evaluation of a 2011 Bakken Completion $70/BBL net Oil, Dunn County ND Model Validation Error 7% EUR 409 MBO WC - $6.3 MM NPV - $9.1 MM PO 19.1 Mo. $1.2 MM More Well Cost $6.2 MM More Value $0.5 MM Less Well Cost $1.5 MM Less Value EUR 359 MBO WC - $5.8 MM NPV $7.6 MM PO 20.6 Mo. EUR 605 MBO WC - $7.5 MM NPV - $15.3 MM PO 14.8 Mo Actual Production 20 Frac Stages Ceramic Prop Model Estimated As Completed Model Estimated 20 Frac Stages Sand Prop Model Estimated 30 Frac Stages Ceramic Prop 13

11 Best Month Avg Rate BOPD New Economic Reality $40/BBL net Oil, 30% Reduction in Well Cost $1.1 MM More Well Cost $3.9 MM More Value WC - $4.4 MM EUR 409 MBO NPV - $3.6 MM ROI 81% PO 28 Mo. WC - $4.1 MM EUR 359 MBO NPV - $2.8 MM ROI 68% PO 31 Mo. WC - $4.3 MM EUR 469 MBO NPV $4.8 MM ROI 110% PO 23 Mo. WC - $5.2 MM EUR 605 MBO NPV - $6.7 MM ROI 128% PO 21 Mo Frac Stages Ceramic 20 Frac Stages Sand 30 Frac Stages Sand 30 Frac Stages Ceramic 15

12 Eagle Ford Neural Network Modeling (ANN) 54 Wells 39 Train, 9 Test, 6 Validation 16

13 Eagle Ford Predictive Model (ANN) SPE Model Predictor Influence on BOE Productivity +10% Influence on Oil Productivity +10% C4+C5 Fraction 14.2% 19.4% Eagleford Top SSTVD (ft) 12.9% 2.9% Average Gamma Ray 7.4% 6.8% Total Treatment Vol (gal) -6.2% -10.6% No_of_Fracture_Treatment 5.1% 8.9% Proppant Conductivity and Amount 6.0% 4.5% Perforating 5.8% 7.7% Average Treatment Rate (BPM) 5.3% 6.3% Average Normalized Total Gas -0.4% -3.5% Frac Fluid Viscosity -4.1% -5.8% Completion Length (ft) 2.3% 2.5% Controllable Completion and Frac Parameters Non-Controllable Reservoir Related Parameters 17

14 30 Day Porductivity BOE/psi Optimization of an Eagle Ford Completion Karnes County TX Model Validation Error 2% 15% 10% Actual, 18 Fracs, Ceramic-Sand Model Estimated, 18 fracs, Ceramic- Sand Model Estimated, 18 Fracs, All Sand Model Estimated, 24 Fracs, All Ceramic Fluid (bbl) 68,300 68,300 68,300 68,300 Proppant (lb) 4,650,000 4,650,000 4,650,000 4,650,000 BOE/psi

15 Cumulative Production (BOE) Production Comparison to Offset Well Karnes County TX 200, , , , , ,000 80,000 60,000 40,000 20, Fracs, 4.65 Mlb Ceramic-Sand Offset Well, 18 Fracs, 5.33 Mlb Sand Production Differential - 20% Time (Months) 19

16 Marcellus Neural Network Modeling (ANN) 48 Wells - 34 Train, 14 Test 20

17 Marcellus Predictive Model (ANN) SPE Parameters\Outputs Peak Gas +10% First 30 days Gas +10% Top Marcellus (TVD ft) 7.4% 7.1% No of Frac Stages 4.8% 5.0% Upper Marcellus Thickness (ft) 3.5% 3.6% Avg TG 2.7% 2.5% Avg GR -2.7% -3.1% Fraction C1 2.3% 3.0% Proppant Mass (lb) 2.1% 2.5% Net Perforated Length (ft) 1.7% 1.9% Fluid Volume (bbl) 0.9% 1.2% Average Rate (BPM) -0.2% -0.1% Controllable Completion and Frac Parameter Non-Controllable Reservoir Related Parameter 21

18 First 180 Day Gas Cum (MCF) Evaluation of a Marcellus Completion Susquehanna County PA 3,000,000 2,500,000 Model Error 12% 2,000,000 1,500,000 1,000, ,000 0 Model Predicted 10 Frac Stages Model Predicted 25 Frac Stages Model Predicted 30 Frac Stages Actual Production 25 Frac Stages Best Month Gas (MCF) 196, , , ,850 First 180 Day Gas (MCF) 960,248 2,147,617 2,182,532 2,434,957 Fluid Volume (BBL) 116, , , ,140 Proppant (lb) 6,118,000 12,035,000 12,035,000 12,035,000 22

19 Summary and Conclusions Optimization of Multi-Fractured Horizontal Completion Design Requires some type of Predictive Model Use Well Specific Reservoir Related Characteristics Horizontal Well Bores Present Formation Evaluation Challenges Generally there is Limited Well bore Specific Data for Completion and Frac Optimization Readily Available Measurements Made during Drilling Operations are Useful There can be Significant Reservoir Variability over Short Distances Parameters Related to Contact and Conductivity are the Primary Controllable Drivers that Affect Production Fracture Spacing, Staging and Perf Clustering Proppant Type and Amount Fluid Selection and Volume Horizontal Drilling and Perforating Targets 23

20 Thank You Спасибо Gracias Merci 谢谢你 Grazie Takk شكرا جزيال Robert F (Bob) Shelley, P. E. Director StrataGen Sağol ありがとう Obrigado 24

21 Your Feedback is Important Enter your section in the DL Evaluation Contest by completing the evaluation form for this presentation Visit SPE.org/dl Society of Petroleum Engineers Distinguished Lecturer Program 25