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SPE 164820
Probabilistic and Deterministic Methods: Applicability in
Unconventional Reservoirs C. Coll, BG, S. Elliott BG
Copyright 2013, Society of Petroleum Engineers This paper was
prepared for presentation at the EAGE Annual Conference &
Exhibition incorporating SPE Europec held in London, United
Kingdom, 1013 June 2013. This paper was selected for presentation
by an SPE program committee following review of information
contained in an abstract submitted by the author(s). Contents of
the paper have not been reviewed by the Society of Petroleum
Engineers and are subject to correction by the author(s). The
material does not necessarily reflect any position of the Society
of Petroleum Engineers, its officers, or members. Electronic
reproduction, distribution, or storage of any part of this paper
without the written consent of the Society of Petroleum Engineers
is prohibited. Permission to reproduce in print is restricted to an
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copyright.
Abstract
Unconventional resources are pervasive throughout large areas
and are not affected by hydrodynamic forces. In contrast with
conventional reservoirs, the discovery risk is typically low with
reservoir boundaries typically extending beyond the limits of the
acreage holding. When estimating reserves and resources, the major
uncertainty in unconventional reservoirs tends to be around the
local reservoir properties that control well production potential
and ultimate recovery. Very high areal variability of factors such
as permeability, well deliverability, saturation state, rock
mechanical properties exists. Accordingly, field appraisal is
continuous as the field is developed to help understanding
reservoir heterogeneity dictates the initial production from the
wells and decline rates. Technological advances help optimizing the
mechanical efficiency of well operations improving the economic
viability of these resources through increases in well rates and
ultimate recoverable volumes accessed by each well as demonstrated
by the successful implementation of new fraccing technologies in
shale gas reservoirs.
The new SEC1 rules adopted in 2009 allow the application of
probabilistic methods for reserves and resources estimations. In
unconventional reservoirs different stages of maturity exist as
described by SPE PRMS2, COGEH3 and SPEE4 Monograph 3 guidelines.
The COGEH guidelines are based on deterministic methods whereas
SPEE Monograph 3 guidelines are mainly focused on probabilistic
methodologies to use for reporting reserves in resource plays (CSG,
Shale, Tight Gas/Oil and Basin- centered Gas Systems) particularly
how to estimate proved undeveloped reserves in areas where enough
drilling and production exist. COGEH3 (volume 3) provides valuable
guidance on deterministic estimation of reserves and resources for
coal bed methane (CBM) and Bitumen/SAGD consistent with the
definitions provided in COGEH Volumes 1 and 2. In November 2011 the
new Guidelines for Application of the Petroleum Resources
Management System were published by the SPE5. These new guidelines
used the 2001 original guidelines as the starting point updating
significantly two new areas: Estimation of Petroleum Resources
Using Deterministic Procedures and Unconventional Resources. The
2011 SPE guidelines cover more extensively unconventional
reservoirs describing the reservoir characteristics, extraction and
processing methods, assessment methods, commercial and
classification issues for heavy oil, bitumen, tight gas formations,
coalbed methane, shale gas, oil shale and gas hydrates. This paper
provides some guidance on best practices on the applicability of
deterministic and probabilistic methods to estimate reserves and
resources for unconventional reservoirs based on the maturity of
the resource play and existing industry guidelines.
Introduction A large focus exists on unconventional resources
due to the very large potential volumes that exist in these types
of reservoirs around the world. Unconventional resources include
shale gas and oil deposits, coalbed methane, heavy oil and bitumen,
tight gas, basin-centered gas systems and gas hydrates. Each of
these types of play requires unique strategies and technological
advances to develop and must meet increasing challenges of product
prices. In the US7 conventional reserves and resources have been
declining in the last 40 years and replaced by unconventional gas
reserves and resources mainly tight gas, shale gas and CBM (see
Figure 1). Specialized evaluation techniques are required for
estimating the in-place estimates in unconventional reservoirs
which may be different from those applied to conventional
reservoirs. Comprehensive appraisal and development programs should
include
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pilot programs required to evaluate the technical and commercial
viability of these resources. The resource evaluation process
starts with the original-in-place estimates to define the areas
that may be potentially recovered by a defined development plans
and recovery mechanism. Exploration/appraisal drilling and testing
may have identified the presence of a large discovered resource
already and/or the discovered resource has been judged to have
production potential based on analogues. The discovery risk is
often small. The main challenge to develop these resources is to
identify and economically screen recovery processes to discriminate
between technically feasible resources and potentially economic or
commercial recoverable quantities. For unconventional reservoirs
similar classification systems to conventional reservoirs can be
used (Figure 2). These systems include prospective resources,
estimated contingent resources at discovery, followed by reserves,
with maturation linked to the phases of the resource play8,9. As
the resource play matures and technologies are screened the
development projects are better defined. Sections of the estimated
resource volumes may be assigned to the contingent resources
subclasses using for instance SPE PRMS2 guidelines that recognize
the technical and commercial maturation towards reserves. Because
unconventional accumulations are pervasive and developed with
high-density drilling, well counts are typically large making
statistical analysis of well performance feasible. As a result
probabilistic techniques may be more appropriate to understand the
uncertainty ranges of estimated ultimate recoveries per well and
associated confidence levels. Extrapolation of results requires
careful consideration of the geology and engineering
characteristics of a particular area to predict future well
productivity. This analysis could be more intricate than in
conventional reservoirs due to the short production history and the
particularly complex displacement mechanisms that may be happening
in these reservoirs. In many cases the challenge is to locate and
identify through drilling sweet spots with better reservoir
properties (e.g. permeability or porosity or mechanical rock
properties) than the rest of the areas making more feasible an
economic development. If the recovery processes have been confirmed
as not technically feasible, the in-place volumes need to be
classified as discovered unrecoverable and may remain like this
until new technology is available in the future.
Figure 1: Proved natural gas reserves for the USA, as derived
from DOE/IEA annual totals (2011 data) For the estimation of
resources in both conventional and unconventional reservoirs it is
important to understand the impact of the reservoir uncertainties
and the impact of development plans. Geological uncertainties like
gross rock volume, porosity, permeability, hydrocarbon saturation,
and reservoir continuity have a large impact on in place volume
(GIIP, OIIP). Engineering uncertainties (e.g. relative
permeability, capillary pressures, viscosities, aquifer properties)
impact the physical processes in the reservoir during the
production of hydrocarbons determining gas/oil recoveries and
finally the ultimate reserves/resources. There are dependencies in
some of the parameters that will need to be built in some of these
uncertainties to represent interaction between parameters (e.g.
porosity and permeability). Deterministic methods use a single
value for each parameter used in the resources estimation, for
instance in the in-place volumes and recovery factors. The
prospective resources can be classified as Low, Best and High with
discovered resources
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SPE 164820 3
volumes classified as 1C, 2C and 3C. The reserves volumes can be
then classified as Proved, Probable or Possible in the incremental
approach, or 1P, 2P, 3P in the cumulative approach, depending on
the level of uncertainty. Each of these categories typically relate
to specific areas or volumes in the reservoir. The estimation of
the low and high using deterministic methods is usually difficult
because when selecting parameters in deterministic models
practitioners tend to select and aggregate the upside of the
different parameters or the downside creating reservoir models
which are often much less than a P90 or much more than a P10
confidence levels required. In probabilistic methods the
statistical uncertainty of individual reservoir parameters is used
to calculate the statistical uncertainty of the in-place and
recoverable resource volumes. Probabilistic methods are tailored to
handle uncertainty in the different parameters required to estimate
reserves and resources. Estimations of recoverable resource
quantities using probabilistic methods allow the inclusion of the
associated uncertainty in each parameter that applies to both
conventional and unconventional resources. Existing Guidelines
Different organizations have focused their efforts to standardize
the definitions of petroleum resources for both conventional and
unconventional resources. Early guidance on reserves only existed
for proved reserves. The Society of Petroleum Engineers (SPE) was
pivotal supporting the standardization of reserves classification
achieved in 1997 when the SPE and the World Petroleum Council (WPC)
jointly approved the Petroleum Reserves Definitions. The SPE
updated the definitions in 2000 also approved by SPE, WPC, and the
American Association of Petroleum Geologists (AAPG) as the
Petroleum Resources Classification System and Definitions. These
were subsequently updated in 2007 and approved by SPE, WPC, AAPG,
and the Society of Petroleum Evaluation Engineers (SPEE) as the
2007 Petroleum Resources Management System, globally known as PRMS
(Figure 2). PRMS has been acknowledged as the oil and gas industry
standard for reference and has been used by the US Securities and
Exchange Commission (SEC) as a guide for their updated rules,
Modernization of Oil and Gas Reporting, published 31 December 2008.
Resources and resources definitions in SPE PRMS2 are appropriate
for all types of petroleum accumulations regardless of their
in-place characteristics, extraction method applied, or degree of
processing required.
Figure 2: PRMS Resource classification network The Canadian Oil
and Gas Handbook (COGEH), Volume 1, Section 5 published in 2002
(revised in 2007) is used as the standards when preparing
evaluations for public disclosures under the Canadian legislation,
National Instrument 51-101 Standards of Disclosure for oil and gas
activities. The COGEH Volume 3 published in 2007 contains detailed
guidelines for the estimation and classification of Coal Bed
Methane (CBM) Reserves and Resources/International
Properties/Bitumen and SAGD Reserves Resources. These guidelines
provide deterministic methodologies to estimate reserves and
resources in CBM. The Resources and Reserves classification system
in COGEH is very similar to SPE PRMS. In 2010 the SPEE (Society of
Petroleum Evaluation Engineers published the SPEE Monograph 3:
Guidelines for the Practical Evaluation of Undeveloped
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Reserves in Resources Plays which provided analytical techniques
and probabilistic methodologies to be used for resource plays to
evaluate undeveloped reserves. The UNFC-20096 (United Nations
Framework Classification) is a classification system that is
applicable to both minerals and petroleum sectors. It is a generic
principle-based system in which quantities are classified n the
basis of there criteria in a three dimensional system (E=economic
and commercial, viability), F=field project status and feasibility
and G=geological knowledge). In November 2011 the new Guidelines
for Application of the Petroleum Resources Management System were
published by the SPE. The SPE recognized that new application
guidelines were required for the SPE PRMS that would supersede the
2001 Guidelines for the Evaluation of Petroleum Reserves and
Resources. The new guidelines are using the 2001 original
guidelines as the starting point updating significantly two new
areas: Estimation of Petroleum Resources Using Deterministic
Procedures and Unconventional Resources. The intent of the 2011
guidelines is to cover the areas that were previously absent in
2001, updating some areas to reflect current technology advances,
expanding the guidance on unconventional and providing useful
examples to the practitioner. Unconventional Resources SPE PRMS2
defines unconventional resources as resources that exist in
petroleum accumulations that are pervasive throughout a large area
and that are not significantly affected by hydrodynamic influences
(also called continuous-type deposits). Unconventional
accumulations are not significantly affected by hydrodynamic
influences, reliance on continuous water contacts and pressure
gradient analysis to interpret the extent of recoverable petroleum
may not be possible. In many cases the extracted petroleum may
require significant processing prior to sale (e.g., bitumen
upgraders). Conventional resources on the other hand are defined as
discrete petroleum accumulations related to a localized geological
structural feature and/or stratigraphic condition, typically with
each accumulation bounded by a down-dip contact with an aquifer,
and which is significantly affected by hydrodynamic influences such
as buoyancy of petroleum in water. To reduce the uncertainty in
reservoir properties in unconventional accumulations large sampling
density with wells and testing is required. Variations in reservoir
properties are likely to happen in these very large areas (for
instance permeability in CBM) requiring detailed geological
characterization using well data (Figure 3). This is clearly
crucial before any resources estimation exercise can start. SPE
PRMS2, COGEH3 and SPEE4 Monograph 3 emphasize the importance of
geological characterization. In SPEE Monograph 3 they are referred
to as the identification of the geological subsets - within the
Resource Play might be geologic areas separated by faults, regions
exhibiting different lithologies, or areas of changing fluid
properties. In many cases the geological sub-sets are already
related to sweet spots where economic viability has been
demonstrated and development plans are identified to proceed.
Figure 3. Geological heterogeneity unconventional reservoirs
Similar to improved recovery projects applied to conventional
reservoirs, successful pilots or operating projects in the
particular reservoir or successful projects in analogous reservoirs
may be required to establish a distribution of recovery
efficiencies for non-conventional accumulations. Such pilot
projects are important to evaluate both extraction efficiency and
the efficiency of unconventional processing facilities to derive
sales products. There four main stages in the evaluation process:
exploration, evaluation, delineation and development as discussed
by Hasket at al. (2005)8 and Chan et al. (2012)9. SPEE Monograph 3
identifies them as the four phases of maturity in a resource play:
early, intermediate, statistical and mature. Depending on the
maturity of the resource play different amounts of data are
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SPE 164820 5
available to the evaluators and therefore different
methodologies would be appropriate for reserves and resources
estimation. If well data is very limited deterministic methods
would be more appropriate. If large datasets with enough production
data exists then probabilistic methods could be used. Early Phase
is typically characterized by geological exploration, wide well
spacing, and minimal well production. Since production data is
sparse deterministic methodologies like the ones proposed in COGEH3
are best suited. In the intermediate phase, well counts have
increased significantly and many new wells are exploiting areas
around existing production. Total well count is usually high, but
many of these wells are still not Analogous Wells according to the
SPEE Monograph 3 because operators continue to experiment with the
completion techniques. All these issues make statistical analysis
difficult but deterministic or hybrid methods10 are well suited.
Once enough well data is acquired statistical analysis becomes both
meaningful and useful and SPEE4 calls this phase the Statistical
Phase. Most wells are exploitation wells, although operators
continue to examine the effects of well spacing and minor changes
in completion procedures. In the Mature Phase the reservoir extent
is reasonably delineated and well density is very high. Well count
is obviously high enough for statistical analysis, but may not be
very helpful since most PUD locations are already infill locations.
Production interference may be noticed during this phase and more
sophisticated modelling methods including numerical simulation may
be applied to estimate reserves. Deterministic Methods
Deterministic methods should be used at the early and intermediate
phases of maturity where limited amounts of data exist and,
therefore, probabilistic methods are not well suited. As discussed
above there are two key aspects to consider during the evaluation
of unconventional resources. One is the detailed geological
characterization where geological subsets start to be identified.
Appraisal drilling for instance often reveals that even modest
folding can induce fractures that affect permeability. Where
folding is anticlinal permeability can be increased and where
synclinal the opposite can be the case. The main structural
features can be identified on seismic data but not the possibly
significant small scale faulting and folding present across the
area due to open line spacing. The folding can be enhanced by
compaction and draping. The structural features play an important
role in permeability and fracture distribution. Coal seams can be
discontinuous and permeability can be variable over short distances
(Figure 3). Rock properties and stress regime will control the
fracability in shale reservoirs. In shale gas reservoirs the
calcite or silica content, and hence brittleness, can be key in
producing successful frac zones (Figure 4). While developing shale
reservoirs ductile layers should be avoided because they will not
be areas where effective fracture networks can be created through
hydraulic fracturing in horizontal wells. Estimated Ultimate
Recovery (EUR) could be directly related to the efficiency of the
fracture network. Haskett et al.10 discussed the use of EUR
envelopes (Figure 4) to reflect uncertainty based GIIP and
recoverable volumes an area of potential (e.g. to reflect
stimulation and completion effect).
Figure 4. Example of uncertainty in EUR for shale gas
reservoirs
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Deterministic methods like the ones used in COGEH3 (volume 3)
are based on DSU by DSU spacing mining conventions and well spacing
rules. These are a legacy from old US SEC and N. American
regulations. In these methods proved undeveloped (PUD) are defined
within 1 drainage radii from producing or tested economic well.
Probable is typically immediately adjacent to proved DSUs and
typically corresponds to 2 drainage radii away from PUDs, whereas
possible is 2-3 drainage radii away from producing or tested
economic well (Figure 5).
Figure 5. Example reserves classes haloes around drilled wells
Resources are defined beyond the possible areas and up to 6 section
radius with reasonable expectation of economic production if no
faults or geological barriers are encountered. This approach helps
the evaluator to restrict the areas that can be claimed as
discovered resources after drilling exploration and appraisal wells
leaving the rest of the areas as prospective resources until wells
are actually drilled and hydrocarbons discovered (Figure 5). Some
level of extrapolation is allowed by COGEH for reserves by the use
of bracketing to extrapolate between proved undeveloped locations
(Figure 7).
Figure 7. Use of bracketing methods from COGEH and example from
GIS mapping system This approach is also consistent now with the
SEC rules1 that state Reserves in undrilled acreage shall be
limited to those directly offsetting development spacing areas that
are reasonable certain of production when drilled, unless evidence
using
Dev elopment well
D I S C. R E S O U R C E S
POS
P SR
D OB
D = Flowing Development/P ilot well completion
Exploration and Appraisal well
D I S C. R E S O U R C E S
POS
P SR
T OB
T = Succesfully Tested E&A well
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SPE 164820 7
Reliable Technology exists that established reasonable certainty
of economic producibility at greater distances. Reasonable
certainty means a high degree of confidence that the quantities
will be recovered. This allows locations beyond direct offsets to
be classified as proved undeveloped (PUD) locations under SEC rules
if they meet the SEC criteria of reasonable certainty. SPE PRMS
uses the same category of reserves as the SEC with proved, proved
plus probable and proved plus probable and possible and assigns a
P90, P50 and P10 confidence level to each category. It is
recognized under COGEH that bracketing based on geological and
performance continuity is possible and recommends using geological
knowledge and an integrated multidisciplinary approach to do this
(Figure 7). High certainty confidence areas are defined between
drilled and tested locations if no geological discontinuity or
engineering issue is detected that will stop extrapolation. A
Geographic Information System (GIS) can be used to estimate the
respective reserves areas on an OGIP per unit area map of proved,
probable, possible and discovered resources which can be multiplied
by a recovery factor derived from existing well estimated EURs or
analogue data. A number of different parameters can be imported
into the associated GIS attribute table including company
production rights and the net revenue interest (NRI) which applies
to each lease to determine net EURs. When only a limited number of
wells have been drilled it is appropriate to use a deterministic
method to calculate the reserves areas. A combination of
engineering methods should then be used to estimate EURs.
Integrated engineering methodologies should be used to estimate and
validate EUR estimates. Engineering methodologies consist of simple
extrapolation methods to rigorous model-based analysis including
decline analysis, semi-analytical techniques, model based analysis
(RTA) and flowing material balance formulations and/or other
analytical methods combined with rate transient analysis and
flowing material balance13,14. Some authors have published
intermediate approaches that we call Hybrid Methods for reserves
and resources evaluation that lie in between pure deterministic and
fully probabilistic. Baker et al.9 published a method considered to
be SPE PRMS compliant, that falls into this category (Figure 8).
The methodology used can still employ well development spacing
conventions like COGEH 9 (yellow areas) to estimate the areas of
confidence around the drilled wells. High, medium and low
confidence areas are mapped based on well information. Uncertainty
ranges are defined larger uncertainty in less well defined areas
and lower uncertainty in the areas bounded by drilling. EUR
uncertainty ranges are also defined and applied to each
corresponding confidence area. The main contribution of these
methods is that it actually provides the link between the reserves
areas and the approved development project as required by SPE PRMS
guidelines. In the example in Figure 8 the approved development
project spans over a 400 km2 area where reserves could be
attributed. The upside case is 600 km2 where the project is not yet
approved (not reserves but contingent resources). This method lends
itself to the use of probabilistic methods.
Figure 8. Hybrid Methods to estimate reserves and resources
based on confidence areas Probabilistic Methods SPE PRMS and the
new SEC rules issued in 2009 both allow the use of probabilistic
methods for reserves estimations and specifically state that if
probabilistic methods are used, there should be at least a 90%
probability that the quantities actually
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recovered will equal or exceed the 1P estimates. For 2P reserves
there should be at least a 50% probability that the actual
quantities recovered will equal or exceed the 2P reserves estimates
whereas for 3P there must be at least a 10% probability that the
actual quantities recovered will equal or exceed the sum of proved,
probable, and possible estimates. Reservoir uncertainties can be
defined by a probability distribution. When Monte Carlo methods are
used to generate probability functions, a random sampling of the
input reservoir uncertainty distributions is performed to estimate
the in place estimates (Figure 9). Distributions of EURs should be
generated using independent integrated engineering methodologies
used to generate recovery factor distributions. Different
categories of reserves or resources are estimated using the final
probability distribution by selecting the confidence levels (P90,
P50 and P10) required for each reserve category (1P, 2P and 3P).
When using deterministic methods the estimation of the downside
(low) and upside (high) recoverable reserves is difficult because
when selecting parameters in deterministic models practitioners
tend to select and aggregate the upside of the different parameters
or the downside creating reservoir models which are often much less
than a P90 or more than a P10 confidence levels. Resulting
cumulative probability functions are then used for various
quantitative risk analysis and decision making methods to optimize
development plans. This demonstrates the enormous value of
probabilistic methodologies helping to understand and quantify the
impact of major uncertainties and the confidence levels associated
with the reserves or resources estimates.
Figure 9. Probabilistic Methods There are more sophisticated
probabilistic methods like Experimental Design 15,,16, (ED or DOE)
and Global Optimization Methods 17,18,19,20 (GOM) that have become
increasingly popular methods used for probabilistic
reserves/resources estimations in conventional reservoirs.
Experimental Design techniques are used to reduce the number of 3D
reservoir models that would need to be run to correctly quantify
the output response and the probability distribution of reserves.
These methods work by defining the combination of parameters to be
input in the 3D reservoir models that will help to sample the
response under study (e.g. gas and oil ultimate recoveries).
Results of these simulations are used to generate an approximate
analytical model called Response Surface Model (RSM) that relates
the response to the key uncertainties. These analytical models can
act as a reasonable proxy for the simulator and will be used for
predicting the responses that different combinations of input key
uncertainties can have. They are also used to build the cumulative
distribution response curves16. The use of some of these
methodologies in unconventional reservoirs is rare because of the
need for sophisticated reservoir models which are typically not
available in unconventional reservoirs due to the size (number of
wells) and complexity of the numerical models required (e.g.
multiple frac horizontal wells for shale gas). The computational
requirements (cost and time) of these methods have reduced the
enthusiasm of practitioners. Instead a combination of in place
conventional mapping methods and simpler engineering methods to
define the EUR distributions has become increasingly popular. The
emphasis is in validating the EUR estimates through integrated
engineering techniques using consistent workflows (RTA, Flowing
material balance, decline or other analytical methods).
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SPE 164820 9
The SPEE Monograph 3 proposes a probabilistic and statistical
methodology to estimate the EUR to use for proved undeveloped
locations in a resource play. The methodology relies on the use of
existing producing wells to estimate the EUR in undrilled locations
using probabilistic and statistical methods as reliable technology.
Under SEC reliable technology must be tested for repeatability and
demonstrated to be predictive. Exhaustive statistical tests are
required using analogous well datasets (continuous geological area
and similar completion methods) before this method can be used to
estimate EURs for proved undeveloped reserves. This methodology
relies on the assumption that in unconventional reservoirs wells
may exhibit very different EURs due to local heterogeneity but
there is much more homogeneity when groups of wells are considered
(resource is homogeneous at the larger scale). The methodology also
requires enough well and production data to support statistical
analysis. The SPEE methodology advises the practitioner to follow 5
different steps:
1. Identify analogous wells 2. Create a statistical distribution
for the analogous wells 3. Determine the drill opportunities (drill
count) 4. Prepare a Monte Carlo simulation 5. Calculate proved,
probable and possible reserves using appropriate definitions
Its important to emphasize that the geological sub-set should be
previously defined using all the geological information available
(Figure 10). Appraisal drilling often reveals that different
geological sub-sets may exist, separated by faulting or other
structural features playing an important role in permeability and
fracture distribution. Faults can act as barriers to production
between wells and connectivity has to be demonstrated by production
pilots. Production pilots are very important to define whether
there is communication across faults situated within the evaluation
area. For instance in shallow coal seams there is a chance of gas
desaturation and water influx. The up-dip limit of viable CSG
reservoirs might be, say, at ~100m depth below surface. The down
dip limit could be where compaction has been sufficiently high such
that permeability does not allow economic production without
stimulation. These limits define the productive reservoir and can
be mapped out with outcrop studies and core data from wells.
Permeability threshold contour maps can be overlaid on to the depth
contours to define the productive reservoir and a geological
sub-set. Differences in fluid types and pressure regimes need also
to be evaluated before the study area is defined21.
Figure 10: Geological sub-set Once the geological sub-set is
defined, analogous wells (step 1 above) should be examined. Figure
11 shows an example of EUR distributions defined from analogous
proved developed producing wells. Differences in well performance
due to completion techniques need to be clearly understood and
examined. To estimate the EURs per well it is recommended to
perform a comprehensive analysis using a combination of integrated
engineering methods13,14. In the case of shale gas for instance, a
combination of decline analysis methods matched to actual
production performance, rate transient analysis and
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flowing material balance should be used to reduce the
uncertainty and QA/QC the EUR per well to be used. As observed in
Figure 11, the evaluator should identify if the EUR distributions
for the different generations of wells have a similar shape and
compare the mean estimates and the P90 over P10 ratios. In some
cases the analysis may reveal the existence of wells with different
EUR distributions not because of technology changes during drilling
and completion but because the wells are in a different geological
sub-set in which case it would be advisable to re-evaluate the
extent of the geological sub-set to understand from the geological
or engineering point of view the reasons for the differences.
Figure11. EUR distributions in the Barnett Shale
SPE Monograph 3 provides guidance on the number of producing
wells required to have the required sample size based on the
dispersion of the EUR distributions (P90/P10) in the analogue wells
in the geological subset. A statistical distribution of EURs for
all analogue wells in the geological sub-set is required. The
P10/P90 ratio is used to establish the minimum recommended sample
size. A number of statistical tests need to be performed on the
analogous wells. The tests will start by selecting a group of
randomly distributed anchor wells that satisfies the minimum
recommended sample size. A statistical distribution for these wells
is generated and compared to that of the analogous wells. The
statistical distribution should be similar to that of the analogue
wells. Once this is confirmed subsets of wells that are part of the
analogue wells but are not anchor wells needs to be selected with a
minimum sampling size. The statistical distribution for the new
sub-set wells (selected for instance following concentric circles
away from the anchor wells) should be similar to the anchor and
analogue well distribution. The test of similarity should be based
on a Pmean differences (e.g. +/-10 %) for the different sets. The
extent of the proved area is controlled by the test of the mean
value in the sub-sets. Once the test fails the extrapolation away
from existing producers should stop. Step 3 is particularly
important because the SEC rules require that proved undeveloped
reserves can only be accounted for locations that are in the
development plan and that will be drilled within 5 years and with
project commerciality demonstrated by positive economics. Step 4
uses Montecarlo methods to calculate the distribution of the EUR to
use in the proved undeveloped (PUD) locations using the well counts
determined by Step 3. This step may require that the practitioner
performs actual aggregation of well distributions (Madhav et
al.20). SPE Monograph 3 provides some guidance on aggregation
factors. Step 5 estimates the reserves in the undeveloped locations
using the results of the confidence levels obtained from Step 4.
The methodology described in SPE Monograph 3 is complex and
requires comprehensive well counts, well by well performance
analysis, statistical analysis and geological/engineering analysis
of the study area to validate the geological sub-set criteria. It
is based on the assumption that existing production wells can be
used to predict the performance of undrilled locations away from
the direct offset wells. A key aspect of this analysis is how
robust the EUR estimates are reason why evaluators should promote
the use of integrated engineering methods to improve the estimation
of EURs and uncertainty ranges which forms the basis of this
approach. In areas with limited production data and therefore large
uncertainty in EUR caution should be exercised. The assumption of
repeatability of EUR distributions in analogous wells needs to be
validated
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SPE 164820 11
with the sub-sets. All the methodology relies on the definition
of the geological sub-set which should be done as part of a
multidisciplinary exercise. This is also the case when simple
deterministic methods are used and assumptions will need to be made
regarding how relevant a well data point is for estimating EURs in
an undrilled locations. The statistical analysis is only valid if
enough sampling exists (Statistical play) so enough wells should be
on production with enough production history to reduce the
uncertainty on EURs. Conclusions Reserves and resources evaluation
methods in unconventional reservoirs are different from
conventional reservoirs. Existing guidelines have evolved in the
last decade with new guidelines being issued in the last 10 years
like SPE PRMS, COGEH, SPE PRMS application Guidelines and SPEE
Monograph 3 providing guidance to help with the evaluation of
unconventional resources. Based on the phase of the resource play a
combination of deterministic/hybrid and/or probabilistic methods
may be required with deterministic methods evolving towards
probabilistic methods as the resource play evolves from the early
phase to the mature phase. To define the best methodology to use in
a particular area the evaluator should first identify the phase of
the resource play and evaluate the amount of data available for the
analysis. Deterministic methods are usually recommended during the
early phases with COGEH guidelines providing a good foundation for
the application of these methods. Deterministic methods are easy to
implement and to review. However deterministic estimates do not
relate clearly to defined probabilities (P90, P50, P10) required
for reserves and resources estimations classification. During the
intermediate phase there may be enough data to use either
deterministic and/or hybrid methods. Probabilistic methods can only
be used when enough well production data exists. Probabilistic
methods recommended by the SPE Monograph 3 require large amounts of
data and complex analysis methods but provide the confidence levels
associated with each resource and reserves category. A key outcome
of the probabilistic methods is the ranking of reservoir
uncertainties to evaluate the impact of the different uncertainties
in resources and reserves. These uncertainties can be ranked and
examined which should help to understand and quantify project risk.
Industry efforts will be required to harmonize guidelines for
unconventional resources providing the evaluator with a complete
set of guidelines that cover the different phases of maturity in a
resource play. References
1. Modernization of Oil and Gas Reserves Reporting. [Release Nos
33-8995; 3459192; FR-78; File No. S7-15-08]. SEC Website, December
2008.
2. SPE/WPC/AAPG/SPEE-SPE PRMS Petroleum Resources Management
System. 2007.
http://www.spe.org/industry/docs/Petroleum_Resources_Management_System_2007.pdf.
3. Canadian Oil and Gas Evaluation Handbook (COGEH Volumes
1,2,3) co-authored by the Society of Petroleum Evaluation Engineers
(Calgary Chapter) and the SPE Canada (formerly the Petroleum
Society of the Canadian Institute of Mining CIM.
4. Society of Petroleum Evaluation Engineers (SPEE), Guidelines
for the Practical Evaluation of Undeveloped Reserves in Resource
Plays, Monograph 3, 2010.
5. SPE/AAPG/WPC/SPEE/SEG PRMS, Guidelines for Application of the
Petroleum Resources Management System, November 2011.
6. United Nations Framework Classification for Fossil Energy and
Mineral Reserves and Resources-2009 (UNFC-2009), United Nations,
ECE Energy Series N0 39,E.10.11.E.15
7. Weijermars, Ruud, Alboran Energy Strategy Consultants and
Delft University of Technology, paper presented at the 2012 SPE
Economics & Management.
8. Haskett, W.J. Brown, P.J. Decisions Strategies, Evaluation of
Unconventional Resource Plays, SPE 96879, paper presented at the
2005 SPE Annual Technical Conference and Exhibition held in Dallas,
Texas, October 2005.
-
12 SPE 164820
9. Chan, SPE, AJM Petroleum Consultants; John R. Etherington,
SPE, PRA International; Roberto Aguilera, SPE, University of
Calgary Schulich School of Engineering, A Process To Evaluate
Unconventional Resources , SPE 134602-MS, paper presented at the
2012 SPE Economics and Management Symposium.
10. Geoffrey J Barker, SPE, Resource Investment Strategy
Consultants (RISC Pty Ltd), SPE 117124, Application of the PRMS to
Tight Gas and Coal Seam Gas Projects, paper presented at the SPE
Asia Pacific Oil and Gas Conference and Exhibition, 20-22 October
2008, Perth, Australia.
11. W.J. Haskett, SPE, and P.J. Brown, SPE, Decision Strategies
Inc. , SPE 96879, Evaluation of Unconventional Resource Plays.
Paper presented at the SPE Annual Technical Conference and
Exhibition, 9-12 October 2005, Dallas, Texas.
12. William J. Haskett, SPE, Decision Strategies Inc., and P.
Jeffrey Brown, ExplAnalysis, Inc., SPE 135208, Pitfalls in the
Evaluation of Unconventional Resources, paper presented at the SPE
Annual Technical Conference and Exhibition, 19-22 September 2010,
Florence, Italy.
13. D. Ilk, Texas A&M University; A.D. Perego and J.A.
Rushing, Anadarko Petroleum Corp.; and T.A. Blasingame, Texas
A&M University, SPE 114947, Integrating Multiple Production
Analysis Techniques To Assess Tight Gas Sand Reserves: Defining a
New Paradigm for Industry Best Practices, paper presented at the
CIPC/SPE Gas Technology Symposium 2008 Joint Conference, 16-19 June
2008, Calgary, Alberta, Canada.
14. V. Okouma, Shell Canada Energy, D. Symmons, Consultant, N.
Hosseinpour-Zonoozi, D. Ilk, DeGolyer and MacNaughton, and T.A.
Blasingame, Texas A&M University, Practical Considerations for
Decline Curve Analysis in Unconventional Reservoirs -Application of
Recently Developed Rate-Time Relations, SPE 162910 papare resented
at the SPE Hydrocarbon Economics and Evaluation Symposium, 24-25
September 2012, Calgary, Alberta, Canada.
15. Christopher D. White, SPE, Louisiana State U. and Steve A.
Royer, Shell Exploration and Production Co.: Experimental design as
a Framework for Reservoir Studies, paper SPE 79676 presented ate
the SPE Reservoir Simulation Symposium, Houston, February 2003.
16. E. Manceau, M. Mezhani, I. Zabalza-Mezghani and F. Roggero,
IFP: Combination of Experimental Design and Join Methods for
Quantifying the Risk Associated with Deterministic and Stochastic
Uncertainties- An Integrated TesStudy, paper SPE 71620 at the 2001
SPE Annual Technical Conference and Exhibition in New Orleans,
2001.
17. Ralf Schulze-Riegert, SPE, and Markus Krosche, Scandpower
Petroleum Technology; Abul Fahimuddin, Inst. of Scientific
Computing TU Braunschweig; and Shawket Ghedan, SPE, The Petroleum
Inst. Abu Dhabi, : Multiobjective Optimization With Application to
Model Validation and Uncertainty Quantification, SPE105313, paper
presented at the SPE Middle East Oil and Gas Show and Conference,
11-14 March 2007, Kingdom of Bahrain
18. Griess, OMV, and A. Diab and R. Schulze-Riegert, Scandpower
Petroleum Technology: Application of Global Optimization Techniques
for Model Validation and Prediction Scenarios for a North African
Oil Field, paper SPE 100193 presented at the SPE Europec/EAGE
Annual Conference and Exhibition, 12-15 June 2006, Vienna,
Austria.
19. M. K. Choudhary, SPE, and S. Soon, SPE Chevron Energy
technology Co., and B.E. Ludvigsen, Scandpower PT. : Application of
Global Optimization Methods in History Matching and Probabilistic
Forecasting- Case Studies paper SPE105208 presented at the 15th SPE
Middle East Oil & Gas show and Conference, Bahrain 11-14 March
2007.
20. Schulze-Riegert, R. W.., Haase, O. and Nekrassov, A.:
Combined Global and Local Optimization Techniques applied to
History Matching, paper SPE 79668, 2003
21. Kulkarni, Madhav M.; Cox, Stuart A.; Woods, Marcelyn E.; Van
Meter, Gregory M.; Jensen, Timothy, R.; Altemus, Rebecca L.;
Marathon Oil Corporation, SPE 159174, Quantifying Proved
Undeveloped Reserves in the Woodford Shale: A Seamless Integration
of Statistical, Empirical, and Analytical Techniques, presented at
the SPE Annual Technical Conference and Exhibition, 8-10 October
2012, San Antonio, Texas, USA.