CARBONATE RESERVOIRS...2019/03/07 · • Carbonate sand migration - In high-energy locations, the...

CARBONATE RESERVOIRSHow to choose the right petrophysical

evaluation method – Evaluation of mineralogy, pore geometry, saturation and permeability

Brian Moss RetiredSteve Cannon Retired

Petrophysics 101 SeminarCo-convened with PESGB Young Professionals

The Geological Society, Burlington House7th March 2019

Brian Moss & Steve Cannon – LPS March 2019 1

What are carbonates?

• Limestones are greater than 50% calcite and dolomites or dolostones are greater than 50% dolomite

Rocks made up of calcareous minerals:

Calcite – CaCO3

Aragonite – CaCO3

Dolomite – CaMg(CO3)2

Stain in Alizarin Red to identify dolomiteFix in blue resin to show porosity


PresenterPresentation NotesCarbonates are chemically produced sediments, generally found close to where they are created but can be transported, but not far! They are produced in warm, shallow oceans either by direct precipitation or by biological extraction of calcium carbonate from sea water to produce skeletal material.

Carbonates are sediments composed of mineral types formed of calcium (Ca2+), magnesium (Mg2+) and/or iron (Fe2+) with the carbonate radical (CO32- ), such as Calcite: CaCO3Dolomite: CaMg (CO3)2 Siderite: FeCO3

Subjected to lithification, cementation and other diagenetic processes during and after deposition to form a carbonate rock. Usually composed of varying proportions of bioclastic fragments and lime mud

The resulting sediment is composed of particles with a wide range of sizes, shapes and mineralogy, mixed together to form a multitude of textures, chemical compositions and associated pore-size distributions.


PresenterPresentation NotesModern carbonate deposition is distributed in warm water and cold water shelf environments as well as deep ocean oozes. Ancient carbonate deposition probably took place in the same types of environment.The resulting biogenic make-up and mineral content will vary depending on the environment

How are carbonates formed? Where?• Carbonates are both allochthonous and autochthonous in origin and occur along

stretches of coastline and subduction zones (form in situ or transported)• Comprise atolls, carbonate ramps, rimmed shelf• Geologically, although carbonate platforms may develop in a range of geotectonic

settings, the majority can be found along passive continental margins and back–arc basins to foreland basins


http://en.wikipedia.org/wiki/Image:Atoll_pacific_300px.jpg

Comparison: clastics and carbonates

Clastics• Sandstones – quartz, lithic fragments,

carbonate, clays

• Transported from elsewhere and modified through compaction, lithification, diagenesis

• Principal reservoir quality controls: - Mineralogy, grain-size, sorting, texture

(layering), and induration – MINERALOGY AND TEXTURE

• Classic petrophysics geared to porosity and matrix, so are tuned to clastics

Carbonates• Calcite, aragonite, dolomite, with

evaporites• In-situ precipitation or organic growth

and modified through compaction, lithification, diagenesis including dolomitization and dissolution

• Principal reservoir quality controls: - Pore-size distribution, pore connectivity,

fracturing and dolomitization – PORE CHARACTER

• Interpretation requires partitioning into units with similar pore characteristics, so pore-typing is paramount


PresenterPresentation NotesClastics: Sandstones comprise quartz principally and other minerals in varying quantitiesTransported from elsewhere, modified through weathering, compaction, lithification, diagenesisRes. Quality controls are principally: mineralogy, grain-size, sorting, texture (layering), and induration – i.e. MINERALOGY and TEXTUREMost petrophysical interpretation techniques are geared to mineralogy and porosity and thus have been tuned to clastics.

Carbonates:Simpler mineralogy - calcite, dolomite, with evaporates in certain conditions of depositionIn-situ from precipitation or organic growth, or generally very local transportation and deposition as particulate material (e.g. reef talus), subsequently modified by weathering, compaction, lithification and diagenesis, also including dolomitisation and dissolutionRes. Quality controls are pore-size distribution, pore connectivity, fracturing and dolomitisation – i.e. PORE CHARACTERDetailed reservoir interpretation requires partitioning into units with similar pore characteristics, so need for pore-typing is paramount – difficulties arise because standard logs are only weakly sensitive to rock fabric

Clastic vs Carbonate sequences• Sediment supply mechanisms are fundamentally different giving a

different response to sea level change• Carbonate production is proportional to the area of flooded platform,

thus greatest sedimentation occurs during a highstand

• Rate of carbonate production may exceed sea-level rise or subsidence leading to progradational/aggradational packages with shoal profiles even though sea-level rises

• When the accommodation space is filled by sediment, the excess may be shed off the slopes to deeper water

• Carbonate ramps tend to have smaller producing areas and behave like siliciclastic shelves


PresenterPresentation NotesSediment supply mechanisms are fundamentally different giving a different response to sea level changeCarbonate production is proportional to the area of flooded platform, thus greatest sedimentation occurs during a highstandRate of carbonate production may exceed sea-level rise or subsidence leading to progradational/aggradational packages with shoal profiles even though sea-level risesWhen the accommodation space is filled by sediment, the excess may be shed off the slopes to deeper waterCarbonate ramps tend to have smaller producing areas and behave like siliciclastic shelves

Depositional processes• Tidal flat progradation

- Results in the formation of upward-shallowing sequences via re-deposition of subtidal sediments over tidal flats and beach ridges during major storm periods

• Reef progradation- Seaward growth of the reef over the fore-reef slop at rimmed shelf margins;

reefal sequences of various facies types will be produced, particularly vertically

• Carbonate sand migration- In high-energy locations, the migration of carbonate sand bodies is an

important depositional process, particularly on ramps and sand shoals or shelf margins

• Offshore storm transport- Results in the deposition of shore-face carbonate sediments

• Slumps, slides, turbidity currents, debris flows- Are various types of re-sedimentation of previously deposited sediments. They

are very common and mostly restricted to shelf margins and slopesBrian Moss & Steve Cannon – LPS March 2019 7

PresenterPresentation NotesTidal flat progradationResults in the formation of upward-shallowing sequences via re-deposition of subtidal sediments over tidal flats and beach ridges during major storm periodsReef progradationSeaward growth of the reef over the fore-reef slop at rimmed shelf margins; reefal sequences of various facies types will be produced, particularly verticallyCarbonate sand migrationIn high-energy locations, the migration of carbonate sand bodies is an important depositional process, particularly on ramps and sand shoals or shelf marginsOffshore storm transportResults in the deposition of shore-face carbonate sedimentsSlumps, slides, turbidity currents, debris flowsAre various types of re-sedimentation of previously deposited sediments. They are very common and mostly restricted to shelf margins and slopes

Carbonate sediments and controls• Most carbonate sediments are

produced in water depth less than 50m with little clastic input

• Comprise skeletal and non-skeletal components – bioclasts and grains

- Organic/skeletal – corals, algae, molluscs bryozoa

- Inorganic/non-skeletal – coated grains (ooids), peloids, clasts: generally slower build up rate

• Main controls are water depth, temperature and photosynthetic activity in top few tens of metres

• At higher latitudes fewer major reef builders and increase in bio-clastic shoals and patch reefs

• Carbonate factories can be killed off when drowned

Keep up

Catch up

Start upLag phase

Log phase

LAW OF SIGMOIDAL GROWTH

TimeG

row

th

After Neumann and Macintyre, 1985


PresenterPresentation NotesMost carbonate sediments are produced in water depth less than 50m with little clastic inputComprise skeletal and non-skeletal components – bioclasts and grainsOrganic/skeletal – corals, algae, molluscs bryozoa Inorganic/non-skeletal – coated grains (ooids), peloids, clasts: generally slower build up rateMain controls are water depth, temperature and photosynthetic activity in top few tens of metresAt higher latitudes fewer major reef builders and increase in bio-clastic shoals and patch reefsCarbonate factories can be killed off when drownedCarbonate reef or platform growth follows a sigmoidal law: starts slow and builds up rapidly to catch up with sea level rise.

Skeletal components• Skeletal organisms have varying

mineralogy and these have varied with geological time possibly related to global climatic change and sea level

• Calcite seas dominate during greenhouse conditions when sea levels are high and aragonite during icehouse conditions when sea levels are lower


Non-skeletal components – grains/mud

• Carbonate solubility in seawater (after Bathurst 1971)

- CO2+H2O+CaCO3 Ca +2HCO3

• Solubility driven by:- Temperature (lower solubility in warmer water)

- Acidity/alkalinity (High CO2 = acid = dissolution)

- Pressure (increases solubility)

• Calcite dissolves in deep ocean water

2cm

ooids

grapestone

peloids, shells, forams


Carbonate cycles• During a sea level fall the area of

carbonate production is substantially reduced forming a lowstand wedge

• There is little erosion during the lowstand, only chemical weathering

• Expect to see a different type of carbonate grain composition deposited in the lowstand wedge

• With a subsequent rise carbonate production increases


Sea level fall

Sea level rise

High resolution sequence stratigraphy

• Detailed sequence stratigraphy requires the identification of parasequences

• Core and log integration provides the key to the high resolution breakdown

• If pattern is regular and predictable then a deterministic approach to stratigraphy can be used


PresenterPresentation NotesSequence identification in carbonates is essential because deposition is widespread over many 10’s-100’s km or more, and when unless local tectonics takes a part the same depositional environment will the continuous

Diagenetic overprinting• Basic processes to consider:

- Calcite cementation- Compaction- Selective dissolution- Dolomitization- Evaporite mineralization- Massive Dissolution

• Each results in a specific rock-fabric; processes may overlap in time and space

• Timing of events usually associated with deposition, meteoric flushing, later burial and hydro-thermal events

• Petrography can determine the sequence of events


PresenterPresentation NotesDiagenesis makes predicting reservoir quality and distribution more difficult especially without good well control. Modern seismic attributes are of limited use in predicting good quality reservoir because calibration with cores and logs is challenging. Even where a reliable porosity relationship is found does not mean good permeability.

Generalized diagenetic sequence


PresenterPresentation NotesDetailed petrographic analysis can help to unravel a complex diagenetic sequence. Porosity can be created, destroyed and recreated at different stages.

Early diagenesis• Marine deposition

• Isopachous fringes• Marine vadose

• Meteoric flushing• Limited compaction

due to drusy cementation.

• Mouldic porosity


Burial Diagenesis

• Sparry calcite • Non-ferroan and

ferroan

• Saddle dolomite• Non-ferroan and

ferroan

• Fills intra-grain voids and mouldicporosity to varying degrees


Burial Diagenesis

• ‘Exotic’ minerals –fluorite, kaolinite, anhydrite, celestite.

• “Blue John”• Often associated with

petroleum filling but no fluid inclusion data.

• Late stage secondary porosity.


Comparison of Reservoir QualityBasin 1 Basin 2

Poroperm : 10-12%, 10-80 mD Porosity similar, perm. lower


Roof collapse after dolomitization


Core to Log Integration • Use of limited core data to

calibrate readily available log data

• Key is accurate and consistent depth matching and QC every step

• Core description should include:- Dominant lithology, grain types

colour, texture – grainsize/sorting- Interparticle porosity, dolomite

crystal size- Separate and touching vug space

• How can basic rock-fabric characteristics be related to log data….


Core to log integration (2)• Interparticle porosity is estimated by subtracting separate vug

porosity, estimated from sonic logs, from total porosity from density/neutron logs

• Grain size and sorting can be estimated from gamma ray, but better still from interpreted resistivity logs that give water saturation

• Touching-vug pore-space is best estimated from high resolution image logs

• Exotic minerals can be identified by Spectral Gamma Ray and Photo-Electric Factor (PEF) logs; flooding surfaces, evaporites, dolomitic zones


Core/Log CalibrationA two step process:1. Describe the cores and capture the

data in graphical and numerical formats

2. Accurately depth shift the cores by:- comparing core-gamma with wireline

gamma- comparing core porosity with log

porosity- comparing core mineralogy with log-

derived mineralogy

Present the results in a composite 1:200 scale format including major stratigraphic/zonal markers


Will a carbonate reservoir produce hydrocarbons?

• Carbonates reservoirs are very difficult to evaluate because…

• Heterogeneity – many different pore types impact resistivity logs

• Dual permeability systems – matrix, vugs, fossil moulds, fractures

• Diagenetic overprinting – dissolution, dolomitization, micritization

• Wettability – commonly oil to intermediate but how to measure

• Need to develop integrated methods using thin sections, cores, logs, well tests, seismic

• Archie saturation equation works in intergranular or intercrystalline porosity:

n

mt

ww R

RaS

1

××

=φ


n

mt

ww R

RaS

1

××

=φ

Pore space terminology• Confusing number of classification schemes, many are hybrid

- Archie (1952) – textural, classically petrophysical but does not relate to geology

- Dunham (1962) – textural, useful for determining depositional energy- Folk (modified 1962) – compositional and textural- Choquette and Pray (1970) – pore space genesis, fabric selectivity,

does not distinguish isolated and connected pores- Lucia (1983) – petrophysical, distinguishes between interparticle and

vuggy porespace

• Pore space should be defined in terms of rock fabrics, texture and petrophysics to capture geology and engineering

• Most schemes are based only on petrography


Dunham’s 1962 Classification

• Varies from chalks through limestones to dolomites; muds, grains, shells, corals, crystals…

• Can contain evaporates e.g. as disseminated anhydrite; • Can contain clay minerals and other conductive material e.g. pyrite; • Pore geometry and mineralogy may only be weakly correlated


Folk’s 1962 Classification

• Varies from chalks through limestones to dolomites; muds, grains, shells, corals, crystals…

• Can contain evaporates e.g. as disseminated anhydrite; • Can contain clay minerals and other conductive material e.g. pyrite; • Pore geometry and mineralogy may only be weakly correlated



Pore types

Did you say porosity…

…or porosity?

PresenterPresentation NotesAs an indication of the potential for complex pore systems, here is the system of classification created by Choquette and Pray in 1970.

It comprises 15 major classes or types of pores, extended by many modifiers to describe in detail the origin and likely controls on the distribution of the pores under consideration.

As a means to describe physical core samples, it provides a comprehensive basis. However it is not practical from the point of view of log data.

PORE TYPES

Intergrain

Intercrystal

Moldic Intrafossil Shelter

Cavernous Fracture Solution

ARCHIE (1952)

Matrix

Visible A, B, C, and D

Choquette & Pray (1970)

Fabric Selective Non-Fabric Selective

LUCIA (1983) Interparticle

Vuggy

Separate Touching


PresenterPresentation NotesCarbonate Reservoir Character

Understanding carbonate texture is one thing, but more important for petrophysics is to understand pore morphology and particularly its connectedness.

An early attempt at petrophysical relevance by Archie (types A, B, C and D were increasing pore volumes of visible porosity in hand specimen (cores)) was too simplified.

Lucia 2007 (work stretched back to 1970s) maintained differentiation into interparticle, separate vugs and touching vugs. His interparticle system was developed into size-related classes that had presence in porosity:permeability plots. Therefore, core derived phi:k data or log derived phi:k estimations could thus be used to establish classes and hence make predictions outside of cored interval…

Central issue• Disparate pore character at the root of interpretation difficulties• Classic petrophysical methods have been developed for clastics

• Intergranular phi, some fractures; texture; mineralogy (e.g. clays)

• Carbonates are different…• Interparticle, intraparticle; variable interaction with secondary pore system

comprising dissolution voids and fractures; mineralogy arguably simpler• Heterogeneous at all scales

• Require delineation of pore facies – principally type and connectivity

• For prediction:• Distribution and mode of formation of phi – layering, core examination, mineralogy

- downhole imagery, chemical logging

• For reservoir quality:• Volume, pore size distribution and connectivity – Pc, NMR, Dielectric, classic logs

(nuclear, resistivity) Brian Moss & Steve Cannon – LPS March 2019 29

PresenterPresentation NotesCore and log data in carbonates confirm that disparate pore character is at the root of interpretation difficulties

Classic petrophysical methods were developed for clastics – intergranular phi with some fractures; clays; textures

In carbonates the primary phi can be interparticle, intraparticle, and interact variably with a secondary pore system comprising dissolution voids, fractures

Carbonates are heterogenous at all scales

Carbonate interpretation requires delineation of pore facies – principally type and connectivityFor prediction in context with geological controls – we seek distribution and mode of formation of phi – layering, core examination, mineralogy – downhole images, chemical logging are principal data sourcesFor reservoir quality – volume, pore size distribution and connectivity – Pc, NMR, Dielectric, classic logs (nuclear, resistivity)

Partitioning terminology - 1• Facies (Gressly, 1838) – distinctive rock unit forming under certain conditions,

reflecting a particular process or environment

• Electrofacies (Serra et al., 1982) – log-identifiable rock types

• Lithofacies (Miall, 1990) – rock types with distinct texture and mineralogy

• Petrofacies ( AAPG Watney et al., 1998) – petrophysically distinct pore types and fluid saturations expressed on a phi-Sw cross-plot (aka the “Pickett” plot)

• Rock-fabric facies (Jennings and Lucia, 2003) – rock types with characteristic range of pore size (inferred from particle size and sorting), characteristic distribution of interparticle porosity and characteristic nature and size of vuggyporosity; fracture porosity and dissolution are modifiers

• Electroporefacies (Bust et al, 2011) – rock-fabric facies partitioned in terms of pore character that are log-identifiable. Logs required to accomplish this go beyond the standard suite

• Partitioned units must be geologically architecturally significant – correlatableand distributable through geological controls


PresenterPresentation NotesOriginally for clastics we have lithofacies (Miall 1990) – rock types with distinct texture and mineralogy.Serra et al. 1982 created electrofacies to describe log-identifiable rock typesRock types must be geologically architecturally significant – in order to be correlatable and distributable through geological controls

In carbonates, geological relevance is necessary but mineralogy not sufficientRock texture (sedimentary origin and fabric), and pore-size distribution are key.Jennings and Lucia (2003): rock-fabric facies: a rock type with characteristic range of pore size, inferred from particle size and sorting, distribution of interparticle porosity and the nature and size of vuggy porosity.

Bust et al, 2011 define electroporefacies as a rock-fabric facies partitioned in terms of pore character and that has a sufficiently exclusive set of log signatures. Fracture porosity and fissure (lineated dissolution) porosity are accounted for as modifiers.

Recognising and delineating log-identifiable rock types in carbonates, identified through pore characterisation, requires identification of electroporefacies. Logs required for this go beyond the standard suite.

Partitioning terminology - 2• Petrophysics is a remote-sensing discipline

• Outside the laboratory, downhole methods sensing formations that cannot directly be touched

• Often we cannot directly observe the phenomena of interest but use physical measurements that are heavily, but not exclusively, influenced by the parameters of interest – such relations established through algorithms

• To be differentiated by petrophysical measurements, rock types require a sufficiently exclusive set of algorithms (functions); as such they become Petrofacies (Worthington, 2002).

• Petrofacies may equal electroporefacies, but not necessarily; former are often discarded whilst latter are predictable beyond the sample

• “m” and “n” may vary by porosity and saturation• The estimation of fractures and fissures largely the same as for clastics


PresenterPresentation NotesAlgorithms/functionsPetrophysics is a remote sensing investigation/discipline:Outside the laboratory, downhole methods sensing formations that cannot directly be touched; andOften we cannot directly observe the phenomena of interest but use physical measurements that are heavily but not exclusively influenced by the parameters of interest – relations are established through algorithms (functions)

To be differentiated by petrophysical measurements, Petrophysical rock types (however based/defined), require a sufficiently exclusive set of interpretive algorithms (functions). As such, they become petrofacies (Worthington 2002) (short for petrophysical and not petrological…).

Petrofacies may equal electroporefacies but not necessarily. Former is essentially an interpretative tool for petrophysical analysis and is often discarded; latter are predictable beyond the sample.

Archie ‘m’ and ‘n’ may vary by pore type, pore volume and/or saturation

Fractures and fissures estimation is largely same as for clastics – nothing special required for carbonates; fractures and fissures may, however, be more prevalent in brittle carbonates.

Pore volume

Pore volume on its own generally can not sufficiently distinguish fabric-related reservoir character (and hence flow potential)


PresenterPresentation NotesPetrophysical interpretation methods can generally get a good estimate of pore volume.

From classic toolsDensity methods are good, if mineralogy is knownSonic methods can be used to differentiate connected and separate pore systemsNeutron is calibrated to limestone (usually) so can do well in carbonates that have v. simple mineralogy.

From newer technologiesNMR gives total porosity and, crucially, pore size; however it cannot give connectivityDielectric can give water-filled pore volume that may instruct choice of Archie parameters

Beyond pore volumeTypical scatter of porosity:permeability data renders pore volume on its own inadequate as a discriminant of permeability

Bring fabric into the mix and order emerges…

Fabric is related to environment and hence amenable to geological controls


PresenterPresentation NotesHere is the basis of Lucia’s approach.

Wide scatter on phi:k cross plots is common for carbonates. Porosity volume is not a sufficient discriminant of permeability.

Adding fabric-related information introduces clarification to the trends and they start to cluster within the overall picture.

And this can help in prediction because fabrics are related to locations within the carbonate shelf-edge belts.

Petrophysical classification• Permeability and saturation are related to pore-size

distribution, which in turn is related to rock fabric• Pore-size distribution is related to rock fabric through a pore-

type:1. Interparticular – between grains and bio-clasts2. Separate-vug – dissolved fossils, molds3. Touching-vug – inter-connected vuggy systems

• Each class has a different pore-size distribution and inter-connection and will be represented by a different volume or proportion in the reservoir

After Lucia, 1999


PresenterPresentation NotesFabric can also be a principal control on the major pore types.

Differing pore types are distinguishable through their respective pore-size distributions.

Capturing these differences allows the partitioning of the phi:K plots into a fabric-related schema that “tames” the wide scatter generally observed in bulk data from carbonate reservoirs.

Interparticle pore-space

Interparticle PSD can be described in terms of particle size, sorting and porosity

Particle size can be related to Pc displacement pressure through pore size

Large pores are filled first then smaller pores as pressure increases

After Lucia, 1999


PresenterPresentation NotesMercury Injection “Capillary Pressure” data (the so-called MICP data from cores) hold valuable information about the connectivity within the pore system and the size distribution of the pores.

The nature of the measurement – introducing mercury through the pore system of a plug or plug-end offcut – ensures that the minimum pore sizes (“pore throats”) are fully represented.

If large pores (e.g. vugs) are only connected via thin fractures, the applied mercury entry pressure has to overcome entry to the fracture system before the mercury can reach the vugs. Hence the shape of the MICP curve is often diagnostic of key attributes of the pore system and its connectivity.

NMR data (T2 distributions) react to the bulk volume of the pores and so respond more to the larger pore sizes than the smaller.

To compare these two data sets requires that an adjustment or calibration be made.

Porosity, particle size and capillary pressure

• Lucia (1983) showed the relationship between displacement pressure and particle size for MICP data on a range of porosity classes with permeability >0.1mD

• Suggests boundaries at 20 μm and 100μm related to different permeability fields

• Refined using textural information –grain or mud dominated: in reality a continuum

Mer

cury

disp

lace

men

t (ps

ia)

0 20 100 Average particle size (μm)After Lucia, 1999


PresenterPresentation NotesLucia detected a clear relationship between the entry pressure of pore systems and their pore size characteristics.

From this he was able to determine the likely particle size that gave rise to the interparticle pore system being observed.

Knowledge of the particle size allowed the partitioning of the phi:K scatter into smaller and more manageable sub-groups.

With today’s NMR data it would be possible to work back towards an estimate of particle size by knowledge of the PSD seen by the NMR tool.

Porosity/permeability relationships

Non-vuggy limestones• Grainstone – PSD controlled by

grain size and sorting• Packstone – PSD controlled by

particle size and size of micriteparticles between grains

• Muddy/Wackstone – PSD controlled by size of micriteparticles

Grainstone: φ=25%, k=15D GrnDomPkst: φ=16%, k=5.2mD

Wackestone: φ=33%, k=9mDMdDomPkst: φ=18%, k=4mD

After Lucia, 1999


PresenterPresentation NotesWhat Lucia went on to observe was that overall carbonate fabric plays an important role in the contained pore systems.

His pragmatic grouping of data by particle size in phi:K crossplots could also be characterised by their Dunham/Folk rock types as well.

Porosity/permeability relationships

Non-vuggy dolomites• Dolomitization changes rock

fabric significantly; remnant textures may be preserved

• Dolomite crystals range from 10’s of micron up to 200μm so that micrite particles can increase by 2 OOM

• In muddy fabrics permeability increases as crystals grow

Dolgrnst: φ=7.1%, k=7.3mDGrnDomDolPk: φ=9% k=1mD

DolWkst: φ=16%, k=30mDDolWkst: φ=20%, k=4D

After Lucia, 1999


PresenterPresentation NotesOne key element of dolomitisation is the timing of the recrystallisation to dolomite.

Early in burial history and the dolomite tends to be pore-destructive; the onset of dolomite crystallisation occluding open pores as burial takes hold.

Later in burial history and the dolomitization CAN enhance porosity; being a denser mineral, the transformation from calcite to dolomite can lead to a size reduction of the particles.

Texture and pore type

From Lucia, 1999, 2007


PresenterPresentation NotesIn summary on Lucia’s observations:

Lucia noted a relation between particle size and capillary pressure that was far stronger than the relation between pore volume and capillary pressure.

He then superimposed particle size on to porosity:permeability cross plots and noted pronounced clustering of data. This led to the establishment of 3 principal classes defined in porosity:permeability “space”.

He then noted a similarity between particle type and those same classes.

Rock fabric classes• Class 1(>100μm) – limestone and

dolomitized grainstones; large crystalline, grain dominated dolo-packstone and muddy dolostones

• Class 2(100-20μm) – grain dominated packstones; fine-med crystalline, grain dominated dolo-packstone; medium crystalline muddy dolostones

• Class 3(

Rock fabric classes• RMA transforms exist for each class

to predict permeability:1. k = (45.35x108)xφip8.537 (r=0.71)2. k = (2.040x106)xφip6.38 (r=0.80)3. k = (2.884x103)xφip4.275 (r=0.81)

• In reality there is a continuum from mudstone to grainstone and from very fine dolostone to coarse crystalline dolostone

• Diagenesis can produce unique types of interparticle porosity that need to be considered, e.g. collapse of vuggy fabrics may result in “diagenetic particles”

After Lucia, 1999

Graph is both limestone and dolomite non-vuggy fabric; dotted lines RMA


Capillarity gives pore geometry



PresenterPresentation NotesExamining samples from within the classes, he noted the ranges of capillary pressure curve shapes that were found were similarly grouped according to class.

[Later in the presentation I show a method to quantify a set of defining parameters related to capillary pressure curve shapes These parameters can be compared and mapped against log data (for example), to provide a means of prediction outside the cored interval.]

The point about diagenesis can be taken further here: it is possible for diagenetic alteration to destroy completely the original fabric of the carbonate. In rocks of this nature, the PSD is paramount in the delineation of partitions within the samples.

Porosity/saturation relationships• A common way to relate porosity,

permeability and water saturation is to use a J-function:

• In reality Sw = f(Pc, φip, rock-fabric) where Pc is related to reservoir height

• Each rock-fabric class has a defined relationship to water saturation and height

φθσKPcJ

cos162.3

=

Conversion Values Laboratory (Hg/air/solid)

Reservoir (Oil/water/solid)

σ (dynes/cm) 480 28

θ (degrees) 140 44

Water density (g/cc) 1.04 0.88

φθσKPcJ

cos162.3

=

After Lucia, 1999


PresenterPresentation NotesLeverett established a relationship between capillary pressure curves and the “hydraulic radius” of the pore system (estimated by square-root of the permeability@porosity ratio (permeability being in units of metres-squared)).

J curves can be plotted against water saturation data to derive a link from pore system characteristics to fluid saturation.

Porosity/permeability/saturation relationships• Each rock-fabric class

integrates permeability and water saturation with interparticle porosity and reservoir height:

Sw = A*HB * φipC

A B CClass 1 0.0222 -0.316 -1.745Class 2 0.1404 -0.407 -1.440Class 3 0.6110 -0.505 -1.210 After Lucia, 1999


PresenterPresentation NotesLucia derived some average relationships for his basic rock-type classes.

Thus, links are established back to rock fabric and from there to the potential distribution of the different rock types in the context of sedimentary facies within the reservoir.

A word on vugs - I



PresenterPresentation NotesVugs – I, II

A “vug” is defined as a pore of approximately >= 2x the volume of the prevailing particle size

Vugs can exist at the microscale, where the prevailing particle size/type is calcite mud or micrite

Or they can be solution voids big enough to put your fingers into when captured on core

Or they can be caverns and other macro-scale solution features often associated with subaerial exposure to acidic meteoric (vadose) water or fresh-water phreatic systems (sub-water table)

An example of the latter: Monte Alpi Field in the Southern Apennines in Italy: the reservoir rock is entirely restricted to a system of criss-crossing, leached fissures situated within massive, zero porosity carbonates. This is not a layered-system reservoir!

Separate-vug pore-space• Separate-vug pore-space can be

connected inter-particle porosity• Typically fabric selective such as

intrafossil (chambers) or dissolved grains

• Larger examples are fossil moulds or dissolved crystals in mud-dominated lithologies

• Grain-dominated rocks may show composite moulds –solution fabrics?

After Lucia, 1999


PresenterPresentation NotesVugs are defined as any pore that is at least twice the size of the containing fabric particles. Thus, in micrite, pores could still be very small yet are capable of being characterised as “vugs” sensu strictu if a little amount of dissolution has occurred, for example, and enlarged the interparticle pores to be larger than the fine micrite crystals.

Separate-vug systems are not connected vug-to-vug directly.

Separate-vug porosity increases overall pore sizes and possibly overall porosity, but may not contribute to overall permeability; that will depend on the nature of the connecting interparticle pore system between the vugs.

Fabric-selective dissolution pore systems are fairly common in carbonates. For this reason, high porosity in carbonates does not always infer high permeability.

Touching-vug pore-space• Significantly larger than particle size

and forms an inter-connected pore system

• Usually non-fabric selective –caverns, breccia, fractures and solution-enlarged features, karsts

• Fractures are included because of the pore-type not the way in which they formed

After Lucia, 1999


PresenterPresentation NotesTouching-vugs are what it says on the tin. These are large pores (up to cave size!) that transcend the rock fabric and physically connect to neighbouring vugs.

Petrophysics of vuggy pore-space • Separate vugs are connected through

the matrix

• Can significantly increase porosity but may not impact permeability

• Separate vugs usually considered to be oil-saturated unless interparticle pore-space is impermeable

• Intragrain microporosity may trap water leading to high water saturation within an otherwise productive interval

• Touching vugs lead to greatly improved permeability but are difficult to characterise and predict e.g. fractures

Plot shows bi-modal nature of intergrain and intragrain microporosity

After Lucia, 1999


PresenterPresentation NotesVugs can show up clearly on the MICP data. Often they manifest as a different “plateau” on the MICP curves. MICP curve actual appearance in the presence of vugs will depend on the nature of the vug connectivity.

A word on vugs - II



PresenterPresentation NotesVugs – I, II

Lucia categorised vugs as “separate” or isolated voids and “touching” or connected voids.

The former can provide high tortuosity to a pore system and hence boost resistivity, yet form only poor quality reservoirs. An example is from the Permian Khuff of the North Field in Qatar, studied by Focke and Munn 1987

Touching vug systems can form prolific reservoirs. Lucia 2007 provides examples.

More on vugs


From Lucia 2007: needs core calibration

In Limestones –

Φsv=104.09-0.1298[DT-141.Φtotal ]

In Dolostones –

Φsv=10 4.4419-0.1526 [DT-145.Φtotal]

PresenterPresentation NotesOne way of using log data to detect connected vug systems is to determine the deviation from a porosity trend associated with interparticle pore systems visible on compressional sonic data

The theory is that isolated vugs are ignored by the sonic wave on account of the solid rock being faster than the liquid-filled pores, so the sonic pulse by-passes the vugs and travels faster through the rock to reach the detectors first

Pervasive interparticle porosity has a “normal” porosity:travel-time trend, similar to that seen in clastics

Touching vug pore systems may not be pervasive but localised, yet their interconnectedness results in them contributing to a slowing down of the sonic wave

Using the relations developed by Lucia it may be possible to quantify volumes of touching vugs and remove them from total porosity to arrive at the interparticle fraction of the pore system that is the basis of Lucia’s pore-type class system discussed earlier.

Lucia’s workflow - 2007For irreducible hydrocarbon zone – above transition zone and pre-production


• Calculate φtotal using available logs; calibrate to core• Calculate φsv using sonic travel-time; calibrate to core• Calculate interparticle porosity: φip = (φtotal - φsv)• Calculate petrophysical class number:

Log(rfn) = [A + B.log(φ) + log(Swi)] / [C + D.log(φ)]rfn = rock-fabric number (range 0.5 to 4) (aka petrophysical class)Swi = irreducible water saturationφ = total porosityA = 3.1107; B = 1.8834; C = 3.0634; D = 1.4045

• Calculate permeability using the global transform:Log(k) = [a - b.log(rfn)] + [c - d.log(rfn)].log(φip)a = 9.7982; b = 12.0838; c = 8.6711; d = 8.2965

PresenterPresentation NotesLucia’s step-by-step workflow

The values of constants in the global permeability transform restate the permeability transforms for each class into a function of the class number and the porosity within that class.

Comparison of class schemes

From Bust et al. 2011

Industry classification schemes do not all emphasise the same attributes


PresenterPresentation NotesLucia’s scheme is one way to classify porosity:permeability relationships and partition the data into usable and predictable sub-groups.

Data do appear to be following the subdivisions derived by Lucia.

But other methods exist, including “Winland R35” and Hydraulic Flow Units.

As seen from this chart, they have quite different response characteristics. The Winland method seems the least coherent with respect to the plotted data in this example from Bust et al. 2011

Hydraulic Flow Units – Amafule et al. 1993


Care is required in deriving HFU from log data if k = f(∅)

−

=e

ez φ

φφ1

zgvs

RQISF

FZIφτ

=

= 22

1

e

kRQIφ

0314.0=0.0314

𝑘𝑘∅𝑒𝑒

=∅𝑒𝑒

1 − ∅𝑒𝑒1

𝐹𝐹𝑠𝑠𝜏𝜏𝑆𝑆𝑔𝑔𝑔𝑔

k = permeability (md)Φe = effective porosityFs = shape factorτ = tortuositySgv = surface area per unit grain volume

RQI = Reservoir Quality Index (µm)Φz = pore volume to grain volume ratioFZI = Flow Zone Indicator (µm)

Amafule, J.O., Altunbay, M., Tiab, D., Kersey, D.G., and Keelan, D.R., 1993, Enhanced Reservoir Description: Using core and log data to identify hydraulic (flow) units and predict permeability in uncoredintervals/wells. Proceedings of the 68th SPE Annual Technical Conference and Exhibition, October 3-6, 1993, Houston, Texas, Society of Petroleum Engineers, pp 205-220.

𝑙𝑙𝑙𝑙𝑙𝑙 𝑅𝑅𝑅𝑅𝑅𝑅 − 𝑙𝑙𝑙𝑙𝑙𝑙 ∅𝑧𝑧 = 𝑙𝑙𝑙𝑙𝑙𝑙(𝐹𝐹𝐹𝐹𝑅𝑅)

PresenterPresentation NotesThis is a methodology that tries to capture the “hydraulic radius” by reference to pore volume, grain surface area and a shape factor.

On its own, pore volume is not in general a satisfactory predictor of permeability in carbonates; thus the premise of the ratio of these two measurements being characteristic is weakened

Including an estimate of surface area and a shape factor leans towards use of fabric and particle size and the method is strengthened in carbonates as a result

In the log domain, if the estimated permeability is a function of connected pore volume (“effective porosity” in this method) the use of their ratio is negated and the relationship may not be very precise and so be diminished in utility

Figure 4 Grouping of the MICP curves by R35 value, denoted by colour. Left: All groups. Right: selected groups illustrating that because the only criterion used in grouping MICP curves is the value of pressure at mercury saturation of 35% (red line), this approach results in a wide range of MICP curve shape, and hence pore geometry, being incorporated within each R35 group.

From Skalinski et al. 2005

Using Pc curves - 1

WINLAND R35


PresenterPresentation NotesWinland R35 is not suited to multi-modal pore systems.

On the left are groups of capillary pressure (Pc) curves (from mercury-injection), defined by their respective values of Pc at Sw(non-wetting) of 35%

On the right we focus on a few of those groups and it is evident that the value of Pc at 35% saturation says nothing about the total pore system morphology of the sample. In unimodal samples, this value of Pc can be used to estimate effective pore size, but the utility of the measurement fails completely in multi-modal pore systems.

Example summarising parameters marked in red on the crossplot

Inflexion point (~effective pore radius)

Hyperbolic asymptote (~entry pressure)

Higher asymptote (~closure effect magnitude)

Lower asymptote (~smallest pore system heterogeneity)

Slope (~overall pore system heterogeneity)











Example summarising parameters marked in red on the crossplotExample summarising parameters marked in red on the crossplot





Slope (~overall pore system heterogeneity)

Figure 5 Illustrative hyperbolic tangent function for a single MICP sample, showing its summarizing parameters and their physical meaning. These parameters are calculated for each curve, and the resulting set of parameters is partitioned for effective grouping amongst them. Such grouping forms the basis for the MICP derived rock types (Figure 8). The parameter “constant” is not shown on this plot – it is an overall scaling constant used to reflect position of samples in the axes (generally high or generally low pressures etc.). The lower asymptote is also not highlighted because it has no easily identifiable physical significance in normalized saturation data such as used in this study.

Using Pc curves - 2

From Skalinski et al. 2005


PresenterPresentation NotesHere is a methodology that permits the entire shape of the Pc curve to be captured as a series of parameter values. Different pore morphologies express as different shapes of Pc curves and give rise to a different set of defining parameters. The defining parameters related to the mathematical function (hyperbolic tangent) can in turn be related to physical phenomena related to pore-size distributions as expressed by Pc curves.

Hence, the defining parameters become characterising data related to physical attributes of pore systems, and provide a means to characterise and partition the set of Pc data.

Figure 6 The coherent points in the middle crossplot of pairs of summarizing parameters from the hyperbolic tangent functions for all curves reflect the single-mode capillary curves (left), with multi-mode capillary curves (right) plotting off-trend [NB. The colour in the middle and left plots indicates that between-plot selection is active; the selection shows that single-mode samples (i.e those with unimodal pore-size distributions) plotted in the left graph correspond to the red points in the middle graph that form a coherent relationship in terms of the two parameters plotted (constant and inflexion point). The scatter in the black points in the middle graph is much greater; these black points are “off-trend” in the middle graph; these black points are from multi-mode capillary curves and are plotted in the right-hand graph.]

Using Pc curves - 3

From Skalinski et al. 2005Brian Moss & Steve Cannon – LPS March 2019 56

PresenterPresentation NotesOn the left are unimodal Pc curves from the Tengiz oilfield in Kazakhstan

In the middle is a cross plot of two parameters (constant vs. inflexion point), from the sets of parameters calculated for each Pc curve from the field. The highlighted points falling on trend are from the unimodal Pc curves plotted in the left diagram.

On the right are multi-modal Pc curves from more complex pore morphologies and we see these have parameter pairs that plot “off-trend” in the middle plot

Thus, the parameter sets from the hyperbolic tangent analyses provide a basis to delineate partitions among the Pc data that relate to physical attributes of the pore systems studied.

Figure 7 Sorted and organized Self-Organised Map, subdivided into 9 groups (the colours) by reference to the full capillary curve data as described by the MICP tanh function summary parameters.

Figure 8 Capillary pressure data differentiated by summary parameter group; groups 1-5 (left plot) relate to multi-modal capillary data and groups 6-10 (right plot) relate to uni-modal capillary data. Groups are generally much more coherent than those found from R35 groupings alone.

Using Pc curves - 4

From Skalinski et al. 2005Brian Moss & Steve Cannon – LPS March 2019 57

PresenterPresentation NotesThe parameter sets are amenable to partitioning by a variety of techniques, here illustrated by means of a “self-organising map” (SOM). As a method that copes best with non-linear partitions in the multivariate parameter space, the SOM is particularly applicable to partitioning this type of data. This application can be “indexed” to log data to map the divisions found in Pc “space” on to core and/or log data for propagation beyond the Pc samples themselves.

Other partitioning methods may be applicable, but care is needed if the partitions are based on simple Gaussian clustering, as used in classic statistical analysis, since these methods may not reflect the convoluted and non-linear boundaries between groups in the multivariate data space under investigation.

Another approach – E.A.Clerke, Saudi Aramco - I


PresenterPresentation NotesE A Clerke I, II, III

Ed Clerke at Saudi Aramco in a body of work published over a number of years has demonstrated the interplay between macro and micro-porosity in Ghawar, Saudi Arabia.

From analysis of the mercury Pc data through use of Thomeer functions, Clerke established a number of pore types could be simultaneously presen tin the rock samples. The macro pores are flushed by water in water flood recoveries, but then the micro-pores undergo spontaneous imbibition of the water and expel oil that they contain. The micro-pores contain oil in Ghawar because the oil column height is sufficient for buoyancy to overcome their entry pressure during the accumulation phase.

Another approach – E.A.Clerke, Saudi Aramco - II





Another approach – E.A.Clerke, Saudi Aramco - III





A word on Fractures


• Some carbonates fracture more readily than others and even than clastics

• Dolomites fracture most readily because they resist compaction during burial

• Evaporites can provide support framework, but will fracture

• Faults in carbonates rarely seal• Fracture porosity is very small, generally adding

Core calibration of log analysis• Could be its own seminar• Representative core samples:

• Plugs may not be the requisite 2 orders of magnitude greater than the largest pore size; may need higher sampling to cope with heterogeneity

• Whole cores necessary, unless interparticle porosity is proved – use a size equivalent to log responses

• X-ray CT scans and a quantitative definition of heterogeneity are useful

• How many samples? At 95% confidence to within p% tolerance: 𝑁𝑁𝑙𝑙 = 200. [

𝑠𝑠𝑎𝑎𝑚𝑚. 𝑝𝑝

]2

(Ref. Hurst & Rosvoll, 1991; s = sample std.dev. and am = sample average value)

• Stress sensitivity• Responses of porosity and permeability to stress vary considerably with

pore type and particle type• May lead to a concept of a “stress facies”


PresenterPresentation NotesCore calibration of log analysis

Treated in more detail later in the seminar

Representative core samples: Plugs may not be the requisite 2 orders of magnitude greater than the largest pore size; may need higher sampling to cope with heterogeneityWhole cores may be necessary, unless interparticle porosity is proved – use a sizeof whole core sample that is equivalent to log responsesX-ray CT scans and a quantitative definition of heterogeneity are useful at both picking samples and understanding their internal morphology

It is very important to establish sampling strategies that adequately represent any heterogeneity among the samples used, both in terms of location of samples and the number used. This is true for all sampling strategies and is not confined just to carbonate characterisation. (See notes on next slide.)

Stress sensitivityResponses of porosity and permeability to stress vary considerably with pore type and particle typeMay lead to a concept of a “stress facies”

Heterogeneity

From Lucia, 2007


PresenterPresentation NotesHere are shown examples of the range of plug data heterogeneity compared to whole core samples from a couple of fields examined by Lucia.

How many samples are necessary? (See function on previous slide.)

From Hurst and Rosvoll, 1991:

No = [200 . (s/am.p)]^2No is number required to characterise a given interval s = standard deviation of a sampling distributionam = average value of a sampling distributionp = percentage tolerance for an estimate of am

No is a measure of heterogeneity within the sample used – more heterogeneity needs more samples. Essentially this is a recast measure of spread, and illustrates satisfaction of the 95% confidence interval in the presence of heterogeneity of samples.

Porosity types• Logs measure conductivity of

reservoir fluid: C=1/R• If Sw is 100% then:

Cb = (1-φ)Cm + φCf ;because Cm = 0,

Cb = φCf• In more complex reservoirs a

cementation factor m is introduced

Cb = φmCf• How does it change when

hydrocarbons are present?

From Asquith,1985


What happens with hydrocarbons and water?

• When Sw

Determination of cementation factor• Traditionally making electrical

measurements on core samples and using x-plot techniques

• Pickett plots, plot deep resistivity against porosity from logs; m is the slope of the line in the water leg

• With two porosity measurements sonic (φs) and/or density-neutron (φt) you have another method

• Sonic measures matrix porosity only D-N measures total porosity

• Doesn’t work quite so well in oomoldic limestones

t

smφφ

log)(log2

≥

)(2 stvug φφφ −=

vugtm φφφ −=Brian Moss & Steve Cannon – LPS March 2019 66

PresenterPresentation NotesHere is an illustration of the determination of “m” from plots of resitivity versus porosity in water-bearing formations.

The estimates of “m” from such plots of resistivity logs against porosity in water-bearing sections can also indicate pore system variability.

“m” is not always constant

From Lucia, 2007

From Focke and Munn, 1987Brian Moss & Steve Cannon – LPS March 2019 67

PresenterPresentation NotesResistivity based saturation calculations may be rendered difficult by distinctly “non-Archie” behaviour of carbonates.

Complex pore morphology can give rise to higher resistivity through pore tortuosity rather than through fluid distribution in pore spaces. Variation in “m” and “n” between rock-fabric classes and variable “m” functions may be characteristic of carbonates

Saturation-height functions calibrated to core data may be a better way of defining fluid saturation variation in carbonates. The need to partition the Pc data for pore morphology type, as discussed previously, applies.


0.01

0.1

1

10

100

1000

10000

0.1110100

Formation Factor vs Porosity - Core data

F

Intergranular φm ~ 2a ~ 1

“Brecciated” samplesm = ~1.25

a > 1.0

Form

atio

n fa

ctor

(Ro/

Rw) o

hm

Plug porosity %

PresenterPresentation NotesArchie parameters can be very different from standard values in the case where the pore system is not “conventional” intergranular.

See next slide

All data

Selected data

With > 1% porosity

Red dashed line approximates a = 1, m = -2

‘a’ and ‘m’ in resistivity evaluation can be very different from standard values in fractured intervals

Logging Tool response

Fabric-sensitivity is the key. - Pore type and connectivity, rather than simple pore

volume, are the controlling characteristics of interest.

- The standard suite [SP, GR, density, neutron, resistivity, sonic], responds predominantly to pore volume, and is only weakly fabric-sensitive

- Pay attention to log resolution- Need higher resolution logs – e.g. images


PresenterPresentation Notes

Standard logging tools are sensitive to pore volume, fluid content and mineralogy. Some sensitivity to pore type can be seen in the compressional sonic response, under favourable circumstances.

These tools are otherwise only weakly fabric-sensitive. In general, their vertical resolution is of the order of tens of centimetres, which can be considerably larger than the scale of heterogeneity of importance in carbonate formations.

Fine-scale log resolution is necessary to begin to characterise fabric morphology and hence derive an idea of pore morphology.

Image magic - I


From Lucia, 2007

PresenterPresentation NotesImage data can greatly assist in the task of defining fabric morphology

Examples show different gross features of carbonate examples are clearly captured in these resistivity images.

Note the scale of important texture variation is often below that of the standard suite of logging tools

Image magic - II


From Lucia, 2007




Image magic - III


From Lucia, 2007




Logging Tool response• Log signatures require more than simple mineral/porosity

discrimination- Sonic wavetrain (Stoneley) measurements - An elemental/geochemical log- A dielectric log - A magnetic resonance log- Formation testers- Production logs (with well tests)

• Such a log suite should be run in at least one key reference well, which should also be fully cored


PresenterPresentation NotesThis list of modern logging tools provides information about the formation other than simple pore volume, fluids and mineralogy

Sonic wavetrain (Stoneley) measurements Tube waves can provide permeability indicationsAn elemental/geochemical logMineralogy and carbon content in the event of tar mat/bitumen contaminationA dielectric logWater-filled porosity – provides another calculation independent of Archie and can be used to estimate Archie’s “m” and “n” parameters A magnetic resonance logResponds to total porosity without formation effects. Indicates pore size but not pore connectivity. Inference of connectivity in cases where large pores exist with a good population of smaller pores in the absence of clastic clay materialFormation testersDeterminants of pressure distribution, fluid types and flow on a small scaleProduction logs (with well tests)Determinants of pressure, pressure compartment shape and size, fluid types and flow on a larger scale

Case Study – Canada, Bitumen in Vuggy Dolomite

• Modern solutions

• LithoScanner*

• NMR (CMR*)• Shortest echo spacing captures

fluid porosity in smallest pores. Organic matter decays too fast to be measured

• Dielectric (ADT*)• Responds to water-filled

porosity from dielectric permittivity dispersion

• Deliver accurate saturation estimates in difficult conditions

*Mark of Schlumberger

Ref. SPE-166297, 2013, P.R.Craddock et al. Brian Moss & Steve Cannon – LPS March 2019 75

PresenterPresentation NotesAn example from a Canadian vuggy dolomite where the LithoSCanner* tool is used to determine the calcite:dolomite ratio and the carbon content associated with the bitumen. The NMR provides total porosity and the Dielectric provides the water filled porosity. Together, these three technologies allow the segregation of the pore space by fluid type and hence the derivation of an accurate volume of liquid hydrocarbon and flow potential.

Ref. Bust et al., 2011, SPE-142819

Workflow schematics


PresenterPresentation NotesIn both the log and core domains, the principles of the workflow are similar:

Determine rock fabricDetermine pore type, pore-size distribution and pore volume; determine the presence of fractures and/or fissures and their likely impactFrom the distribution of fabric controls and pore morphology, create electroporefacies and determine controlling data characteristics that a) allow quantification of reservoir quality, electroporefacies-by-electroporefacies and b) allow delineation and prediction by geological control of each electroporefaciesAppropriately subdivided and quantified in total, the porosity model then is used to quantify flow and hydrocarbon saturation.The static pore/fluid/permeability model thus derived is used to parameterise the 3D model of the reservoir for Reserves quantification and production modelling

Conclusions• It’s mostly about pore geometry

- need to distinguish and classify, then find log-definable partitions• Mineralogy may be important but the degree of importance

needs to be established• Pay attention to sampling plans• Use modern logging tools including images for at least the key

well(s), which should be cored• Make no assumptions as to appropriate algorithms or

methodology- Use Exploratory Data Analysis to investigate

relations/partitions/functions

• Be open for the most effective partitioning criteria• Be open to non-Archie behaviour


PresenterPresentation NotesConclusions

It’s mostly about pore geometryThere is a clear need to distinguish and classify, then find log-definable partitionsMineralogy and primary fabric may be important However, in carbonates, the mineralogy may be only weakly correlated with pore type; this relationship needs to be established for each case; equally, primary fabric may have been completely suppressed by diagenetic influences and retain minimal or zero control over pore morphology and distributionHeterogeneity and complexity within pore systems are very common among carbonate reservoirs, which necessitates certain measures: Pay attention to sampling plansUse modern logging toolsUse imageryNo assumptions as to appropriate algorithms or methodologyBe open for the most effective partitioning criteriaBe open to non-Archie behaviour

Asquith, George B., 1985. Handbook of log evaluation techniques for carbonate reservoirs, Methods in Exploration 5, AAPGBust, V.K., Oletu, J.U., Worthington, P.F., 2011, The Challenges for Carbonate Petrophysics in Petroleum Resource Estimation. SPE Reservoir Evaluation and Engineering, February 2011. SPE-142819.Chilingarian, G.V., Mazullo, S.J., Rieke, H.H., 1992, Carbonate Reservoir Characterisation: a geologic –engineering analysis part I. Elsevier. Developments in Petroleum Science 30Chilingarian, G.V., Mazullo, S.J., Rieke, H.H., 1996, Carbonate Reservoir Characterisation: a geologic –engineering analysis part II. Elsevier. Developments in Petroleum Science 44Clerke, E.A., et al., 2014. SPWLA Annual Symposium, 2014Emery, D., & Meyers, K.J. (eds), 1996. Sequence Stratigraphy, Blackwell Science, OxfordFocke, J.W., and Munn, D., 1987, Cementation Exponents in Middle Eastern Carbonate Reservoirs. SPE Formation Evaluation 2 (2); p155-167. SPE-13736Lucia, F.J., 1999, Carbonate Reservoir Characterisation. Springer, 226pp.Lucia, F.J., 2007, Carbonate Reservoir Characterisation. An Integrated Approach. Springer, 336pp. 2nd EditionMoore, C.H., 2001, Carbonate Reservoirs. Porosity Evolution and Diagenesis in a Sequence Stratigraphic Framework. Elsevier. Developments in Sedimentology 55.Skalinski, M., Gottlib-Zeh, S. and Moss, B.P., 2005, Defining and Predicting Rock Types in Carbonates –Preliminary Results from an Integrated Approach using Core and Log Data from the Tengiz Field. SPWLA 46thAnnual Logging Symposium, June 19-22, 2005, New Orleans, Louisiana, Paper Z.Skalinski, M., Se, Y., Playton, T., Theologou, P., Narr, W., Sullivan, M. and Mallan, R., 2015, PetrophysicalChallenges in Giant Carbonate Tengiz Field, Republic of Kazakhstan. PETROPHYSICS Vol 56, No. 6 (December 2015); Pages 615-647. Sung, R.R., Clerke, E.A. and Buiting, J.J., 2013, Integrated Geology, Sedimentology and PetrophysicsApplication Technology for Multimodal Carbonate Reservoirs. Saudi Aramco Journal of Technology, Fall 2013.Tucker, M.E. and Wright, V.P., 1991, Carbonate Sedimentology. Blackwell Scientific PublicationsWright and Burchette in Reading (1996); Sedimentary Facies

Some key references


PresenterPresentation NotesCarbonate references can now fill many libraries… this list provides some seminal texts and papers that have informed this presentation


PresenterPresentation NotesPlaces like these are where you can…Watch the ooliths languidly rolling into existence in the gentle surf at the water’s marginFeel the algal muds squish smoothly between your toes as you wade in the azure and calm lagoonal watersSnorkel among the delights of the more energetic environment of the fringing barrier reef, out where the breakers can be seen offshore and where the change in water colour denotes deeper water exists beyond the reef.

Alternatively, just kick back and enjoy your pina colada in the shade of the palm trees while you contemplate whether to take the lobster or the calamari for lunch….

Case study – Middle East Carbonate• Ideally both core and log data are

required for a robust HU solution

• There is a fundamental requirement for accurate depth matching of the two data sets

• Generally a RRT scheme will be in place against which these results will be calibrated

• However by starting with pre-conceived notions of variables such as HAFWL and log derived water saturations, can make this exercise a major challenge!

Dominant Lithology MRT FZI > 1

MRT FZI < 1

Mean Phi

GMean Perm

Grainstone MRT 1 N/A 18.5 63

Floatstone MRT 2 N/A 17.9 22

Grain Dominated Packstone

N/A MRT 3 20.7 5.7

Packstone MRT 4 MRT 5 14.4/17.9 6.9/2.8

Microsparite MRT 6 MRT 7 16.4/18.0 14/1.0

Muddy Packstone/ Wackestone

MRT 8

MRT 9

MRT 10

12.4 0.21

Cemented Reservoir 4.9 0.001

Dense Layers 2.3 1.5


Spreadsheet analysis

• Core data should be reviewed and cleaned of poor quality data

• The HU terms PhiZ, RQI and FZI should be calculated

• The results can be grouped by rock-type and displayed for trends or pre-determined FZI classes used

• The mean value for each FZI class is used to predict permeability from porosity:

RQI vs PhiZ

0.01

0.10

1.00

0.010 0.100 1.000

PhiZ

RQ

I

FZI 1.5

( )23

2

1)(1041

e

eziFk φ

φ−

=


Original Data

WellDepthEFMRTPOROKhLayer_NamePhi_ZRQIFZIMRT

UZ1009891501110.201125IIIG10.2520.783.1Grainstone1

UZ1009892501110.1783.7IIIG10.2170.140.7Grainstone1


UZ02710337501110.17313IIIG10.2090.271.3Grainstone1yx

UZ0267693414210.16538.8IIIE00.1980.482.4Grainstone10.0010.001



UZ1619386412210.136180IIIE00.1571.147.3Grainstone1FZI>1FZI1FZI1FZI

Quality controlCore Permeability vs Predicted Permeability

y = 1.0687x0.946

R2 = 0.9433

0.01

0.1

1

10

100

1000

0.01 0.1 1 10 100 1000

Core Permeability

Pre

dic

te

d P

erm

ea

bil

ity

MRT 1MRT 2MRT 3MRT 4MRT5MRT 6MRT7MRT 8All Data Trend

Plot of core permeability against predicted permeability shows excellent 1:1 correspondence except for the best RRT’s: r2 value is 0.943


Original Data










Porosity against Predicted Permeability

0.01

0.1

1

10

100

1000

0.000 0.050 0.100 0.150 0.200 0.250 0.300

MRT 1MRT 2MRT 3MRT 4MRT 5MRT 6MRT 7MRT 8

MRT 1, 2 & 4 are the best reservoir rock types in terms of reservoir quality: grainstones, grain-dominated packstones and floatstones

MRT 6 represents a good quality microsparite

MRT 3, 5, 7 & 8 are mud dominated rocktypes: muddy packstones and wackestones


Original Data










Not-used slides follow


Petrophysical Evaluation

• Two primary sources of data:• Wireline Log: in situ, in direct measurement • Core: ex situ, direct measurement

• Challenges:• Sampling issues: volume and bias• Data quality: acquisition and interpretation• Integration: correlation and calibration

• Petrophysics is not just log analysis • Reservoir evaluation is:

Petrophysics constrained by geologyBrian Moss & Steve Cannon – LPS March 2019 85

Describing the rock fabric• Spatial geological data

integrated with quantitative engineering data leads to a description of the reservoir rock fabric

• Results in a generic petrophysical classification of the carbonate pore space

• All inputs are needed to describe the reservoir for 3D reservoir property modelling

Bureau of Economic Geology, UTAF. Jerry Lucia


Use of bulk volume water (BVW)

• Bulk volume water is product of water saturation and porosity:

BVW = Sw x φ• BVW indicates whether a reservoir is at

irreducible water saturation when all the water is held by capillary forces and therefore produces dry oil

• X-plots of Swirr and porosity on BVW charts identify where water free production might occur



CARBONATE RESERVOIRS �How to choose the right petrophysical evaluation method – Evaluation of mineralogy, pore geometry, saturation and permeabilityWhat are carbonates?Slide Number 3How are carbonates formed? Where?Comparison: clastics and carbonatesClastic vs Carbonate sequencesDepositional processesCarbonate sediments and controlsSkeletal componentsNon-skeletal components – grains/mudCarbonate cyclesHigh resolution sequence stratigraphyDiagenetic overprintingGeneralized diagenetic sequenceEarly diagenesisBurial DiagenesisBurial DiagenesisComparison of Reservoir Quality Roof collapse after dolomitizationCore to Log Integration Core to log integration (2)Core/Log CalibrationWill a carbonate reservoir produce hydrocarbons?Pore space terminologyDunham’s 1962 ClassificationFolk’s 1962 ClassificationSlide Number 27Slide Number 28Central issuePartitioning terminology - 1Partitioning terminology - 2Pore volumeBeyond pore volumePetrophysical classificationInterparticle pore-spacePorosity, particle size and capillary pressurePorosity/permeability relationshipsPorosity/permeability relationshipsTexture and pore typeRock fabric classesRock fabric classesCapillarity gives pore geometryPorosity/saturation relationshipsPorosity/permeability/saturation relationshipsA word on vugs - ISeparate-vug pore-spaceTouching-vug pore-spacePetrophysics of vuggy pore-space A word on vugs - IIMore on vugsLucia’s workflow - 2007Comparison of class schemesHydraulic Flow Units – Amafule et al. 1993Slide Number 54Slide Number 55Slide Number 56Slide Number 57Another approach – E.A.Clerke, �Saudi Aramco - IAnother approach – E.A.Clerke, �Saudi Aramco - IIAnother approach – E.A.Clerke, �Saudi Aramco - IIIA word on FracturesCore calibration of log analysisHeterogeneityPorosity typesWhat happens with hydrocarbons and water?Determination of cementation factor“m” is not always constantSlide Number 68Slide Number 69Logging Tool responseImage magic - IImage magic - IIImage magic - IIILogging Tool response�Slide Number 75Workflow schematicsConclusionsSome key references�Slide Number 79Case study – Middle East CarbonateSpreadsheet analysisQuality controlPorosity against Predicted PermeabilityNot-used slides followPetrophysical EvaluationDescribing the rock fabricUse of bulk volume water (BVW)Slide Number 88

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CARBONATE RESERVOIRS...2019/03/07 · • Carbonate sand migration - In high-energy locations, the...

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