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Net Pay: What is it? What does it do? How do we quantify it?How do we use it?Paul F. Worthington, SPE, Gaffney, Cline & Associates 

Copyright 2009, Society of Petroleum Engineers

This paper was prepared for presentation at the 2009 SPE Asia Pacific Oil and Gas Conference and Exhibition held in Jakarta, Indonesia, 4–6 August 2009.

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 beenreviewed 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, itsofficers, 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 toreproduce 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

A knowledge of Net Pay is important in the volumetric estimation of hydrocarbon Resources, a practice that underpins thevalue of the petroleum industry. Yet, there is no universal definition of Net Pay, there is no general acceptance of its role in

integrated reservoir studies, there is no recognized method for evaluating it, and there are disparate views on how to make use

of it. Partly for these reasons, Net-to-Gross Pay constitutes a major source of uncertainty in volumetric Resources estimates,

second only to gross rock volume. With the aim of improving this unsatisfactory state of affairs, this presentation charts a

critical path of Net-Pay understanding and application by exploding some of the unhelpful myths that abound within theindustry and replacing them with a defensible rationale to guide the quantification of Net Pay (thickness). Central to this

 process is the identification of Net-Pay cut-offs, themselves the subject of much controversy over the years. The approach is

data-driven, in that it uses what we know, and also fit-for-purpose, in that it takes account of reservoir conditions. Theoutcome is a sounder basis for incorporating Net Pay into volumetric estimates of ultimate recovery and thence petroleum

Resources.

Introduction

 Net Pay is a key parameter in reservoir evaluation, because it identifies those penetrated geological sections that havesufficient reservoir quality and interstitial hydrocarbon volume to function as significant producing intervals. It contributes

to the estimation of a meaningful in-place volume against which recovery efficiency can be usefully assessed. Thus, Net Pay

is central to the “static volumetric” method of estimating ultimate recovery. Moreover, it demonstrably facilitates reservoirsimulation, because non-reservoir rock does not need to be characterized for inclusion. Yet, there are no industry-standard

definitions of Net Pay and no generally-accepted protocols for its incorporation into reservoir models as a basis for resource

estimation. These matters were articulated by Caldwell & Heather (2001).

“Despite net pay being a fundamental input into not only volumetric reserves calculations, but also well test analysis and

 predictive calculations, there is surprisingly little in the way of insightful guidelines on different computational methods and

their strengths and weaknesses.”

This paper examines where we are in terms of quantifying Net Pay using determined formation properties and it promotes an

improved methodology that does not have the drawbacks of traditional approaches. These matters are especially important

during the early stages of field life, when uncertainty in estimated petroleum Resources is greatest.

Petroleum ResourcesA subsurface petroleum discovery is confirmation through drilling and downhole measurements of sufficient movable

hydrocarbon volumes to be of potential interest as an exploitable resource. Historically, the terms “resources” or “totalresources” have been taken to include all hydrocarbon volumes in the subsurface: discovered and undiscovered; recoverable

and unrecoverable; remaining and produced; economic and uneconomic; commercial and non-commercial. However, here

the definitive term “Resources” is used, pursuant to the Petroleum Resources Management System (Society of PetroleumEngineers et al. 2007).

There are three classes of Resources, and all of these relate to recoverable hydrocarbon volumes. The term “Prospective

Resources” applies to the exploration stage in that it admits undiscovered recoverable volumes. The term “Contingent

Resources” notionally relates to the appraisal stage by including only discovered recoverable volumes, but these may be

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economic or uneconomic. The term “Reserves” relates to the development stage. It includes only those volumes that are

discovered, remaining (in the subsurface), recoverable, and commercial. Commerciality means that the hydrocarbon volumes

are economically exploitable and that there is a commitment to produce. The commitment is manifested through adevelopment plan. Progression through the classes is governed by project maturity. To progress from Prospective Resources

to Contingent Resources requires a discovery. To progress from economic Contingent Resources to Reserves requires a

commitment to develop in real time.

The estimation of discovered petroleum Resources is summarized in Table 1. Geology-based methods can contribute to

estimates of Contingent Resources or Reserves, according to project maturity. By their very nature, production-basedmethods lead to estimates of Reserves. This paper is set within the context of geology-based methods, in particular the

widely-used “static” volumetric approach.

Formation ThicknessesThe total thickness of a designated evaluation interval either along hole or in true vertical space is termed “Gross Thickness”.

The term “net thickness” indicates that some of the Gross Thickness has been removed. There are three types of netthickness: Net Sand; Net Reservoir; and Net Pay. Their interrelationship is summarized in Fig. 1. It can be seen that Net Pay

is a subinterval of Net Reservoir, which is, in turn, a subinterval of Net Sand. These thicknesses are used along hole for

completions purposes and in true vertical space for volumetric computations.

This classical terminology is rooted in the onshore history of the oil industry. It is in widespread use, but it is inexact.

 Net Sand would be better described as “Net Potential Reservoir”, a term that encompasses, for example, carbonates andfractured basement. Net Reservoir intervals contain rocks that have been identified as having a useful capability to store

fluids and allow them to flow. In this respect, the term “static volumetrics” is potentially misleading. Net Pay is a descriptorthat originates in single-well completions onshore, where technical and economic decisions are contemporaneous. Where

economic decisions are made on a field scale, a better term might be “Net Hydrocarbons”.

Terminology issues will merely be noted here and placed in abeyance. Note, however, that other conventions have been

 proposed. Some of these were collated and compared by Worthington & Cosentino (2005), who adopted the convention of

Fig. 1 as their reference. This adoption has subsequently been endorsed by Ringrose (2008). This paper will also adopt theconvention of Fig. 1.

In terms of the adopted convention, the ratio net-to-gross thickness takes three forms: Net-to-Gross Sand; Net-to-Gross

Reservoir; Net-to-Gross Pay. It is not appropriate to use the term “net-to-gross” without qualifying what it is.

What is Net Pay? Net Pay is a thickness with units of length. Net Pay can only be measured at a well. It is a subinterval within the Gross

Thickness. It comprises Net Reservoir rock containing a significant volume of hydrocarbons in place. There is no standarddefinition of Net Pay, even in a semiquantitative sense (Worthington & Cosentino 2005). Net-Pay subintervals are often

aggregated to give a total Net Pay and thence, by ratio to Gross Thickness, Net-to-Gross Pay. The quantification of Net-to-Gross Pay lies on the critical path to the estimation of ultimate recovery through the “static volumetric” method. However,

for engineering purposes, an aggregated Net-to-Gross Pay is not useful in itself: an inventory needs to be kept of exactly

where the pay intervals are located. Moreover, historically Net Pay has been a major source of uncertainty in geology-basedmethods of estimating ultimate recovery.

There is a body of opinion that the evaluation of Net Pay is based on somewhat arbitrary criteria and that Net-to-Gross

Pay, and therefore Net-to-Gross Reservoir, should be fixed at unity regardless of the nature of the geological succession andthe fluids they contain. It is further argued that an analysis of reservoir dynamics through a simulator will be sufficient to

quantify realistic recoverable volumes. Although it is true that some of the earlier methodologies have been arbitrary, a more

incisive response can be gleaned from the answers to the following key questions.

•  Do we believe that all rocks that host a given hydrocarbon accumulation are functional reservoir rocks?

•  Do we believe that all hydrocarbon volumes in an accumulation contribute significantly to the energy of the reservoir

system?•  Do we believe that all hydrocarbon volumes in an accumulation are potentially recoverable to a significant degree?

If the answer to one or more of these three questions is “No”, the identification of Net Pay has to be an integral part of any

formation-evaluation exercise.

What does Net Pay do?There is no universal perception of the role of Net Pay in geology-based reservoir studies. In essence, the Net Pay conceptleads to the identification of those sections of a reservoir that will contribute to reservoir performance. It excludes the rest.

Thus, Net Pay allows recovery efficiency to be evaluated meaningfully against an initial hydrocarbon volume that is

contained within reservoir rock. Otherwise, estimates of recovery efficiency can be distorted by the inclusion of non-

contributing volumes that will not be produced.

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 Net Pay, in conjunction with Net Reservoir, allows petrophysical algorithms to be established over those very same

intervals to which they are to be applied. Otherwise data from non-reservoir intervals might influence the establishment of

interpretative algorithms and thence degrade their application over Net Pay. Moreover, if there is no separation of NetReservoir and Net Pay from the Gross Thickness, it will be necessary to characterize the non-reservoir rock to the same

degree as the reservoir rock. Given that core analysis of poor quality rock is more expensive than for conventional reservoir

rock, it is a challenge to envisage this happening as part of the evaluation of conventional reservoirs in any cost-conscious

culture.

The identification of Net Pay and the associated elimination of non-reservoir rock form the basis for a more meaningfulinitialization of a reservoir simulator. Although there is a point of view that simulation is a catch-all that can account for

non-reservoir rock as well as reservoir rock, recent experience has confirmed that if Net Pay is systematically quantified, the performance of reservoir models is demonstrably improved in terms of more readily attainable history matches. Of course,

there are other factors that contribute to a sound initialization of a simulator, not least an integral geoscience model, taking

account of rock types, and honoring (grid-height) scale when establishing permeability predictors.

The Net-Pay concept has been challenged specifically in the case of tight gas layers that release hydrocarbons intoadjacent beds when a sufficiently large pressure differential has been established through primary depletion. Since those

tight gas sands may not be incorporated within Net Pay at the well bore, it has been argued that late onset recovery from these

sands renders a Net Pay protocol meaningless. The rebuttal to this argument calls for a return to basics. Net Pay can only be

measured at a well bore. It is a measure of the thickness of hydrocarbon-bearing reservoir rocks whose constituent fluids

express themselves significantly at the well bore. Late-onset recovery is initiated away from the well bore and the depleted permeable layers provide a conduit to the well. Thus, a permeable layer can show an inflated recovery factor, in some cases

above 100%, but this does not negate the Net-Pay concept, provided that the underlying recovery mechanism has beenrecognized.

How is Net Pay Quantified?The earlier literature alluded to the “picking” of Net Pay with a view to a particular application, so that the intended use

influenced how Net Pay was identified (e.g. Snyder 1971). Today, this exercise is largely automated with the possibleexception of single-well completions in real time. Net Pay is quantified through the use of petrophysical cut-offs that are

applied to well logs. Cut-offs are limiting values of formation parameters that remove non-contributing intervals. The role

and application of cut-offs in integrated reservoir studies have been discussed previously (Worthington & Cosentino 2005;

Worthington 2008). Traditionally, a shale volume fraction, V  sh, cut-off is used to identify Net Sand, a porosity, φ , cut-off is

additionally used to delineate Net Reservoir, and a water saturation, S w, cut-off is further used to define Net Pay.Perhaps the biggest argument that has been made against the introduction of cut-offs is the arbitrary nature of historical

approaches. It is true that there is no generally accepted method of identifying cut-offs. It is also true that certain rules of

thumb have existed in the petroleum industry for over 50 years and that some authors have even advocated generally-

applicable cut-offs for clastics, on the one hand, and for carbonates, on the other (Desbrandes 1981). However, more recentapplications have been data-driven, and these have formed the basis for an improved protocol by avoiding the use of industry

defaults and generic specifications. Further guidance can be gleaned from the answers to the following key questions.

•  Do we believe that a reservoir can be characterized solely in terms of its ability to store hydrocarbons?Yes – we can use static Net-Pay cut-offs.

 No – we must use Net-Pay cut-offs that also take account of reservoir dynamics.

•  Do we believe that all rocks have the same physicochemical character?Yes – we can use generic Net-Pay cut-offs.

 No – we must establish Net-Pay cut-offs for each identified rock type.

•  Do we believe that all reservoirs are produced in the same way?

Yes – we can use a universal approach to the generation of Net-Pay cut-offs. No – we must condition our Net-Pay cut-offs to the production mechanism.

If the answers to these three questions is “No”, the identification of Net Pay has to be dynamically conditioned and fit for purpose, i.e. it must take account of flow criteria, rock type and depletion mechanism.

The application of these principles to quantify Net Pay calls for an examination of porosity and permeability as

represented within a conventional core dataset in the light of the recovery mechanism and with appropriate honoring of scale.This approach has been described more fully by Worthington & Cosentino (2005). In essence, there are ten key stages:

•  Specify an evaluation interval in a well.

•  Select a reference parameter, such as a reservoir quality indicator (k /φ )0.5 for primary depletion or end-point relative permeability k ro(S wirr ) for waterflood depletion.

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•  Establish any data partitioning.

•  Identify a value of the reference parameter that corresponds to the lower limit of hydraulic behavior of the rock over a partitioned fraction of the evaluation interval. If in doubt, select the most all-encompassing lower limit that is

compatible with the principle. Adopt this limiting value as a “reference cut-off”.

•  For each partitioned dataset, relate the reference cut-off to a corresponding value of a conventional core analysis parameter such as porosity or Klinkenberg-corrected air permeability taking due account of scale where feasible.

Adopt this corresponding value as a “dynamically-conditioned cut-off”.

•  Using the principle of synergic cut-offs (Cosentino 2001), relate the dynamically-conditioned cut-off to correspondingcut-offs of log-derived porosity (where required), shale volume fraction, and water saturation.

•  Apply the cut-offs simultaneously to obtain Net Pay within the partitioned dataset: if the lower depth limit of theevaluation interval is a sharply-defined hydrocarbon-water contact or a hydrocarbons-down-to level, and there is no

 perched water, the resulting Net Reservoir and Net Pay will be very similar.

•  Obtain average log-derived porosity and (porosity-weighted) water saturation over the Net Pay thickness(es) of each partitioned interval.

•  Integrate the data from all partitioned intervals.

•  Repeat the process for all wells in the project database.

This process is illustrated schematically for conventional reservoirs in Fig. 2. Tangible examples have been provided by

Worthington (2008). The process has also been tracked by Egbele et al. (2005), albeit with different cut-off parameters for

identifying Net Pay.

If the available core data include special core analysis, there are other approaches that can be adopted. For example,capillary pressure measurements can be used in conjunction with conventional porosity and permeability data to ascertain the

critical porosity and permeability values for which the pore throats are too small to allow hydrocarbons to enter the rockduring migration. This analysis defines Net Reservoir, not Net Pay, because it is concerned with the issue of reservoir

 potential and not reservoir fluid content. Again, if both Dean-Stark extracted water saturations and relative permeability

measurements are available, these can be used to identify the critical saturation at which water flows in preference tohydrocarbons. It is also possible to use composite cut-off parameters such as bulk volume water (the product of porosity and

water saturation). If there are no core data available, recourse has to be made to a static analysis of cut-offs (e.g. Joshi &

Lahiri 1998) or to an informed use of analog discriminators. Having said that, neither of these approaches should be viewed

as satisfactory.

The above methods have to be varied for certain types of reservoir. These include laminated reservoirs, discrete stackedreservoirs, naturally fractured reservoirs, tight gas reservoirs, and coal-bed methane reservoirs.

Laminated Reservoirs. Sand-shale sequences constitute the biggest cause of overlooked pay in the world today. The problem is rooted in the inability of standard logging tools to resolve individual sand laminae. A partial solution is to use anelectrical micro-imager to identify the laminae and a tensor resistivity tool to quantify the resistivity of the sands. The

evaluation of sand porosity can be problematic because of inadequate spatial resolution of porosity tools and uncertainty

associated with the application of shale corrections to porosity logs. Where core has been recovered, and the thickness of thesand laminations is greater than the diameter of horizontal core plugs, the evaluation of sand porosity can be groundtruthed

 by judicious selection of core-plug locations. Otherwise, it is usually necessary to draw upon some kind of volumetric model

for shaly sands (e.g. Thomas & Stieber 1976). A noteworthy exception has been the use of minipermeametry to estimate the

 permeability of the sand laminae and a conventional porosity vs. permeability transform to estimate laminar porosity

therefrom (Flølo et al. 1998). This approach reverses the usual practice of estimating permeability from porosity. A furthercomplication is whether a minimum sand thickness is required to qualify as (recoverable) pay: this is especially important

when considering floodable pay, because a flood front might not be able to sweep layers of subcritical thickness.

Discrete Stacked Reservoirs.  Where multiple stacked reservoirs are separated by mudstones or shales of equivalent

thickness and the depositional system has rendered the succession congenital, the determination of Net Pay can use a single

set of (dynamically-conditioned) cut-offs and be referred to an overall Gross Thickness. However, the concept of a grossevaluation interval breaks down where stacked reservoir units are separated by very thick mudstones or other impermeable

sediments, perhaps more than an order of magnitude thicker than the reservoir units themselves, because Net-to-Gross Pay

will be misleadingly low. Instead, each unit should be regarded as a separate reservoir zone and Net-to-Gross Pay should be

evaluated for each zone at each well. Different sets of cut-offs may be needed if the reservoir units are not congenital. In any

case, the reservoir units should be mapped separately.

Naturally Fractured Reservoirs.  Natural fractures are hydraulically different from drilling-induced fractures. The latter act

as localized extensions of the borehole wall. As such, their presence leads to a better well efficiency over the affected

intervals. On the other hand, natural fractures can serve as regional conduits that transmit fluids from an intergranular“hinterland” to the well bore. The key parameter is the ratio of intergranular transmissibility to fracture transmissibility for a

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hydraulic unit. Where this ratio is high, the fractures augment the intergranular flow, and the Net-Pay concepts, as outlined

above, are applicable. In other words, the well-log scale is appropriate for Net-Pay evaluation. Where the transmissibility

ratio is low, the reservoir cannot function without the fractures, which dominate fluid flow. Here, reservoir performance isgoverned by the effective scale of the fracture network, and this can be much greater than the well-log scale. Therefore, what

can be measured at the well bore provides only a partial insight into reservoir performance. On this basis, such a naturally

fractured reservoir is better analyzed at the reservoir scale through simulation. Downhole activity reduces to the imaging of

fracture occurrence in conjunction with inflow analysis based on production logs, and it is beneficial in guiding completions.

Of course, many reservoirs lie between these two extremes. In such cases, dynamically-conditioned Net-Pay concepts should be used alongside fracture identification: Net Pay will include intervals that show good intergranular character with and

without fractures (cf. Aguilera 2003). In general, a “static volumetric” approach is likely to be inadequate as a stand-aloneapproach to Resource estimation where fracture flow is significant. Special care should be taken where natural fractures have

 been blocked through mineralization, because the sealed fractures can compartmentalize the reservoir with considerable

reductions in recovery. 

Tight Gas Reservoirs. It has long been recognized that traditional methods of reservoir evaluation can break down in tight

sands (e.g. Brown et al. 1981). There are four aspects that impact the quantification of Net Pay. The first is whether

conventional core analysis can be undertaken in tight gas sands with the same accuracy is in conventional reservoir rocks

and, if so, whether the data are as useful. For example, in poor quality rock, conventional air permeability can be orders of

magnitude greater than the true effective permeability to gas at minimal water saturation. The second question is whetherlogging tools are capable of delivering accurate parametric values of physical properties that are known to be related to

reservoir properties. In other words, does the tightness of the formation take standard well logs beyond their calibrationlimits? The third issue is whether log-analysis models are appropriate to tight formations, which are often characterized by

high capillarity and a high pore surface area. Finally, there is the matter of cut-off selection and whether these allow all

 potentially recoverable volumes to be represented, given that tight reservoirs can be markedly heterogeneous and

consequently recovery factors can be highly variable. The key is to approach data acquisition and analysis in a fit-for-

 purpose manner (e.g. Bennion et al. 2000).

Coal-bed Methane Reservoirs.  The identification of Net Pay in coal-bed methane (CBM) reservoirs is in its infancy. A

CBM reservoir is unusual in the sense that it constitutes both the source rock and the reservoir rock. Coal seams can be

recognized with standard logging tools as having a low gamma ray response, low density, high neutron porosity and highresistivity, even though they are often water-wet. Gas is adsorbed onto the internal structure of the coal and this phenomenon

allows substantial quantities to be stored. The gas can only be released when the reservoir pressure has dropped to a critical

desorption pressure, which is attained through water production. CBM reservoirs often have comparatively low matrix permeability. Gas is transmitted to natural fractures through a cleat network, which is usually a system of orthogonal joints.

The key to characterizing CBM reservoirs is to identify the effectiveness of the cleat network for, without a well-developedcleat system, coal seams are unlikely to produce gas at economic rates, even through a fracture network enhanced by

stimulation. Dipole sonic logs have discerned the development of a fracture network but they do not deliver quantitative

information about potential producibility. That information can only come from formation tests of pressure, produced fluidsand effective permeability (e.g. Schlachter 2007). However, it is likely that the optimum exploitation strategy will be based

on multi-lateral wells (Maricic et al. 2008), and this means that Net-Pay concepts will have to be modified in a horizontal

well setting (see below).

How is Net Pay Used? Historically, the main reason for determining Net Pay has been to obtain a value of Net-to-Gross Pay for the calculation ofhydrocarbons-in-place. It has long been recognized that the distribution of resources in a reservoir is better understood if Net

Pay is analyzed for each depositional unit in turn (e.g. Finley, 1985). This is not equivalent to saying that Net Pay should be

quantified by using rock-type-specific cut-offs. It is more concerned with how to interpolate Net Pay at the field scale after it

has been quantified at discrete wells. Net Pay appears as a Net-to-Gross term in the following volumetric equation forestimating hydrocarbons in place and thence ultimate recovery under primary depletion:

 EUR  = [(GRV  x N/G1 x φ 1 x hS  1) / B]  RF   (1)

where

 EUR  = estimated ultimate recovery (standard conditions)GRV   = gross rock volume (reservoir conditions)

 N/G1  = Net-to-Gross Pay fraction (reservoir conditions)

φ 1  = average porosity over Net-Pay interval(s) (reservoir conditions)

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hS  1  = average porosity-weighted hydrocarbon saturation over Net-Pay interval(s) (reservoir conditions)

 B  = formation volume factor (reservoir conditions/standard conditions)

 RF   = recovery factor (fraction) to economic limit (standard conditions)

The use of this equation is rooted within the culture of geological unit correlation and layer averages. It is most commonly

applied during the early stages in the life of a field, although it does persist in Reserve audits and related quick-lookevaluations.

The advent of geocellular models has changed this culture. Grid cells are populated with Net-to-Gross Reservoir and

 porosity data, with water saturation being assigned through a saturation-height function that ideally has been established atthe vertical grid-cell scale using Net-Reservoir inputs. Net Pay can only be computed after this has been done. Volumetrics

are addressed by grid cell and then aggregated for each reservoir unit. For an oil reservoir unit, the volumetric algorithm can

 be written:

STOIIP   = Σ ( BRV  x N/G2 x φ 2 x hS  2) / B  (2)

where:

STOIIP   = oil initially in place (standard conditions)

and, for each grid cell:

 BRV   = bulk rock volume of a cell (reservoir conditions) N/G2  = Net-to-Gross Reservoir fraction (reservoir conditions)

φ 2  = average porosity over Net-Reservoir interval(s) (reservoir conditions)

hS  2  = computed hydrocarbon saturation over Net-Reservoir interval(s)

(reservoir conditions)

 B  = formation volume factor (reservoir conditions/standard conditions)

In computing BRV, a cell is truncated at a fluid contact where present. Recovery factor would be derived from simulation

 based on the geocellular model. The benefits of using synergic cut-offs, which reduce the disparity between Net-to-GrossReservoir and Net-to-Gross Pay, are evident from a comparison of equations (1) and (2).

Contemporary methods of 3D reservoir modeling can accommodate a greater degree of reservoir complexity in the formof Net-to-Gross Reservoir and porosity distributions, and also saturation versus height variability. Reservoir modeling can

also be done using a facies-based approach governed by admissible Net-Reservoir and non-Net-Reservoir proportions offacies over the evaluation interval: this approach is not pursued here.

Several key stages can be identified in the context of integrated reservoir studies. An approach to using Net Pay is

described below for a deterministic application, although it can be easily adapted for geostatistical models. It assumes that

 Net Pay has initially been established using core data. As always, Net-Pay concepts are intertwined with those of Net

Reservoir. For simplicity, the following description assumes that all wells are vertical. Special procedures are required fordeviated wells and these are programmed into commercial software packages.

•  Once Net Reservoir has been identified using cut-offs established at the core scale, (re-)establish petrophysicalalgorithms over the admitted intervals, taking due account of rock typing issues. These algorithms include the Archieequations or shaly-sand variations and also porosity versus permeability relationships.

•  Scale up these relationships to the well-log scale where feasible.•  Apply these relationships to logs over Net-Reservoir intervals. Evaluate porosity and water saturation and (thence)

estimate permeability.

•  (Re-)establish the criteria for Net-Reservoir delineation at the well-log scale.

•  Average the interpreted data over the Net-Reservoir fraction of each grid cell along every well.

•  Establish saturation versus height function(s) at the grid-cell scale.

•  Populate the geocellular model with Net-to-Gross Reservoir, porosity and permeability, taking due account of anyreservoir zonation.

•  Apply the saturation-height function(s) to the cells with a Net-Reservoir fraction.

•  Identify Net Pay.

•  Compute hydrocarbon pore volumes by grid-cell aggregation and by reservoir zone if appropriate.

•  Transpose to surface conditions using (cell-specific) formation volume factor(s).

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•  Undertake dynamic sensitivity studies to investigate ranges of uncertainty.

•  Estimate petroleum Resources either through analog recovery factors or reservoir simulation.

•  Iterate as more data become available.

This process is illustrated schematically for conventional reservoirs in Fig. 3.

Other uses of Net Pay are to evaluate infill-drilling potential (Yeager et al. 1996), to target zones for formation

stimulation (Kessler et al. 2000), to identify perforation intervals (Grieser et al. 2001), to aid in the interpretation of well-test

data (Spivey & Pursell 1998), to guide the design of fluid-injection programs (Hunter et al. 1990), to initialize reservoirsimulators more effectively (Schoeling & Mark 2000), to sharpen Reserve estimates (Holtz & Hamilton 1998), to reduce risk

in highly complex reservoirs (Martin et al. 1999), and in equity redetermination, which is often based on in-place volumesand for which the procedures are usually proprietary.

DiscussionAbuses of Net Pay.  The literature contains numerous abuses of Net Pay as a component of reservoir evaluation. Forexample, the practice of correlating Net Pay with permeability mixes Net-Pay and Net-Reservoir criteria. The identification

of net thickness using cut-offs based on porosity-permeability character, fluid injection profiles and pore throat size actually

delivers Net Reservoir but it is sometimes described as Net Pay. Again, the concept of a “Gross Pay” with “Net Pay” as a

subset has little logical foundation. Moving on to volumetrics, there is still considerable usage within the industry of the rule-of thumb Net-Reservoir cut-offs (sometimes erroneously called Net-Pay cut-offs) of 0.1 mD for gas reservoirs and 1.0 mD

for oil reservoirs: these need to be superseded by a data-guided culture for improved reservoir description. Yet again, the

volumetrics equation (1) is sometimes presented as containing Net-to-Gross Pay but with porosity and hydrocarbonsaturation averaged over Net-to-Gross Reservoir, another example of mixing concepts. At the most basic level,

distinguishing reservoir from non-reservoir has been equated to identifying sand and shale, which actually constitute Net

Sand and non-sand, respectively.

Dynamic Conditioning of Cut-offs. The process of dynamic conditioning merits further comment. Permeability is pivotal

to this process. In many field databases, the permeability data are air permeabilities that may or may not have been corrected

for gas slippage effects through a Klinkenberg correction. If no correction has been applied, the data are arbitrary because

they depend on the average of the upstream and downstream flowline pressures used in the laboratory. There have been

many cases where these pressures have not been reported. Therefore, where several contractors have been used and these pressures vary between laboratories, the data cannot be integrated. The Klinkenberg correction avoids these problems, but it

remains an air permeability based on flow across the entire pore cross-sectional area. This measurement condition is not

 prohibitive provided that the hydraulic character of an oil-bearing water-wet rock can be diagnosed meaningfully throughKlinkenberg-corrected air permeability. If this is the case, the use of an air permeability term as a composite reference

 parameter can be accommodated. However, the industry should be encouraged to move towards effective permeability,

specifically the (end-point) permeability to hydrocarbons at irreducible water saturation. The implications have been

exemplified by Cobb & Marek (1998). At the very least, a subset of preserved samples should be measured for effective permeability so that correction factors can be investigated for “converting” conventional air permeability to pseudo effective

 permeability.

Horizontal Wells. For simplicity this discussion assumes horizontal beds. Along-hole Net Pay is the thickness measured inthe well bore of those reservoir rocks containing a supracritical volume of hydrocarbons that can express itself at the borehole

face. Horizontal wells often target Net-Pay zones that have been pre-identified in vertical wells. Therefore the concept of

 Net Pay in vertical wells is not directly transposable to horizontal wells. For this reason, some authors have distinguished between vertical-well Net Pay and horizontal-well Net Pay (e.g. Lemos et al. 2006). This distinction is appropriate, not least

 because the criteria for accepting an interval as Net Pay will change with rotation from vertical to horizontal well bores

 because of formation anisotropy. Moreover, there is the assumption that a vertical-well completion will tap a laterally

extensive Net-Pay unit of given thickness with consistent hydraulic properties. This perception has to be changed forhorizontal wells, where the along-hole Net-Pay “length” is no longer geologically constrained, but rather it becomes afunction of borehole reach. In other words, Net Pay can be increased simply by drilling further. The key limitation is now

drilling technology. Of course, the down side is that the extent of the target Net-Pay “volume” away from a horizontal well

 bore is constrained, e.g. by overlying and underlying seals, and it is far less likely to possess consistent hydraulic properties

in a plane orthogonal to the well-bore axis. Putting these matters together, in vertical wells Net Pay (thickness) is constrained by geology but the expressive hydrocarbon volume per unit along-hole thickness extends far from the well bore. On the

other hand, in horizontal wells, Net Pay (length) is larger but the expressive hydrocarbon volume per unit along-hole length isgeologically constrained to be closer to the borehole. Therefore, horizontal-well Net Pay should not be handled in the same

way as vertical-well Net Pay. In general, the horizontal criteria for Net Pay are simpler, based on length counts derived from

logging-while-drilling. However, it should never be forgotten that horizontal wells provide an opportunity for reservoir

description between vertical wells.

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Estimation of Hydrocarbon Volumes.  Equations (1) and (2) can be used deterministically or through a combination of

 probabilistic/stochastic methods. The former approach uses best estimates of the different input parameters to deliver a

single value of hydrocarbons in place and thence recoverable volumes: these estimates relate to a particular perception of thereservoir geology. The latter approach sets up a range of values for each input parameter, described by a probability

distribution, and then samples each distribution at random, with the sampled values being used to compute a realized estimate

of hydrocarbon volumes. The process is repeated many times to extract a median estimate, for which it is not possible to

identify a geological reality. Of course, with 3D geocellular models, the differences between the methods are rooted in how

the grid cells are populated, through deterministic algorithms or by geostatistics. The adoption of a geostatistical approachhas implications for reservoir simulation. To honor statistical theory, many realizations would have to be simulated and a

median recoverable volume identified.

Conclusions The concepts of Net Pay and Net Reservoir are strongly interrelated, with the former thickness a subinterval of the latter. The

nature and role of Net Pay have been clarified on the basis of host rock character and how a hydrocarbon accumulation is to be modeled. In so doing, it is noted that the term “Net Pay” would more appropriately be designated “Net Hydrocarbons”,

 because economic decisions extend beyond single-well completions.

In the absence of an industry-wide protocol for quantifying Net Pay, an iterative data-driven approach has been proposed

for the identification of Net-Pay cut-offs. This approach takes account of rock type and reservoir depletion mechanism, and it

honors scale of measurement. The cut-offs should be dynamically conditioned to reflect reservoir quality more completely.The outcomes are more exact petrophysical interpretation of hydrocarbon-bearing intervals and more meaningful reservoir

models.Putting these matters together, it has been possible to formulate a less subjective methodology for the identification of net

hydrocarbon-bearing intervals, as a basis for resource estimation and economic analysis. This is important, because

historically-different approaches to the quantification of Net Pay have furnished very different estimates. The proposed

 protocols are appropriate to the conjunctive use of deterministic and geostatistical approaches to volumetric studies. The

overall benefit is a more meaningful characterization of the reservoir with a better synergy between the static and dynamicreservoir models. Thus, the estimation of Reserves through geology-based methods is given a stronger procedural foundation

with a commensurate reduction in uncertainty, so that an energy company can more fully realize asset value.

AcknowledgementsThe author thanks Vivian Bust and Ian Firth for helpful comments during the preparation of the manuscript.

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Fig. 2 Quantification of Net Pay – ConventionalReservoirs

Fig. 3 Uses of Net Reservoir and Net Pay – ConventionalReservoirs

Define rock types

Establish Net Reservoir using core data

(Re-)establish petrophysical algorithms

Scale-up relationships to well-log scale

Apply relationships to logs over Net-Reservoir intervals

Evaluate porosity, Sw and permeability

(Re-)establish criteria for Net Reservoir at well-log scale

Average interpreted data over Net-Reservoir fraction of each grid cell

Populate geocellular model with Net-to-Gross Reservoir, porosity and permeability

Establish saturation versus height function(s) at grid-cell scale

Apply saturation-height function(s) to cells with a Net-Reservoir fraction

Identify Net Pay

Compute hydrocarbon pore volumes by grid-cell aggregation and by reservoir zone

Transpose to surface conditions using (cell-specific) formation volume factor(s)

Undertake dynamic sensitivity studies

Estimate Resources

Iterate as more data become available

Specify evaluation interval

Synergically relate Xc to conventional cut-offs Yc and Zc

Select reference parameter U

Partition into datasets

Identify critical value of reference parameter Uc

For each dataset, relate Uc to conventional cut-off Xc

Apply Xc, Yc and Zc simultaneously for Net Pay

Average porosity and Sw over Net-Pay thickness(es)

Integrate data from all partitioned intervals

Repeat for all wells in the project database