Ongc to Develop of Parbatpur Area in Jharia Coal Field in Jharkhand for Commercial Production of Cbm
CBM Field development planning studies ; workflows and tools,...
Transcript of CBM Field development planning studies ; workflows and tools,...
CBM Field development planning studies ;
workflows and tools, best practices
Illustrated examples – actual figures have been altered
to ensure confidentiality
Laurent Alessio Managing Partner
Leap Energy Subsurface Consulting Services
Agenda
1. Preamble : CBM plays and the variability complexity Key aspects to frame the problem
Viewing the CBM plays through a statistical lense
Contrasting conventional offshore reservoirs to CBM plays
2. Reservoir Characterisation Key parameters impacting the performance of CBM wells
A few illustrated expected relationships and trends
3. Property Modelling – spatial distribution Geo-statistical methods as a fundamental tool to represent variability
NetCoal and Permeability high-grading
4. Production Forecasting Different methods and associated tools
5. Concept Selection Key elements of the field development planning workflow
Illustration of economical screening – what can be done
6. Conclusions
General characteristics of CBM/CSG
provinces
• Large areas, multiple seams, very variable reservoir characteristics
within short-distances
• Significant inter-well variability, deliverability (perm) and recovery follow
lognormal distribution
Source : SPE 107308
G.Swindell (2007)
Understand Value Of Information – what confidence can be given to sparse data
Concept select decisions under variability
Averaging (upscaling) into a single curve
Source : AAPG 2006
C. Boyer
Viewing CBM provinces through a statistical
lense
Ideal setting for combined geostatistical workflow (Monte-Carlo and
mapping)… but don’t believe the resulting map !
Beware of representativeness of data especially in sparse dataset
Mapping – deterministic vs. statistical representation
Some techniques available for
characterising uncertainty from variability
and available data to date
Confidence curve –SPE-133518
+ =
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60%
% C
on
fid
en
ce
in
Fie
ld M
ea
n
% Data available
Plot 1: Evolution of Confidence Curves (varying folds) vs. % data available
1.10
1.25
2.00
3.00
5.00
7.00
9.00
10.00
88% confidence in a fold of 2.00
At 10% data available (% well drilled)
40% confidence in a fold of 1,25
At 10% data available (% well drilled)
Fold
Asset-Specific Well data
Regional/analogues correlations
*
MeanTopDp[m]
MeanGrossThk[m]
MeanMidDp[m]
MeanNetPay[m]
P10 VL [m3/ton]
P50 VL [m3/ton]
P90 VL [m3/ton]
Mean VL [m3/ton]
P10 PL [kPaa]
P50 PL [kPaa]
P90 PL [kPaa]
Mean PL [kPaa]
P10 DAFGC[g/cm3]
P50 DAFGC[g/cm3]
P90 DAFGC[g/cm3]
MeanDAFGC[g/cm3]
Perm P10 [mD]
Perm P50 [mD]
Perm P90 [mD]
PermMean[mD]
Well data
45.3
293.5
192.1
18.7
28.1
24.3
17.9
23.9
5396.2
3854.4
2312.7
3854.4
6.8
3.3
1.6
3.8
645.19
74.20
8.53
234.79
User Value
45.3
293.5
192.1
14.5
28.1
24.3
17.6
23.9
5396.1
3854.4
2312.6
3854.4
6.8
3.3
1.6
3.8
645
74
8.5
235
Regional&area speci fic
200.0
200
300.0
10
29.3
25.2
18.1
24.8
5673.9
4052.8
2431.7
4052.8
6.8
3.3
1.6
3.8
350.0
132.3
50.0
171.8
MeanTopDp[m]
MeanGrossThk[m]
MeanMidDp[m]
MeanNetPay[m]
P10 VL [m3/ton]
P50 VL [m3/ton]
P90 VL [m3/ton]
Mean VL [m3/ton]
P10 PL [kPaa]
P50 PL [kPaa]
P90 PL [kPaa]
Mean PL [kPaa]
P10 DAFGC[g/cm3]
P50 DAFGC[g/cm3]
P90 DAFGC[g/cm3]
MeanDAFGC[g/cm3]
Perm P10 [mD]
Perm P50 [mD]
Perm P90 [mD]
PermMean[mD]0%
20%
40%
60%
80%
100%
0.1 10 1000
Cu
mu
lativ
e
pro
ba
bil
ity
of
Ex
ce
ed
en
ce
Permeability [mD]
Perm
Cumulative probability
0%
20%
40%
60%
80%
100%
0 5 10 15
Cu
mu
lativ
e
pro
ba
bil
ity
of
Ex
ce
ed
en
ce
Gas Content [m3/t]
GC(scc/g)
Cumulative probability
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04
Cu
m P
rob
ab
ilit
y
Permeability (mD)
Permeability Distribution vs. DepthAssuming NetStress Exponential factors of (P90, P10)=-0.6 , -0.2 Mpa-1
K (mD) at 200 m
K (mD) at 400 m
K (mD) at 600 m
K (mD) at 800 m
K (mD) at 1000 m
K (mD) at 1200 m
K (mD) at 1400 m
K (mD) at 1600 m
K (mD) at 1800 m
K (mD) at 2000 m
0
2
4
6
8
10
12
14
16
18
20
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Gas
Co
nte
nt
(m3/
t)
Depth_mBD
Gas Content vs Depth (Reference Case)
Wallon Bandanna Early Permian Wallon REF Bandanna REF EP REF
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
0 10 20 30 40 50 60 70 80 90 100
pL
(kP
a)
Temp (C)
pL (Langmuir Pressure) vs Temp - REFERENCE CASE
Wallon Bandana EP Wallon Bandanna EP
0
2
4
6
8
10
12
14
16
18
20
0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Gas
Ad
sorb
ed
DA
F (m
3/t
)
Pressure (kPa)
Early Permian Isotherm Model
Ref Sat Hi Sat Lo Sat Gc Ref DB3-C1 DB3-C2 DB3-C3 DB3-C4 DB3-C5 DB3-C6 DB1-CBM455
Projected
Bridge Creek Eurombah East
Currawong
Depth Structure map – Base
ATP
0 5 10 15
Incre
asin
g L
ike
lih
oo
d
>>
Gas Content [m3/t]
X Value
P50
Mean
0%
20%
40%
60%
80%
100%
0 100 200 300 400 500
Cu
mu
lativ
e
pro
ba
bil
ity
of
Ex
ce
ed
en
ce
Gross Res Thickness [m]
Gross Thk
Cumulative probability
0%
20%
40%
60%
80%
100%
0 50 100 150 200
Cu
mu
lativ
e
pro
ba
bil
ity
of
Ex
ce
ed
en
ce
Top Res Depth [m]
Top Coal
Cumulative probability
Spacing [km]
0.88
De
pth
[m b
elo
w s
urf
ace
]
min BHP [psia]
40
Pinnette Horiz
Multi-
latera
l
Deviated Slanted Cavitation Frac UnderReam
No No No Yes Yes No Yes Yes
*Fekete F.A.S.T. CBM
• Variability is defined as a short to medium scale (up to inter-well scale) variations of a given parameter, such as
permeability, porosity, gas content (for CBM reservoirs), hydrocarbon saturation etc… These variations can be often extreme, with
several orders of magnitude differences in permeability commonly observed in fractured reservoirs. Variability is intrinsic and non-
temporal, which means it that does neither change with time nor with the number of data points, and it is a characteristic of the reservoir
(for a given sampling scale*). Ultimate understanding of variability often remains spatially poorly predictive, so the authors recommend a
statistical approach is always conducted in parallel.
• Uncertainty is defined at the field scale, or at least, a sector or segment of the field (field unit), where multiple
wells will be ultimately drilled. It represents, at a given time, how well a field unit is understood. Generally, uncertainty reduces
with time and information becoming available, provided the right framing and uncertainty assessment was conducted. Arguably, the
uncertainty in subsurface givens, such as field porosity average, or in place volumes is strictly a consequence of our lack of knowledge.
Development related metrics, such as field recoverable volumes or production performance, at a given time, are a consequence of our
level (or lack of) of understanding of the subsurface and the concept development choices we have made and will be making.
Reference : SPE 133518
Variability vs Uncertainty
Field Uncertainty
K(mean) vs. depth (P90, P50, P10)
P10
P90
Variability
K(well) vs. depth
OPERATE IMPLEMENT DEFINE CONCEPT SELECT
CONCEPT ASSESSMENT
PROJECT INITIATE
TRADITIONAL E&P OPPORTUNITY MATURATION CAN BE USED BUT ITERATIVELY THROUGH PROJECT LIFE
UNCERTAINTY REDUCTION VS. TIME AND GATE
% SCOPE IMPLEMENTED VS TIME AND GATE
----- CSG : Early pilots and Tech
trials provide some de-risking,
but progressive Uncertainty
Reduction through continuous
development
- - - Conventional:
Major uncertainty
reduction through
Appraisal, FDP studies
and Development drilling
Uncertainty reduction is more progressive through different
project phases in CBM/CSG plays
Residual uncertainty remains through to OPERATE
50%
100%
Time, asset maturation
Success in CBM Development Planning :
Integration of disciplines and reservoir data to achieve a distribution
shift of outcomes
0
1
2
3
4
5
6
7
8
Well PotentialProbability Curve
Optimised
development
plan
Outcome improvement Economic/Value-Metric
Increase the probability of
drilling higher deliverability
wells
Increase the probability of
drilling higher GIP/km2
wells
Pure
pattern drill
Geophysical Geological Petrophysical Reservoir Eng.
Structural deformation
Faults
Coal wash-outs
High net coal mapping
Fracture interpretation
from core
Structural history
Tectonic history
Borehole image
Breakouts and fracture
interpretation
DST interpretation
Formation testing
RESERVOIR CHARACTERISATION
Uncertainty Assessment process
Identify
Uncertainties
Quantify
ranges
Assess
impactRank
Uncertainty assessment workflow
ANOVA
Tornado Plotting
Analogue / Data
assessment
Key Parameters impacting Recoverable Volumes
and some ‘to be expected’ relationships
RECOVERABLE UNCERTAINTY
-3.1%
1%
0%
5%
3%
2%
-2%
0%
-7%
-5%
-30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0%
GC
Skin
Net
Perm
DI
Change to Recovery Factor
Sector 'A'
Hi
Low
-4%
3%
0%
13%
6%
3%
-3%
0%
-17%
-8%
-30% -20% -10% 0% 10% 20% 30%
GC
Skin
Net
Perm
DI
Change to Recovery Factor
Sector 'B'
Hi
Low
Permeability vs depth
A proposed distribution per depth bin model – in line with a prediction of decreasing K
vs. Net Stress and nature of stochastic distribution (log-normal)
DST MEASURED PERMEABILITY FITTED LOG-NORMAL DISTRIBUTIONS, DEPTH-BINNED
P10
P90
Match obtained with P90 and P10
follow a different degradation path
Use an exponential decline vs. net
stress (depth)
Isothermal properties
Dewatering Index as a key measure of performance
PROPOSED DEFINITION DEWATERING INDEX
0
100
200
300
400
500
600
700
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Gas
Co
nte
nt
(scf
/to
n)
Pressure (psia)
Langmuir Isotherm
Langmuir Isotherm
Initial Condition
Critical Point
Abandonment Point
a
b
Dewatering Index is designed to
characterise best the production
response of a well :
The distance between Critical
Desorption Pressure (CDP) and
Original reservoir pressure (Pi)
drives the dewatering phase.
The gas content difference
between CDP and Pab drives the
recovery and production
performance.
Pi
CDP
Pab
COMPARISON OF IMPACT OF WELL TYPE CURVE PARAMETERS : SATURATION VS. ISOTHERM
Stronger correlation between production parameters and DI compared to Sat %
Isothermal properties
Dewatering Index as a key measure of performance
y = 101.68x2 - 304.25x + 199.39R² = 0.9982
0
20
40
60
80
100
120
140
160
180
200
0 0.2 0.4 0.6 0.8 1 1.2
Pe
ak G
as R
ate
(Msc
fd)
Dewatering Index
Peak Gas Rate vs DI
y = 304.1x2 - 149.15x + 27.068R² = 0.9717
0
20
40
60
80
100
120
140
160
180
200
0 0.2 0.4 0.6 0.8 1 1.2
Pea
k G
as R
ate
(Msc
fd)
Saturation
Peak Gas Rate vs Saturation
y = -6E+08x2 + 1E+08x + 4E+08R² = 0.9787
0.00E+00
5.00E+07
1.00E+08
1.50E+08
2.00E+08
2.50E+08
3.00E+08
3.50E+08
4.00E+08
4.50E+08
5.00E+08
0 0.2 0.4 0.6 0.8 1 1.2
Cu
mu
lati
ve G
as (
scf)
Dewatering Index
Cumulative Gas vs DI
y = -8E+07x2 + 6E+08x - 5E+07R² = 0.9375
0.00E+00
5.00E+07
1.00E+08
1.50E+08
2.00E+08
2.50E+08
3.00E+08
3.50E+08
4.00E+08
4.50E+08
5.00E+08
0 0.2 0.4 0.6 0.8 1 1.2
Cu
mu
lati
veG
as (
scf)
Saturation
Cumulative Gas vs Saturation
Isothermal properties
Dewatering Index as a key measure of performance
COMPARISON OF IMPACT OF WELL TYPE CURVE PARAMETERS : SATURATION VS. ISOTHERM
Stronger correlation between production parameters and DI compared to Sat %
y = 283.16e3.2536x
R² = 0.9209
0
2000
4000
6000
8000
10000
12000
0 0.2 0.4 0.6 0.8 1 1.2
Tim
e t
o P
eak
Gas
(d
ays)
Dewatering Index
Time to Peak Gas vs DI
y = 14805e-3.488x
R² = 0.9702
0
2000
4000
6000
8000
10000
12000
0 0.2 0.4 0.6 0.8 1 1.2
Tim
e t
o P
eak
Gas
(d
ays)
Saturation
Time to Peak Gas vs Saturation
y = -0.7366x2 + 0.2783x + 0.4608R² = 0.9209
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
0 0.2 0.4 0.6 0.8 1 1.2
Re
cove
ry F
act
or
Dewatering Index
Recovery Factor vs DI
y = -0.2396x2 + 0.8068x - 0.0547R² = 0.8446
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
0 0.2 0.4 0.6 0.8 1 1.2
Re
cove
ry F
act
or
Saturation
Recovery Factor vs Saturation
PROPERTY MODELLING
RESERVOIR
PROPERTY
Modelling key geological parameters
Proposed considerations – example NET COAL
LARGE SCALE
TRENDS
UNDERLYING
GEOLOGY
CAPTURE VARIABILITY
Example: Net Coal distribution over large play
Decreasing Net Coal from NE to SW
Variability accounted
for with SGS
Geo-statistical model
Large scale-predictive
Variability represented, but cannot be deterministically trusted at inter- well scale
GAS
CONTENT
Proposed workflow for NetCoal prediction from 2D seismic
A tested method using A/B amplitude techniques to utilise different seismic
vintages
Net coal map based on well
gridding only Geophysically
constrained net
coal mapping
Follow consistent
workflow to
remove amplitude
differences
between seismic
vintages
Net coal map integrating
seismic information
Top reservoir = prominent coal package
Channel Washout (Strong top-coal Peak disappears)
Evidence from
seismic data that
coal wash-outs are
present and can be
detected
Key elements of the fracture network
characteristics and optimisation opportunities
Orientation What are the principal fracture
families and their major
orientation
Density What are the areas where a
significantly different fracture
density occurs
Anisotropy Non-regular spacing orientation – reducing
total drilling costs
Deviated and horizontal well azimuth
Permeability modulus Expected higher ‘average’ deliverability &
dewatering profile
Impact on spacing decisions and well type
The higher # of joints
and cleats intersected by
a well, the higher the
chance that at least one of
these fractures has
enough aperture and
continuity to provide good
permeability
Permeability prediction can be enhanced by mapping of fracture characteristics
Permeability controlled (statistically) by
fracture intensity
Hermitage North 1
Fracture density mapping – expected relationship to
permeability (at least statistical!)
Fracture families are individually mapped and a modulus computed
Combined cleat spacing
(defects/10m) map:
•Based on kriging of well data
guided by cleat orientation
•Each cleat set is gridded
separately first, maps per set
are then merged
•# at wells show the well
observed cleat density
Note: cleat mapping is limited to
areas within 10km from well
control (scanner data)
High fracture density
Low fracture density
Single or multiple directions
Expected to coincide with areas of
higher incidence of high permeability
Single or multiple directions
Expected to coincide with areas of
low incidence of high permeability
• Combined cleat anisotropy map (ratio of
largest over smallest set spacing):
• Based on kriging of well data guided by
cleat orientation
• Each cleat set is gridded separately first,
maps per set are then merged
Mapping Anisotropy – relative density of fracture families
allows the assessment of spacing – quantitative analysis is
possible through reservoir simulation or analytical calculations
Highly
anisotropic
(=unidirectional)
network
Isotropic
network
Anisotropic
gridding
Isotropic gridding
PRODUCTION FORECASTING
• Different methods are possible and in use within the industry
– Analytical and Numerical – objectives should be complementary
– Recommend scaled-approach and understand/capture areal trends
• Key is a realistic property mapping
– Again understand trends and dependencies
• Focus on understanding variability and uncertainty
– Speed and replicability of workflows and tools pays off
Dynamic Modelling Approaches Methods, focus areas and recommendations
Full numerical or material-balance analytical models ? Depending on modelling objectives
Very complex well geometries
Very low permeabilities
(<0.1mD)
Capture transient effects (non
boundary-dominated flow)
Test fine-scale heterogeneity
(assuming can be modelled)
3D numerical
simulation
Model well types with
analytical methods (skin)
Most traditional CBM plays
Constant drainage per well
(unchanged with time)
Constant average properties
within each well drainage
Multi-well
material balance
Very large area and no wells
>100 wells – field scale
modelling
Medium areas and no wells (5-
20 wells) - Sector modelling
SPEED
Full numerical or material-balance models ? Some of the available tools on market
3D numerical
simulation
Multi-well Material
Balance
ECLIPSE CBM
CONCEPT SELECTION
Key Decisions to be made
• Well Technology : well trajectory and completion
• Spacing : surface and subsurface
• Sectorisation : define areas of incrementally higher unit cost (or other metric)
• Development Intensity : drilling pace and scheduling
Workflows and tools
• Integrating geological models, production forecasting and (proxy) economical screening capabilities
• Focus on key uncertainties’ impact on decisions
Associated Outcomes from the FDP Optimisation
• Decision mapping for future technology trials and appraisal
• High-grading areas of increasing potential
• Defining economic limits and economic sensitivities
Concept Select and Field Development Planning Optimisation Using nested workflows and tools
Technology Trials
Appraisal Strategy
Well technology concept
Spacing
Sectorisation
The Concept Select Workflow Tackling technology, spacing and sector definition in one iteration
An example of Value Metric:
Run alternative technologies
Set up dynamic models Map or Distribution based
Run alternative spacing
Compute Value Metrics
Selection
Selection of Optimum Spacing
for a given technology
Well Drilling and Completion Cost (USD) vs Depth
y = 313.81x + 1E+06
R2 = 0.7954
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
2,000,000
0 200 400 600 800 1000 1200 1400 1600 1800
DEPTH (m)
USD
$
Cost per well model
Technology ‘Concept A’
Depth over the Asset
Production
Forecast
Repeat calculation at
different spacing
Generate field Economic predictions Unit Cost, NPV/well – different spacing
0
1
2
3
4
5
6
7
8
9
10
-600,000,000.0
-400,000,000.0
-200,000,000.0
0.0
200,000,000.0
400,000,000.0
600,000,000.0
0 0.5 1 1.5 2 2.5P
roje
ct N
PV
($
mm
)
Well Spacing (km)
WELL CONCEPT (A) : DEVIATED WELL, MULTI-STAGED FRACSPROJECT UNPV (@ 4.5 $/GJ) AND UDC v WELL SPACING
PROJ NPV UDC
0
1
2
3
4
5
6
7
8
9
10
-600,000,000.0
-400,000,000.0
-200,000,000.0
0.0
200,000,000.0
400,000,000.0
600,000,000.0
0 0.5 1 1.5 2 2.5
Pro
ject
NP
V (
$m
m)
Well Spacing (km)
WELL CONCEPT (A) : DEVIATED WELL, MULTI-STAGED FRACSPROJECT UNPV (@ 4.5 $/GJ) AND UDC v WELL SPACING
PROJ NPV UDC
Optimum
Field NPV
Optimum Unit
Cost ($/GJ)
Selection methodology
Screening an optimum ‘Concept’ depends on the Value-Metric
that is optimised
Workflow is
replicated for
well spacing
and
technology
For an
integrated
selection
Selection
conducted at
sector level
-800,000,000.0
-600,000,000.0
-400,000,000.0
-200,000,000.0
0.0
200,000,000.0
400,000,000.0
600,000,000.0
800,000,000.0
1,000,000,000.0
0.00 0.50 1.00 1.50 2.00 2.50
FIELD XProject Undeveloped NPV v Well Spacing for Technology
Screening
Alternative
technologies
tested
Combined
selection of
spacing and
technology
Selection methodology
Combined spacing and technology selection – an illustration
Dev
Fracc’ed
1.0 km
spacing
Sectorisation of CBM fields Key to economic optimisation
Selection of sector based on
clustering of higher / lower
potential Value-Metric outcomes
SECTORISATION METHODOLOGY
Assign technology
models (skin)
Sector pre-selection-
Alternative scenarios
Non -technical factorsTechnical considerations
Ref Field property mapReserves Categories
Permit Outlines
Facilities Unit Sizes
Run multi - well vs.
multi -technology
cases
Determine
optimum well /
well concept
Field
Level
Analysis
Sector
Level
Analysis
Comparative
Economical
Modelling
Iterate and finalise sector selection
For each well within the considered sector
Run Sector
statistics
Determine sector
optimum well
concept
Identify potential
sector definiton
refinement
Sector 2 Medium Value Metric
Sector 3 Low Value Metric
Sector 1 High Value Metric
Why managing cost is so important for CBM Illustration of the value creation as a combination of increased developable area,
down-spacing and PVed-cost reduction gains
Value delivered vs. Cost structure As Unit Cost reduces, value increases in
steps as new areas become economic
New areas
become
economical
Value creation within
developable area through
down-spacing and NPV
gain through cost
reduction
Sectorisation and areal analysis
is key to optimisation
Value creation
$ NPV
Low Unit Cost High
Gas Price
Conclusions
Conducting CBM field development studies
– Some learnings from Conventional ‘classical’ uses of technology and processes
– But a lot of very specific differences, under a shroud of variability
– Reliant on geostatistics to a large extent
Not all statistics
– Sweet-spotting of net coal thickness locally can positively impact appraisal and reserves build, but also potentially development
– Deliverability and drainage optimisation achieved through integration and mapping of data at different scales
• Lots of uncertainty doesn’t mean a lesser need for rigour
– Do more to assess the consequences of uncertainty at a given time, especially for future concept select refinements
– Trade complex models (3D numerical – where possible) for efficient nested workflows with multi-well material balance