Estimation of Net Pay in Unconventional Gas Reservoirs

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Estimation Of Net Pay In Unconventional GasReservoirs

Conference Paper August 2015

DOI: 10.2118/178262-MS

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Cranfield University

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SPE-178262-MS

Estimation Of Net Pay In Unconventional Gas ReservoirsPaul Fekete, University of Calgary; Adewale Dosunmu, Shell Aret- Adams; Richard Ekpedekumo, Cranfield

University; Daniel Ayala, University of Calgary; Ediri Bovwe, Dalhousie University; Sitamai Ajiduah, University of

Calgary

Copyright 2015, Society of Petroleum Engineers

This paper was prepared for presentation at the Nigeria Annual International Conference and Exhibition held in Lagos, Nigeria, 46 August 2015.

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

The confidence and the ability to predict reserves more accurately play an important role in developing

a reservoir (conventional and unconventional). The role of Net Pay is very important in unconventional

volumetric calculation of hydrocarbon resources, a practice that strengthens the relative worth of the

Petroleum Industry. However, the estimation of resources has no universal definition of the concept

neither is there a widely accepted procedure or methodology for its determination and incorporation. In

unconventional reservoirs, these shortcomings become even more glaring where there is scarcity of

reservoir data for assessing the storage properties of reservoir rocks and flow behavior. In improving the

current situation of estimating Net pay in unconventional reservoirs, an assessment of the concept (Net

Pay) together with contemporary method of determining it was carried with the aim of determining its

application to Unconventional Gas Reservoir (UGR). In this paper an integrated rock typing approach is

proposed as an alternative method of assessing reservoir quality for unconventional gas reservoirs that

exhibit significant deviations for the Archie Criteria or Formula termed problematic Reservoirs by

Worthington (2011).

Introduction

Although the concept of Net Pay is not new to Reservoir Engineering, especially in conventional reservoir

studies, not much have been achieved in the area of evolving a universal, industry standard definition of

the concept neither have there been a concrete attempt at harmonizing the various disparate views for itsdetermination and incorporation into reservoir models as a basis for resource estimation. This fact which

was clearly articulated by [Caldwell and Heather, 2001] is especially puzzling given the importance of

the knowledge of Net Pay in the areas of volumetric estimation of hydrocarbon resources which underpins

the value of the petroleum industry. Aside volumetric estimation, knowledge of Net Pay also has

widespread applications in predictive calculations and well tests analysis in the characterization of

hydrocarbon reservoirs.

The above shortcomings become even more glaring in unconventional reservoirs where there is a

paucity of reservoir data for generating reasonable estimates of Net Pay for the purpose of estimating

in-place hydrocarbon. The current state may not be unconnected with the challenges encountered in
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extracting quality reservoir data in unconventional reservoirs as some of the methods currently being used

to determine Net Pay for conventional reservoirs are generally not applicable in unconventional reservoirs

due to the prevalence of markedly different reservoir conditions.

Therefore, this paper aims at assessing current data driven, fit-for-purpose approaches to Net Pay

determination in unconventional reservoirs and challenges impacting their application in unconventional

gas reservoirs. Also understanding rock behavior as a key to understanding productivity in unconventional

gas reservoirs is attempted in this paper to provide a consistent methodology involving variation in the

rock lithology. This method proposed byRushing et al (SPE 114164), integrates multiple data evaluation

techniques and data scales using a core-based rock typing approach designed to capture rock properties

that are good predictors of reservoir quality.

Brief Overview of Petroleum Resources

Petroleum resources are the estimated quantities of hydrocarbon naturally occurring on or within the

Earths crust. Resource assessments estimate total quantities in known and yet-to-be-discovered accumu-

lations. The estimation of petroleum resource quantities involves the interpretation of volumes and values

that have an inherent degree of uncertainty. These quantities are associated with development projects atvarious stages of design and implementation. The definition and classification of petroleum resources in

accordance with the Petroleum Resources Management System (Society of Petroleum Engineers et al.

2007) is set out below.

For the purpose of this paper, two broad types of Petroleum Resources have been identified. These are

the Conventional and Unconventional Petroleum Resources:

y Conventional Petroleum Resources: These resources exists in discreet 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 down dip water leg and which is significantly

affected by hydrodynamic influences such as buoyancy of petroleum in water. The petroleum is

recovered through the wellbore and typically requires minimal processing prior to sale without

hydraulic fracturing.

y Unconventional Petroleum Resources:Unconventional Resources are of prime focus in this paper,

exists in petroleum accumulations that are continuous throughout a large area and are not

significantly affected by hydrodynamic influences such as buoyancy of petroleum in water. These

continuous accumulations typically lack a well-defined down dip water contact. Example includes

Tight gas and oil, Shale gas and oil, Coalbed Methane, gas hydrates.

Moreover, the extracted petroleum (hydraulic fracturing) may require significant processing prior to

sale. There is therefore a need for increased sampling density to define uncertainty of in-place volumes,

variations in quality of reservoir and hydrocarbons. Fig. 1 shows Conventional vs. Continuous type

accumulations in their mode of occurrence.

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Classification Framework

According to the Petroleum Resources Management System(Society of Petroleum Engineers et al. 2007),

the major recoverable resources classes were delineated into Production, Reserves, Contingent Resources

and Prospective Resources as well as unrecoverable petroleum. The classification scheme places petro-

leum resources into a broad category named Total Petroleum Initially-In-Place (PIIP). PIIP is then

subdivided into discovered and yet-to-be discovered (undiscovered) petroleum resources. Recoverable

and unrecoverable resources fall within the two subclasses of PIIP.For the sake simplicity there are three classification of Petroleum resources (Fig. 2) that has been

identified viz-a-viz the stage of exploration and production activities (Worthington, 2010a) as follows:

y Prospective Resources potentially recoverable volumes

y Contingent Resources potentially recoverable from known accumulations

y Reserves discovered, recoverable and commercial

Figure 1Conventional vs. Continuous Type accumulations in their Mode of Occurrence [Source: Schenk & Pollastro (2002)]

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The Concept of Formation Thickness

The total thickness with the unit of length of a reservoir either along-hole or in true vertical space is termed

Gross Thickness. In some cases the gross thickness has been removed by the application of certain

parameters resulting to Net Thickness. Net thickness is of three types:

y Net Sand

y Net Reservoir

y Net Pay

These terminologies used to quantify and describe formation thicknesses have seen widespread

application within the industry in the areas of well completions and volumetric computations. (Figure 3).

Figure 2Resource Classification Framework [Source: Petroleum Resource Management System(SPE et al. 2007)]

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Net Pay implies hydrocarbon saturation in exploitable quantities.Worthington (2010a)however noted

that these terminologies used to describe the subdivisions within net thickness are rooted in the onshore

history of the oil industry and are not particularly exact in description and usage. The term net potential

reservoir would be a more precise description for net sand since carbonates and fractured basement are

supposedly included in the classification.

The Net Pay should be appropriately termed Net Hydrocarbon since the former is rooted in single

well completions onshore where technical and economic decisions are contemporaneous as against

multi-well situations, where economic decisions are made on a field scale. Worthington (2010a).

However, for the purpose of this paper, the conventional terminologies (Net Sand, Net Reservoir and Net

Pay) will be adopted.

The concept of Net Pay

Net Pay is a key parameter in reservoir evaluation because it identifies those penetrated geological

sections that have sufficient reservoir quality and interstitial hydrocarbons to function as significant

producing intervals. Through interpolation, Net Pay contributes to the estimation of a meaningful in-place

volume against which recovery efficiency can be usefully assessed. (Worthington, 2010a).

Net pay is basically a thickness unit of length thickness which can only be measured at a well and a

subinterval within the gross thickness that comprises net reservoir rock containing a significant volume

of potentially exploitable hydrocarbons. A Total Net Pay is aggregated once the Net Pay subinterval is

identified in the reservoir and then, by a ratio to gross thickness, net to gross pay.

This paper is also concerned with potentially recoverable hydrocarbon. The quantification of net to

gross pay is one of the many steps involved in the estimation of hydrocarbon recovery using the

volumetric method.

Several approaches (traditional) of evaluating Net Pay have persisted over time. One of such ap-

proaches includes the fixing of net to gross pay at unity irrespective of the nature of the geologic

Figure 3Interrelationship of Formation thicknesses [Source:Worthington (2010)]

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succession or the pore fluids thereby eliminating the need for a comprehensive evaluation of the pay

subintervals within the reservoir. This also tends to imply that the entire evaluation interval comprises

good quality reservoir rock that contains potentially exploitable hydrocarbons. While some of these

approaches may lead to the generation of somewhat plausible estimates of Net Pay, they however are not

backed by real data and in addition they tend to infer that all rocks that hosts a given hydrocarbon

accumulation automatically qualifies as a reservoir rock in which the entire volume of the hydrocarbon

contributes to the energy of the system, a scenario which is grossly idealistic. The implication is thatidentification of Net Pay needs to be integrated into any exercise that aims at achieving a precise

evaluation of any given formation for the purpose of volumetric estimation of in-place hydrocarbon.

The Use of Net Pay

Apart from volumetric estimation of in-place hydrocarbon which is by far the most important use of Net

Pay, it also finds relevance in the areas of measurement of recovery efficiency against in-place hydro-

carbon volume in rocks that will allow reservoir fluids to be stored and to flow. Net Pay can be used to

determine the total energy of the reservoir (both moveable and non-moveable hydrocarbons). Net Pay for

this purpose may be therefore greater than that for volumetric calculation (George and Stiles, 1978).

Net Pay also finds useful application in the evaluation of the potential amount of hydrocarbon availablefor secondary recovery, meaning Net Pay with favorable relative permeability to the injected fluid, i.e.

floodable Net Pay(Cobb and Marek, 1998). Once Net Pay and Net reservoir are identified, petrophysi-

cal algorithms can be established over these intervals, as appropriate. This means that interpretive

equations can be founded exclusively on calibrating data from those very same intervals to which they are

to be applied.

Determination of Net Pay

Literatures referred to picking of Net Pay according to how it was to be used so that the intended method

of application influenced how it was to be identified (Snyder, 1971). Generally, Net Pay is determined

through the use of petrophysical cut offs that are applied to well logs. Cutoffs are limiting values of

formation parameters that remove non-contributing intervals. However, the determination of thesepetrophysical cutoffs has constituted a major source of uncertainty and this can arguably be seen as a

major flaw of historical approaches of determining Net Pay.

Traditionally, a shale Volume fraction, Vshc, cutoff is used to identify net sand. A porosity, , cutoff

is then applied to net sand to delineate net reservoir. Finally a water saturation, Sw, cutoff is applied to

net reservoir to define Net Pay. Thus, Net Pay is nested within net reservoir and this, in turn is nested

within net sand.

Historical Approach of Determining Net Pay Cutoff

Historically, petrophysical cutoffs have been determined arbitrarily using certain rules of thumb. This

practice which has been on in the industry for over 50 years advocates the use of generally applicable

cutoffs for different lithology types. An example is the use of default cutoff for net reservoir of 0.1mD

for gas reservoirs and 1.0mD for oil reservoirs (Phillips and Liwanag, 2006). In addition, Desbrandes

(1985) in his book encyclopedia of well logging proposed cutoff values for calculating hydrocarbons-

in-place for sandstones and carbonates (Table 1). Even though these historic cutoffs were universally

viable to some extent, there have been a plethora of interpretations as to what they actually mean e.g. as

regards the nature of the permeating fluid(s), correction for any gas slippage effects and conversion to

reservoir-stress conditions [Worthington and Cosentino (2005)]. Hence, these arbitrary static Net Pay

cutoffs have been in use even though the basis for their determination was not fully understood neither

did they take into consideration peculiarities in reservoir conditions and dynamics.

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Contemporary Data Driven, Fit for Purpose Approach for PetrophysicalCutoff Determination

The determination of petrophysical cutoffs should be guided by the nature of the available data and shouldaccount for the hydraulic character of the reservoir rather than being arbitrarily selected. Some of the

reasons why cutoffs should be fit for purpose include:

y Relationship to intended deliverable, be it for the purpose of estimation of ultimate recovery or for

equity redetermination

y Accounting for the flow regime, i.e. intergranular flow, fracture flow or a combination of both

y Conditioning by reservoir recovery mechanism and the stage of depletion e.g. primary depletion

reservoirs where pressure declines or waterflooded depletion reservoirs where pressure is main-

tained by injection [Worthington and Consention (2005)].

One very useful approach for determining cutoffs has been discussed by Worthington (2010a). He

noted that the application of these principles in quantifying net reservoir and by extension, Net Pay calls

for an examination of porosity and permeability, k, as represented within a conventional core data set. The

approach should be adopted in the light of the depletion mechanism of the reservoir (Primary, water-

flooded) and with adequate data partitioning and with honoring of scale (upscaling from core to log scale).

For simplicity of presentation, primary depletion mechanism will be discussed in this paper with its

reference parameter being the Leveretts equivalent circular pore diameter, dp (a reservoir quality

indicator). Parameters that can be quantified through downhole measurement are tied back to the reference

parameter so that a reference parameter cutoff can be related to cutoffs for properties that can be

determined from well log analysis.

For primary depletion, Net Pay is based on drainage area; hence the selected reference parameter is the

equivalent circular pore diameter ofLeveret (1941):1

Where kis intergranular permeability andis porosity.

Where a bilinear crossplot of dp with porosity is done, the onset of reservoir character (increased

hydraulic behavior) as expressed through dp can be identified in terms of a limiting porosity (Figure 4).

Table 1Desbrandes (1985) proposed cutoff values [Source: Modified from Worthington and Cosentino (2005)]

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Note that, as mentioned earlier, upscaling of core data to mimic the well log scale should be done; this

can be achieved by using a simple running mean. (Worthington and Cosentino, 2005).

Using the principle of Synergic cutoffs (Cosentino, 2001), the dynamically conditioned cutoff can then

be related to corresponding cutoffs of log-derived porosity (where required), shale volume fraction and

water saturation so that all cutoffs become dynamically conditioned (Figure 5).

Figure 4Correspondence of reference and conventional parametric cutoffs for primary depletion [Source: Worthington (2008)]

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Thereafter, these dynamically conditioned cutoffs can then be applied to each partitioned data set in a

similar way as it is done traditionally to obtain Net Pay within each data set. Average log derived porosity

and water saturation over the Net Pay thickness of each partitioned interval are obtained and integrated

to obtain an overall Net Pay. This process should be repeated for all wells in the project database.

Application to Unconventional Gas Reservoirs

Unconventional gas resources Shale Gas, Tight Gas Sands, Coalbed Methane and Gas Hydrates

constitute a significant percentage of the Gas Resource Pyramid(Aguilera et al., 2008)and contributes

an ever increasing amount of natural gas not only for the US but also China and Canada (EIA,

International Energy Outlook 2011)with a tremendous potential for future reserve growth and production.

Unconventional gas reservoirs are characterized by complex geological and petrophysical systems as

well as heterogeneities at all scales and typically exhibit unique gas storage and producing characteristics.

The efficient development of this resource requires not only realistic descriptions to quantify the extent

and value of gas-in-place but also precise and accurate characterizations to identify the primary reservoir

mechanisms affecting production and optimum recovery (Newsham and Rushing, 2001).

The application of the above approach for Net Pay determination is grounded in the principles of

petrophysics, a discipline concerned with the technical evaluation of laboratory data and downhole

measurements for reservoir properties such as shale Volume fraction Vsh, porosity , permeability k,

net/gross reservoir, water saturation Sw, and net/gross pay.

According to Worthington (2011), in petrophysical analysis/evaluation, reservoirs can broadly be

defined as conforming to the Archie conditions (In which case they are termed Archie reservoirs) or

Figure 5Schematic process for data-driven identification of dynamically-conditioned cutoffs. Synergic quantification of conventional

cutoffs [Source: Modified from Worthington and Cosentino, (2005)]

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show deviations from these conditions (termed Non-Archie reservoirs). Although these conditions were

not explicitly itemized by Archie (1942), they are inherent in the application of Archies equations. The

differentiating attributes for delineating Archie and Non Archie reservoirs can be gleaned fromTable 2.

Unconventional gas reservoirs fall under the category of Non-Archie reservoirs. This category of

reservoirs exhibits some deviations from the Archie criteria in varying degrees depending on the specific

type of unconventional reservoir being dealt with.Table 3highlights the unconventional gas reservoirs of

interest and their deviations from the Archie Conditions.

Table 2Criteria for Archie and Non-Archie (Problematic) Reservoirs [Source: Modified from Worthington (2011)]

Table 3Departures from Archie Conditions. Note that the number against which X is checked corresponds to the Non-Archie

condition number inTable 2[Source: Modified from Worthington (2011)]

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These Non-Archie reservoirs, generally termed Problematic reservoirs by Worthington (2011)

require more complex workflow for their petrophysical evaluation because the Archie equations are not

sufficiently representative of reservoir character.

Below is set out some of the specific constraints that may be encountered in the application of the Net

Pay approach proposed earlier for the various types of unconventional gas resources for the purpose of

estimating in-place hydrocarbon. It is worthy of note that Tight gas, shale gas and Coalbed Methane

reservoirs will be discussed in this paper. Gas Hydrates have been deliberately left out as it is a relativelynew and emerging area and research into it is considered to still be in its infancy.

Suggested/Proposed Approach for the Determination of FlowCharacteristics and In-Place Hydrocarbon Quantities in UnconventionalGas Reservoirs

Although the approach discussed earlier for the determination of in-place hydrocarbon quantities for

conventional reservoirs can be thought of as successful for the most part, same cannot be said for

unconventional gas reservoirs due to the prevalence of markedly different reservoir conditions. The

various constraints encountered in the petrophysical approach for determining in-place hydrocarbon

quantities for unconventional gas reservoirs can be attributed to the unique geologic conditions prevalentduring deposition of these sediments and the subsequent diagenetic transformations that may have

occurred. As a follow up to this observation, Rushing et al, in their paper (SPE 114164) proposed a

method that integrates multiple data evaluation techniques and multiple data scales using a core based

rock typing approach for evaluating the flow characteristics of unconventional gas reservoirs. Specifically,

tight gas reservoirs were understudied. Although the approach can be used for shale gas reservoirs, some

extensions may be required for its application. The extension required relates to the non-Darcy flow effect

in Shale reservoirs, hence in addition to pore throat dimensions, a new parameter, called the Knudsen

number (which is a function of pore throat size, Pressure, Temperature and gas composition) needs to be

introduced. A detailed description of the method is presentedby Roberto Aguilera (SPE 132845).

Gas Content estimation for Coalbed Methane is still based on the traditional desorption canister testing

on multiple samples and then relating gas content to coal composition so that wire-line logs may becalibrated to estimate the gas content in-situ (Clarkson and Bustin, 2011).

The rock typing approach proposed by Rushing et al. (SPE 114164) for tight gas reservoirs (Repre-

sentative Unconventional Gas Reservoir) will be discussed in the next section. This approach is being

proposed as it yields deliverables that could be used for the characterization of Tight Gas and Shale Gas

(with some modifications as discussed above) Reservoirs and aid in the quantification of gas-in-place.

Rock Typing Approach

The concept of rock typing is not new to the Petroleum industry; petroleum literature is replete with

literatures on rock typing techniques for conventional reservoirs. Although it has been noted that there is

no consistent definition of rock type, Rushing et al (SPE 114164) noted that the very first and perhaps

widely used definition was given by Archie (1950) who defined rock type as:

. . .units of rock deposited under similar conditions which experienced similar diagenetic processes

resulting in a unique porosity-permeability relationship, capillary pressure profile and water saturation

for a given height above free water in a reservoir. (Archie, 1950).

Archies definition is very unique in that it implicitly suggests the impact of depositional system

properties, diagenetic effects, and the need for grouping of rocks on the basis of physical properties

controlling fluid storage, flow and distribution.

The role of pore structure (pore and pore throat dimension, geometry, size, distribution etc.) on fluid

flow and storage properties in all types of porous media have also enjoyed widespread attention in the

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Petroleum industry. This attention is hinged on the importance of the understanding of pore structure and

properties in unconventional reservoirs since diagenetic processes often modifies the original pore

structure and reduce the average pore throat diameter thereby increasing tortuosity and isolated pores.

However, some forms of diagenesis have known to enhance porosity by creating secondary or micro-

porosity[Rushing et al. (SPE 114164)].

Work-flow processThe work-flow proposed by Rushing et al. (SPE 114164)for the rock typing of tight gas sands integrates

both large scale geologic elements and small scale rock petrology with the physical rock properties. The

essential components of the process model includes the identification, specification and comparison of

three rock types namely depositional rock types, petro-graphic rock types and hydraulic rock types

(Newsham and Rushing, 2001;Rushing and Newsham, 2001).

Depositional rock types

These are rock types defined within the context of large scale geologic framework and represent those

original rock properties present at deposition. Variations in original rock properties depends on a number

of factors including depositional environments, sediment source and depositional flow regimes, sand grainsize and distribution, type and volume of clay deposited, etc.

These rock types are based principally upon core-derived descriptions of genetic units representing

similar environments, morphology and depositional energy resulting in unique rock texture, sedimentary

structure and stratigraphic sequence.

Depositional rock types help us to define the geological architecture and to describe large scale

reservoir compartments which can aid in assessing reservoir extent and gas-in-place.

Petrographic rock types

These rock types are described in the context of the geological framework established from the

depositional rock types, but are based on a pore-scale microscopic imaging (i.e. thin section descriptions,

x-ray diffraction analysis and scanning electron microscopy imaging of the current pore structure). Thepetrographic rock type is influenced by composite mineral distribution, composition and habitat. The

description of importance here includes rock texture and composition, clay mineralogy and diagenesis.

Both the framework and matrix components have a cause and effect relationship on the diagenetic

processes resulting in preservation, loss, or enhancement of rock properties.

Hydraulic rock types

These rock types are also quantified on the pore scale but represent the physical rock flow and storage

properties as controlled by the pore structure. This rock type classification provides a physical measure

of the rock flow and storage properties at current conditions i.e. it reflects the current pore structure as

modified by diagenesis. The size, geometry and distribution of pore throats, as determined by capillary

pressure measurements, control the magnitude of porosity and permeability for a given rock. Correct

identification of hydraulic rock types should allow for the development of unique permeability-porosity

relationships as a function of the dominant pore throat dimensions.

It is useful to note that all three rock types identified should be similar if little or no diagenetic

transformation has occurred. However, depending on the severity of diagenetic transformation, the

original rock texture and composition pore geometry, and physical rock properties will be modified. Each

rock type therefore represents different physical and chemical processes affecting rock properties during

the depositional and paragenetic cycles and a comparison of all rock types will enable an assessment of

the impact of diagenesis on rock properties.

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Rushing et al. (SPE 114164) discussed a variety of analysis that are carried out to obtain information

regarding the physical properties of the various rock types. Following the extraction of the various

properties of interest from the core samples/plugs etc., the dominant pore throat dimensions are identified

using the Pittmans method. The principle underlying this method is that the pore throats, rather than the

overall porosity control flow capacity especially in low-perm sands with significant diagenesis. In this

respect, identified hydraulic rock types are grouped on the basis of the dominant pore throat dimension

(quantified using mercury injection capillary pressure data in which pore throat radius is plotted against

cumulative mercury saturation, Figure 6)

Next, permeability-porosity relationships for each hydraulic rock types are investigated using the

Pittmans equation (Kolodzie, 1980, Pittman, 1992) which relates absolute permeability to effective

porosity as a function of the dominant pore throat radius. The inflection point of an apex plot of Mercury

saturation divided by capillary pressure on the y-axis vs. mercury saturation on the x-axis defines the

maximum pore throat radius as a function of mercury saturation (Figure 7). This pore throat dimension

was used by Pittman (1992) as an effective correlating parameter for an empirical porosity-permeabilityrelation.

Figure 6Identification of hydraulic rock types in the Bossier Tight Gas Sand Play, East Texas Basin (source: Rushing et al. [SPE

114164)]

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Next, a semi-log plot of absolute klinkenberg-corrected permeability against effective porosity for the

rock types is done and superimposition of these plots in conjunction with the Pittman permeability-

porosity correlation for the identified dominant pore throat sizes shows whether there is a correlation

(indicated by significant clusters of permeability porosity data points from depositional or petrographic

rock types within each of the hydraulic rock type ellipses) between the rock types. As expected, there wasno clear, unique correlation between depositional and hydraulic rock types of the studied play, Bossier

Tight Gas Sand, East Texas Basin (Figure 8), whereas slightly better correlations were observed between

the petrographic and the hydraulic rock types (Figure 9) indicating that there has been significant post

depositional diagenesis that altered the hydraulic properties of the reservoir rocks.

Figure 7Apex plot showing maximum pore throat radius at Mercury Saturation of 25% in the Bossier Tight Gas Sand Play, East Texas

Basin [Source: Rushing et al. (SPE 114164)]

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Conclusion

In the absence of an industry wide approach for determining Net Pay, the data-driven, fit-for-purpose

methodology proposed by Worthington (2010a) serves a very useful basis for estimating cut-offs to be

Figure 8Winland plot showing relationship between depositional and hydraulic rock types in the Bossier Tight Gas Sand Play, East

Texas Basin [Source: Rushing et al. (SPE 114164)]

Figure 9Winland Plot Showing Relationship between petrographic and Hydraulic rock types in the Bossier Tight Gas Sand Play, East

Texas Basin [Source: Rushing et al. (SPE 114164)]

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used for Net Pay determination for Conventional Reservoirs and those Unconventional Ones where the

approach will yield useful data representative of the hydraulic character of the reservoirs. However, In the

event that the approach fails to yield quality reservoir information due to the uniqueness of the

hydrocarbon reservoir in question, then Rushing et al (SPE 114164) proposed integrated rock typing

approach should yield good quality reservoir data for estimating the hydraulic character and reservoir

potentials of Oil and Gas Reservoirs.

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8897. Paper SPE 144688. DOI: 10.2118/144688-MS

SPE-178262-MS 17
https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/236360158_Relationship_of_Porosity_and_Permeability_to_various_Parameters_Derived_from_Mercury_Injection_Capillary_Pressure_curves_for_Sandstones?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/303362214_Rock_typing_-_Keys_to_understanding_productivity_in_tight_gas_sands?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/303362214_Rock_typing_-_Keys_to_understanding_productivity_in_tight_gas_sands?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/303362214_Rock_typing_-_Keys_to_understanding_productivity_in_tight_gas_sands?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==http://greenwood.cr.usgs.gov/pub/fact-sheets/fs-0113-01https://www.researchgate.net/publication/254527521_Using_Wireline_Formation_Evaluation_Tools_To_Characterize_Coalbed_Methane_Formations?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/254527521_Using_Wireline_Formation_Evaluation_Tools_To_Characterize_Coalbed_Methane_Formations?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/254527521_Using_Wireline_Formation_Evaluation_Tools_To_Characterize_Coalbed_Methane_Formations?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==http://www.spe.org/industry/reserves/docs/Petroleum_Resources_Management_System_2007.pdfhttp://www.spe.org/industry/reserves/docs/Petroleum_Resources_Management_System_2007.pdfhttp://www.eia.gov/forecasts/ieo/index.cfmhttp://www.eia.gov/forecasts/ieo/index.cfmhttps://www.researchgate.net/publication/270632310_Recognition_and_evaluation_of_low-resistivity_pay?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/270632310_Recognition_and_evaluation_of_low-resistivity_pay?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcyMzgxNDVAMTQ0NTcxNTczODc2Nw==https://www.researchgate.net/publication/270632310_Recognition_and_evaluation_of_low-resistivity_pay?el=1_x_8&enrichId=rgreq-f52f1617-018d-46b1-9949-02fcb846d465&enrichSource=Y292ZXJQYWdlOzI4MzEyMzA2NDtBUzoyODgxNjY4NDcy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Documents

Estimation of Net Pay in Unconventional Gas Reservoirs