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IMPROVED RESERVOIR ACCESS THROUGH REFRACTURE
TREATMENTS IN TIGHT GAS SANDS AND GAS SHALES
Mukul M. Sharma
The University of Texas at AustinPetroleum and Geosystems
Engineering
April 15, 2009
OutlineProject Objective and GoalsTiming, Project Participants, Major MilestonesValue of the Research – Project ImpactTechnical Overview – Status of Current Technology Project DeliverablesProgress to DateTechnical Issues/Problems EncounteredSummarize
Project Objectives and GoalsUse stress reorientation models to quantify the role played by stress reorientation on refrac productivity improvement.Calibrate the findings with analysis of extensive field dataImprove our ability to predict refracproduction enhancement
Candidate well selectionTiming of refracs
Improve refrac design based on findings.
Project ParticipantsUniversity of Texas at Austin
ContactMukul M. Sharma
Professor of Petroleum & Geosystems Engineering
Noble EnergyContact
Michael ZollCompletions Manager
Denver, CO
BJ ServicesContact
Satya GuptaSenior Research Leader
Tomball Technology Center
Pinnacle TechnologiesContact
Steve WolhartRegion Manager
Anadarko Petroleum Corp.Contact
Jon David CaronProject Engineering Advisor
1 Research Management Plan2 Technology Status Assessment3 Data compilation for the Codell formation 4 Data compilation for the Barnett shale5 Stress reorientation model implementation and runs for Codell re-fracs6 Stress reorientation model implementation and runs for Barnett shale re-fracs7 Evaluation of fractured well performance in the Codell, Barnett and horizontal
wells8 Candidate well selection based on poro-elastic model and field data analysis9 Design of re-frac treatments in the Codell, and Barnett based on simulations, new
fluids and proppants10 Design of re-frac treatments in horizontal wells based on simulations, new fluids
and proppants11 Implementation of re-frac treatments in the Codell, and Barnett (new designs).12 Post frac evaluation of re-frac treatments in the Codell, Barnett and horizontal
wells13 Workshop in Houston to discuss results 14 Final report with all the findings from the study
Milestones
Project Timing1 3
Task Year 2 Year 3
4
5
6
7
8
9, 10
Value of the Research –Project Impact
Beating the decline curve in unconventional gas reservoirs requires continuous drilling and fracturingIn a low gas price environment re-fractreatments offer a low cost alternative to drilling new wells.Performance of re-fracs is highly variable and must be made more reliable and predictable.This project aims to help accomplish that.
Status of Current Technology
Refrac candidate well selection is based on:Statistical databases
Heuristic rules of thumbNeural networks
No systematic way of deciding on the timing of the refracsCurrent refrac treatment designs are done very much like the original fracs.Typically, no account is taken of stress reorientation or previously placed proppant.
Project DeliverablesMonthly status reports. A final report on the results of the Defined Effort. Guidelines for selecting candidate wells for refracturing.Guidelines for selecting the appropriate timing of refracturing given a set of reservoir properties.New designs for better placement of proppants during refracturing operations.Guidelines for fracture placement and spacing in horizontal wells.Guidelines for avoiding fracture interference in wells with multiple fractures.New proppant placement strategies for horizontal well fractures.Detailed analysis and results for at least four refracture treatments in tight gas and gas shale wells.
Project DeliverablesA report on statistical analysis of the refracture database in the Codell formation (2500 refracture treatments).Quantitative guidelines for when to use energized fluids when refracing depleted formations.Guidelines for when to use light weight proppants in refracture treatments.A web site with information about the project and updates as appropriate.A minimum of two presentations in local professional organization meetings; one each in Permian and San Juan Basin areas.At least one presentation at a RPSEA-directed event.An article discussing this project to at least one producer-oriented trade journal.UT will provide technical results containing details and data to be utilized for determination of program impact as requested by RPSEA.
Proposed TasksTask 4. Stress Reorientation around Fractured Wells: Implications for Re-fracturing
Subtask 4.1 Data compilation in the Codell formation and the Barnett shaleSubtask 4.2 Stress re-orientation around fractured wells in shales and tight gas sandsSubtask 4.3 Models for stress reorientation in naturally fractured formations
Task 5. Selecting Timing and Candidate Wells for Re-fracturingTask 6. Re-fracture Designs for Deviated and Horizontal WellsTask 7. Proppant Placement in Re-fracturing Treatments (Vertical and Horizontal Wells)Task 8. Use of Novel Proppant Placement Strategies in Re-fracturing Operations Task 9. Field Design of Re-Fracture Treatments in the Wattenberg FieldTask 10: Design, Implementation and Evaluation of Field FractureDesigns
Task 4: Stress Reorientation
Elastic, homogeneous and isotropic reservoirBiot’s poroelasticity theoryConstant pressure in vertical well and initial fracturePresence of bounding layers with different mechanical properties
Pay Zone
Bounding Layer
Initial Fracture
Task 4: Stress Reorientation around a producing well
Analytical solution compares well with numerical solution
Ref: Zhai, Sharma, 2004
Stress Reorientation Around Producers and InjectorsProducer Injector
Stress Reversal occurs
No Stress Reversal
Direction of Maximum Stress
Angle of Stress Reorientation
Stress Reversal Region
Producer
Isotropic point
Fracture half-length
Direction of Maximum Stress
Angle of Stress Reorientation
Stress reversal region impacts directionof refracture measured in the field
Dimensionless Parameters (Berchenko et al., 1997; Siebrits et al., 1998; Rousell and Sharma, 2009)
Dimensionless Time
Dimensionless Stress Deviator
Dimensionless Fracture Height RatioDimensionless Shear Modulus Ratio
( )( )( )
2 22
4 441 1 21
1xf xf
xf
ct t ktL S L
LM E
κτα ν ν
μν
= = =⎛ ⎞+ −
+⎜ ⎟−⎝ ⎠
Π=S0
σ*
=S0
ηp*
=σhmax−σhmin
α 1−2ν( )1−ν
pRi−pwf
γ =HLxf
β =Gb
Gr
January 20, 2009 DOE Project Kick-off Meeting 18
Parameters Affecting the Stress Reversal Region
The areal extent and timing of the stress reversal depend on:
Fluid propertiesReservoir characteristicsStress contrastDrawdownThickness of the reservoirMechanical properties of the bounding layers
Task 5. Selecting Timing and Candidate Wells for Re-fracturing
The main results were recently published. “Quantifying Transient Effects in Altered-Stress Refracturing of Vertical Wells”, SPE 119522, Presented at the SPE Hydraulic Fracturing Meeting, Woodlands, 2009, Nicolas P. Roussel, Mukul M. Sharma.Work continues to include the effects of stresses induced by fracture creationComparison with field data.
Task 5. Selecting Timing and Candidate Wells for Re-fracturing
0
0.05
0.1
0.15
0.2
0.25
0.001 0.01 0.1 1 10 100 1000
Time (months)
L xf'
/ Lxf
ShaleTight GasSandstone
τmax = 1.3 days
λmax
Optimum time for refracturing
Maximum areal extent of stress
reversal
τmax = 4.13 years
τmax = 1.15 months
Main FindingsAn approaching fracture will go:
Away from a production wellToward an injection well
Stress reorientation depends on:DrawdownStress anisotropyModuli
Stress reversal does occur in fractured producers. We can now compute its,
Spatial extentTiming
January 20, 2009 DOE Project Kick-off Meeting 22
Field Data for Validation
3 wells where refracs worked and 3 wells where refracs did not workA complete dataset would include:
Wellbore schematicBase map showing location of wellsDetails of frac and refrac jobsLogs (dipole sonic)MicroseismicGas flow rate before / after refrac
t = 0
Task 6. Re-fracture Designs for Deviated and Horizontal Wells
t = 0
Stress Reorientation for a Production, Injection Well Pair
t = 0
Stress Reorientation for 1 Production, 2 Injection Wells
t = 0
Stress Reorientation for 2 Production, 1 Injection Well
Task 7, 8. Use of Novel Proppant Placement Strategies in Re-fracturing Operations
Status: Work is underway and we have some initial results.
Wattenberg field, D-J basinCodell formation
Thin sandstone layerLow permeability, requires stimulation
Refractured since 1998Observations indicate that refracture performance is dictated by fracture-fluid viscosity profile (Ref: Miller, J. et al., 2004, SPE 90194)
Fracture reorientation has been reported (Ref: Wolhart, S. et al., 2007, SPE 110034)
Task 9, 10. Design of Re-Fracture Treatments in the Wattenberg Field
Source: USGS
ObjectivesUse principal component analysis to determine the increase in production rate after a refracture treatment.Use stress reorientation models to study the role played by stress reorientation vs other factors such as GOR and depletion.Use these findings to recommend timing for refracsCreate a statistical, predictive model for
Production enhancement Candidate well selection
Groups DescriptionWell information Year
Orig. frac treatment Volume of gel and proppant during the first fracture
Pre-refrac data Production information and number of perforations
Refrac design Gel loading, pad size, surfactant, etc
Refrac treatment Fluid injection, perforations
Rheology Viscosity measurements, gel usage
Water quality Water source, compositionJob comments Problems during the job
Refrac data Production increment
Data SetRefracture well data, approx. 4000 wells Anadarko, Noble Energy (1999 - 2008)
Issues with Data Analysis
Too many parameters (58)Incomplete data setsWide range of refrac production increment
-19.6 ~ 4756 BOE/moNon-numeric data
Data Set ReductionElimination
Elimination criteria
# missing entry > 500
Wells with any missing parameter values
Reduced dataset
Parameters: 58 43
Entries: 2154 1279
Refrac Treatment
Param MGAL
AVG. RATE
New PERF
Total PERF
Leakoff Coef.
PerfFriction
Avg.PSI
131.10 13.60 19 28 0.0010 54 6550
131.21 13.50 30 43 0.0021 100 4400
132.59 13.90 0 60 0.0027 12 5300
132.00 13.00 28 34 270 4900
131.88 14.00 30 50 0.0013 0 7600
131.45 14.50 26 33 0.0013 40 4600
130.76 12.80 18 33 0.0013 210 6100
135.00 14.00 37 0.0013 10 4850
Value
Missing
Entries
127.39 13.00 40 49 0.0013 667 6750
55 58 108 206 1560 1548 62
Missing Values
-2000 0 2000 4000 6000 80000
100
200
300
400
500
600
700
800
Production Increment (BOE/mo)
Freq
uenc
y
Mean : 1282.6Median : 1152.3
Std: 814.7
Mean : 1302.9Median : 1161
Std: 754.5
Data SetReduction
-1000 0 1000 2000 3000 4000 50000
50
100
150
200
250
300
350
400
Production Increment (BOE/mo)Fr
eque
ncy
Data Set ReductionChange of Statistical Properties
0 1 2 3 4-1000
0
1000
2000
3000
4000
5000
Codell Phi*H
Pro
duct
ion
Incr
emen
t (B
OE
/mo)
0 0.2 0.4 0.6 0.8 1-1000
0
1000
2000
3000
4000
5000
Pre-Refrac Cum Rec. Factor
Pro
duct
ion
Incr
emen
t (B
OE
/mo)
Corr. value : 0.21Corr. value: 0.25
Statistical AnalysisCorrelation (2)
Observe the correlations between production increment and parameters
Statistical AnalysisCorrelation (1)
0 1 2 3 40
0.2
0.4
0.6
0.8
1
Codell Phi*H
Pre
-Ref
rac
Cum
Rec
. Fac
tor
0 50 100 150 2000
100
200
300
400
500
600
700
Calcium ppm
Tota
l Har
dnes
s pp
m
Corr value : 0.98Corr. value : 0.13
Observe correlations between parameters
Statistical AnalysisLinear Regression
R2 = 0.47Median error = 42.5%Mean error = 95.9%Less number of dimensions can help improve regression 0 500 1000 1500 2000 2500 3000 3500 4000
0
500
1000
1500
2000
2500
3000
3500
4000
Actual production increment
Pre
dict
ed p
rodu
ctio
n in
crem
ent
goodbad
Parameter ReductionCorrelation among parameters suggest a presence of highly correlated parametersEliminate highly correlated parameters: 43 25
Water quality (11)
Total hardness
Pre-refrac (4)
Pre-refrac Cum Rec. Factor
Orig. Fractreatment (5)
•Orig. Vol (MGAL)•Orig. Zone
• More entries recovered: 1279 1316
Statistical AnalysisLinear Regression w/ Reduced Parameters
R2 = 0.31Median error = 42.1%Mean error = 100.9%Requires a more sophisticated method for better fits
0 500 1000 1500 2000 2500 3000 3500 40000
500
1000
1500
2000
2500
3000
3500
4000
Actual production increment
Pre
dict
ed p
rodu
ctio
n in
crem
ent
goodbad
Statistical AnalysisPrinciple Component Analysis
Original dataset [m x n] mapped into new orthogonal vectorsVariance of dataset can be captured with less dimensionsReduction of dimensions provides a better regression
Choose eigenvectors [n x k]
Covariance matrix [n x n]
Eigenvectors [n x n]
Dataset transformation [m x k]
Future Work
Fill-in missing data instead of row elimination to increase the number of wells in data set.Work with Noble Energy on adding additional data (e.g. regional stress data)Data mining techniques
K-means clusteringNeural nets
Technical Issues/Problems Encountered
Minor issues with getting contract in place.Data access issues have been resolved.
Summary of Progress to Date
Stress reorientation due to poroelastic effects has been calculated for vertical, fractured and horizontal wells.Key parameters and conditions that control this stress reorientation have been identified.The optimum timing of refrac treatments has been computed for the first time. A data set of refrac treatments from the Wattenburg field has been reviewed and is being analyzed for statistical trends.Review of refrac treatment designs in progress.
Thank youQuestions?
I would like to Acknowledge:
RPSEA for their support.
Our partner companies (Anadarko, BJ Services, Noble Energy, Pinnacle) for collaboration and access to data.
Members of the Fracturing and Sand Control JIP at the University of Texas at Austin (Anadarko, BJ Services, BP, ConocoPhillips, Halliburton, Schlumberger, Shell, Total) for providing the cost sharing for this project.