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 abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE 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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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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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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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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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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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.

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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.

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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.