Spatial variability of tight oil well productivity and the impact of technologyJustin Montgomery
PhD CandidateDepartment of Civil and Environmental EngineeringIn collaboration with Francis O’Sullivan and John Williams
MIT Earth Resources Laboratory2017 Annual Founding Members MeetingMay 31, 2017
Williston Basin of North Dakota was at the forefront of tight oil extraction but now faces economic uncertainty
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 2
Rising rig and well productivity suggest greater resilience than expected
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 3
2008 2010 2012 2014 2016
020
040
060
080
0
New−w
ell o
il pr
od. p
er ri
g (b
bl/d
)
050
100
150
200
250
Rig
cou
nt (#
of a
ctive
rigs
)
New−well oil prod. per rigRig count
2008 2010 2012 2014 2016
020
4060
8010
012
0
Mea
n ne
w−w
ell f
irst y
ear p
rod.
(Mbb
l)
010
020
030
040
050
060
0
New
wel
ls (w
ells
/qua
rter)
Mean new−well prod.New wells per quarter
Improvement of well productivity has been driven in part by changes in well and stimulation design
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
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- Trends toward longer wells and larger stimulations (hydraulic fracturing) Increase in proppant (sand) per well over time
- Motivation for identifying impact:1. Forecast well productivity based on
anticipated changes2. Optimize wells
Another important dynamic is where wells are being drilled –“sweet-spotting” or “high-grading”
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 5
Well productivity heat map
Source: Schmidt, 2011
- Activity continuing to cluster in high productivity areas
- Motivation for identifying location influence:1. Need to control for this to accurately
understand impact of design changes2. Assess well portfolios and resource
economics based on location in field
How much of the improvement in well productivity is due to technology (design changes) vs location (sweet spotting)?
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 6
v Horizontal length of completed well
Technology Location v Target formation (Bakken/Three Forks)v Amount of water
injected to create fissures
v Amount of proppant(sand) injected to “prop” fissures open
v Latitude/longitude of wells
- Big public datasets available (Frac Focus, North Dakota Mineral Resources)- Can we use econometrics/machine learning to understand and make predictions?
Current regression models to understand the influence of technology on productivity
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 7
- Nonspatial linear regression (NS)- Fixed Effects (FE), such as county-level used by EIA
- Issues:- Not spatially granular enough- Residuals are spatially autocorrelated
à Omitted variable bias
Regression-kriging provides an appropriate tool for distinguishing between impact of location and technology
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 8
Estimate trend with linear regression
Estimate spatial correlations by kriging residuals
Remove interpolated spatial component
No
Estimates for model parameters
Converged?
Yes
Technologytrend
Spatial component
Start
RK improves accuracy (in 10-fold cross validation) compared to currently used regression models
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 9
0 100 200 300 400
010
020
030
040
0
Predicted first year prod. (Mbbl)
Actu
al fi
rst y
ear p
rod.
(Mbb
l)
Nonspatial regression
MASE = 0.938
0 100 200 300 400
010
020
030
040
0
Predicted first year prod. (Mbbl)
Actu
al fi
rst y
ear p
rod.
(Mbb
l)
MASE = 0.873
0 100 200 300 400
010
020
030
040
0
Predicted first year prod. (Mbbl)
Actu
al fi
rst y
ear p
rod.
(Mbb
l)
MASE = 0.62
Fixed effects-county Regression-kriging
Existing regression models overestimate the role of technology relative to location
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 10
Technology
Location
Overestimating the impact of technology leads to overoptimistic forecasts and poor design choices for wells
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 11
Forecasts for 2018 designsKey findings
1. Regression-kriging improves prediction accuracy
2. Shifts in well design and drilling location have contributed equally in recent years
3. County-level fixed effects inadequate to detect sweet-spotting à EIA forecast is likely overoptimistic
4. Current models encourage over-stimulation of wells
Future work
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 12
- Apply to other unconventional fields
- Predict decline rates
- Use to develop improved field-scale economic models
Thank you! Questions?
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 13
- Thank you to MIT Energy Initiative for supporting this research
- Full paper is:Montgomery, J. B., & O’Sullivan, F. M. (2017). Spatial variability of tight oil well productivity and the impact of technology. Applied Energy, 195, 344-355.
US tight oil production growth has demonstrated the potential of shale and other unconventional formations – Combined output from three of the main US plays alone is now equivalent to the total output of China or Canada
14Source: F. O’Sullivan, United States Energy Information Administration, HPDI Production Database
Illustration of crude oil production growth from some select major U.S. unconventional oil plays since 2005MMbbls of oil per day
0
1
2
3
4
5Eagle Ford
Bakken
PermianCombined, the Bakken, Permian and Eagle Ford have added more than 4 MMbbls per day of production to US output over the past 5 years
Tight oil plays now support more than 50%
of US Crude
15
Surface trend analysis (productivity fit to polynomial of coordinates)
Fixed effects – county or township level
Some other approaches that have been used to control for location
Results of regression kriging – Productivity forecast with typical well designs for 2018
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 16
−104.0 −103.5 −103.0 −102.5
47.0
47.5
48.0
48.5
49.0
Longitude
Latitude
[40,70)[70,100)[100,130)[130,160)[160,190)[190,220)[220,250]
Predicted firstyear prod. (MBbl)
17
Each model provides a good fit to the mean productivity over time
6080
100
120
140
Mea
n ne
w−we
ll firs
t yea
r pro
d. (M
bbl)
2012 2013 2014 2015 2016
NonspatialFESTASEMRK
ActualActual−IQR
18
Training only with data from early wells shows that mean production can be reliably forecasted based on changes in location and technology
7580
8590
9510
010
511
0
Mea
n ne
w−w
ell f
irst y
ear p
rod.
(Mbb
l)
2012 2013 2014 2015 2016
NonspatialFESTASEMRKActual
Training Hindcastvalidation
19
8090
100
110
120
130
Mea
n ne
w−w
ell f
irst y
ear p
rod.
(Mbb
l)
2012 2014 2016 2018
NonspatialFESTASEMRKActual
Using EIA projected designs for 2018
These models are useful for forecasting production and economics of future wells – Important differences between RK and existing approaches such as FE become clear
20
Differences in impact attributed to different parameters
NS FE STA SEM RK
Shar
e of
pro
duct
ivity
incr
ease
(%)
020
4060
8010
0
Location (acreage quality)
Proppant
Water
Lateral Length
NS FE STA SEM RK
Shar
e of
pro
duct
ivity
incr
ease
(%)
020
4060
8010
0
Location (acreage quality)
Proppant
Water
Lateral Length
NS FE STA SEM RK
Shar
e of
pro
duct
ivity
incr
ease
(%)
020
4060
8010
0Location (acreage quality)
Proppant
Water
Lateral Length
NS FE STA SEM RK
Shar
e of
pro
duct
ivity
incr
ease
(%)
020
4060
8010
0
Location (acreage quality)
Proppant
Water
Lateral Length
NS FE STA SEM RK
Shar
e of
pro
duct
ivity
incr
ease
(%)
020
4060
8010
0
Location (acreage quality)
Proppant
Water
Lateral Length
21
– Spatial trends and patterns result from physical processes over long lengths of time– Occur at various scales (e.g. macro: formation thickness, grain size/porosity, thermal maturity;; micro: natural fractures)
– Geological controls may be poorly understood or hard to quantify
Location is important because key geological controls on production vary spatially across basin
22
Depth (ft)
Location is important because key geological controls on production vary spatially across basin
Amount of proppant has been increasing over time and is correlated with productivity
MIT Earth Resources Laboratory2017 Annual Founding Members Meeting
Slide 23
24
Water trends
25
Lateral length trends
26
Definition of models:
Multiple linear regression model:
Ordinary least squares:
Multiple linear regression model with variance-‐covariance matrix:
Generalized least squares:
27
One approach to estimating the effect of technology on productivity is linear regression with ordinary least squares – Omitted-‐variable bias is a problem though
28
Technology
ProductivityGeological controls (omitted variable)
More realistically:
Bias of Estimate:
Bias is introduced if:
29
Evaluating the models
Moran’s I-‐1 10
Highly dispersed
Highly clusteredRandom
Moran’s I to measure spatial autocorrelation:
Back transformation:
Model accuracy:
10-‐fold cross validation:
Comparison of models:
Spatial weights matrix W: Coefficient estimates:
0 10 20 30 40 50
0.00
0.05
0.10
0.15
0.20
k−neighbor neighbor
Mea
n sp
atia
l wei
ght
SEMRK
Comparison of models’ estimates of technology and location driven improvement in productivity
Mea
n ne
w−w
ell p
rodu
ctiv
ity
(inde
xed
to Q
1−20
12)
11.
11.
21.
3
2012 2013 2014 2015 2016
FEFE−technology constant
Mea
n ne
w−w
ell p
rodu
ctiv
ity
(inde
xed
to Q
1−20
12)
11.
11.
21.
3
2012 2013 2014 2015 2016
STASTA−technology constant
Mea
n ne
w−w
ell p
rodu
ctiv
ity
(inde
xed
to Q
1−20
12)
11.
11.
21.
3
2012 2013 2014 2015 2016
SEMSEM−technology constant
Mea
n ne
w−w
ell p
rodu
ctiv
ity
(inde
xed
to Q
1−20
12)
11.
11.
21.
3
2012 2013 2014 2015 2016
RKRK−technology constant