Download Lessons Learned From Data Mining in Unconventional Reservoirs

Lessons Learned From Data Mining
in
Unconventional Reservoirs
Randy F. LaFollette
Director, Applied Reservoir Technology
Baker Hughes Pressure Pumping
Presentation Outline
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Importance of Data Mining
Data Sources
Data Mining Methods
Case Study Highlights
2
Problem / Solution
• Problem…
– High well count, but…
– Most with low-granularity data
– Inconsistent production results
– Multi-million dollar decisions to be made
• Solution – Data Mining for Data-driven
decisions
3
Data Mining Tools – Past and Present
R
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At the Beginning: The Variables
• Reservoir Quality
– Q f { k, h, P , m, r }
– Proxied by well location
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Well Architecture
Completion
Stimulation
Production Management
Production Metrics
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Production Result Time Dependence
Barnett Shale Timeline
Max Gas Rate
H
1981
D
V
Completion Date
2009
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Well Architecture, Completion
& Stimulation Time-Dependence
ENGLISH UNITS
2004 2005 2006 2007 2008 2009
PERFORATED LENGTH, ft
1,793 1,874 2,054 2,187 2,363 2,675
TREATMENT VOL/CLAT, gal/ft
2,206 1,951 1,779 1,497 1,418 1,410
PROP QUANTITY/CLAT, lb/ft
METRIC UNITS
672
711
886
995
993
953
2004 2005 2006 2007 2008 2009
PERFORATED LENGTH
547
571
626
667
720
816
TREATMENT VOL/CLAT, m3/m
27
25
22
19
19
18
PROP QUANTITY/CLAT, kg/m
948
1,112 1,354 1,557 1,615 1,461
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Not the Barnett “Shale,” but close
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History: Spreadsheets and Cross Plots
Do Larger Treatments Yield Increased Production?
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Geographical Information Systems Mapping
• 200,000+ wells in Fort Worth
Basin
• 12,000+ Barnett Horizontals
• Provide for data-driven
discussion of best practices
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Modern Analysis Techniques
• Multivariate, non-linear, using boosted trees
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Available Data
• Commercial data sets
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Well history
Completion & stimulation practices
Monthly production
3,300+ directional surveys
• FracFocus data
• In-house data sets
• Collected, reviewed, put into a database
• Quality Control Process
– Statistical removal of outliers
– Known limits & ratios examination
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Modern Analysis Techniques
Barnett Vertical Wells
• Geographical Information Systems (GIS)
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Lowest 10% and Best 10% Barnett Hz Wells
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SPE 163852
Application of Multivariate Analysis and
Geographic Information Systems PatternRecognition Analysis to Production Results in
the Bakken Light Tight Oil Play
Randy F. LaFollette, Ghazal Izadi, Ming Zhong, Baker Hughes
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Middle Bakken Light-Tight Oil Play,
Montana and North Dakota
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Middle Bakken Eastern Montana vs. North Dakota
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Slide 18
Exploratory Data Analysis
One-Column Format
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Slide 19
Related Variables, Data Clustering, Outlier Identification
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Slide 20
Transformed Scatterplot
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Middle Bakken, Max Monthly Oil,
n=750 wells
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Slide 22
Middle Bakken, BO/ft
Most Influential
•CLAT
•Location
•Prop Qty
•Fluid Vol
•Prop Conc
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Lateral Length and Well Efficiency Log 10 Max Oil Rate
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SPE 168628
Application of Multivariate Statistical Modeling and
Geographic Information Systems Pattern-Recognition
Analysis to Production Results in the Eagle Ford
Formation of South Texas
Randy F. LaFollette, Dr. Ghazal Izadi, Dr. Ming Zhong
SPE, Baker Hughes
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Slide 25
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Slide 26
Data Sources, QC, Focus
• Public and proprietary
• In house proprietary database
• Commercial US Well Database
• Well headers, location, architecture, completion, stimulation,
production
• Focus on oil wells (GOR <15,000 scf/bbl)
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Mineralogy/Rock Properties
Eagle Ford
Dewitt County
Others
Quartz
80 miles
40 miles 30 miles
Total Clays
Total
Carbonates
Dimmit County
La Salle County
Others
McMullen County
Others
Total Clays
Quartz
Quartz
Quartz
Total Clays
Total Clays
Total
Carbonates
Young's Modulus
(Mpsi)
3
Total
Carbonates
Poisson's Ratio
0.16
Total
Carbonates
Young's Modulus
(Mpsi)
Poisson's Ratio
Young's Modulus
(Mpsi)
Poisson's Ratio
6
0.15
3
0.12
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Eagle Ford Completion / Stimulation
Parameters
Area 1 (n = 1908)
Area 2 (n=1103
Area 3 (n=855)
5%
95%
5%
95%
5%
95%
CLAT (ft)
3,017
6,288
3,551
7,466
3,330
7,321
Proppant (lbs)
1,600,210
7,644,200
530,045
8,956,052
1,915,940
7,538,200
Fluid (gals)
1,865,875
7,332,679
260,034
9,453,051
2,408,469
10,249,256
Stage Count
1
19
1
20
1
16
Proppant Concentration
(lb/gal)
0
2
0
2
0
2
Proppant/Lateral Length
(lb/ft)
487
1,555
165
1,544
581
1,355
CLAT (m)
920
1,917
1,082
2,276
1,015
2,231
Proppant (kg)
726,495
3,470,467
240,640
4,066,048
869,837
3,422,343
Fluid (m3)
7,063
27,757
984
35,784
9,117
38,798
Stage Count
1
19
1
20
1
16
Proppant Concentration
(kg/m3)
103
125
244
114
95
88
Proppant/Lateral Length
(kg/m)
790
1,811
222
1,787
857
1,534
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Slide 29
Max Monthly Oil Rate, Area 1
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Slide 30
Max Monthly Oil, Key Drivers, Area 1
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Slide 31
Max Monthly Oil,
Partial Dependence Plots, Area 1
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Summary
• Data sources, methods, tools, lessonslearned from unconventionals
• Interpretation most complete using
multivariate statistical methods
• Reservoir quality, well architecture,
completion, stimulation all significant
production drivers
• Data Mining for Data-driven decisions!
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Acknowledgements
• The author thanks SPE and the Management
of Baker Hughes for the opportunity to
present this work to the global SPE
community.
• Thanks also go to my team members, past
and present, for their hard work and insights
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Dr. Ming Zhong
Dr. Ghazal Izadi
Bill Holcomb
Dr. Jorge Aragon
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Thank You!
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