spe-166178_geological attributes from conventional well logs

7/25/2019 SPE-166178_Geological Attributes From Conventional Well Logs

1/15

SPE 166178

Geological Attributes from Conventional Well Logs: Relating Rock Types toDepositional Facies in Deepwater Turbidite ReservoirsChicheng Xu, SPE, The University of Texas at Austin; Carlos Torres-Verdn, SPE, The University of Texas at

Austin; Ronald J. Steel, The University of Texas at Austin

Copyright 2013, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, USA, 30 September2 October 2013.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenreviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, itsofficers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission toreproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

Grain size and bed thickness are important geological attributes for sedimentary facies analysis and reservoir quality ranking

in deepwater turbidite reservoirs. Grain size controls reservoir quality and has causative or correlational effects on most well

logs. Additionally, bed thickness affects well logs in various ways because of different vertical logging-tool resolutions. Theobjective of this paper is to quantitatively classify rock and bed types based on conventional well logs to assist facies

interpretation and stratigraphic reservoir modeling.

We model physical properties and well-log responses originating from clastic reservoirs with different grain-size distributionsin the context of deepwater turbidite systems. The model accounts for fluid effects introduced by capillary transitions and

salty connate water. Petrophysical relationships are examined between grain sizes and pore geometry as inferred from the

combined effects of initial connate-water saturation and reservoir capillary pressure. From modeling, we derive several

quantitative petrophysical attributes which correlate significantly with grain size. A list of such attributes includesvolumetric concentration of shale, total porosity, neutron-density porosity difference, and reservoir quality index (RQI). Rockclassification is then performed based on a relevant set of attributes to detect and rank different rock types with specific

grain-size distributions. Concomitantly, a quantitative rock classification scheme using three-dimensional Thomas-Stieber

diagrams constructed with gamma ray, bulk density, and resistivity logs is used to classify petrophysical zones based on theiraverage bed thicknesses. A dual-attribute rock classification scheme is proposed to better associate rock types with

depositional facies.

The above-mentioned rock-classification and bed-typing methods are synthesized and applied to a turbidite field from the

Gulf of Mexico. We perform facies analysis based on the vertical succession of rock types (grain sizes) and the inferred

variations of bed thickness. The field case demonstrates that these two geologic attributes have great potential in reducing

uncertainty and non-uniqueness in the construction of stratigraphic reservoir models. Results indicate that the classified rock

types and their petrophysical ranking agree with core data while the interpreted facies fit into the depositional context asinferred from core descriptions and seismic horizons.

Introduction

Rock classification (or simply rock typing) is an emerging reservoir description tool that elicits broad research interests in the

oil and gas industry. It has become increasingly important in modern reservoir description to invoke close integration of

multi-discipline, multi-physics, and multi-scale subsurface data including pore imaging, core measurements, well logs,seismic amplitude data, well testing, and production surveillance (Gunter et al., 1997a). Interestingly, the definition of rock

type still remains ambiguous and sometimes arguable in many situations as it depends highly on the specific description

purposes to be achieved by geoscientists and engineers. Geologists consider rock types as depositional facies or lithofacies

which emphasize the genesis of rock formations to enable 3D stratigraphic reservoir modeling (Fisher, 1982; Kerans and

Tinker, 1999; Muto and Steel, 2007; Slatt, 2007). Petrophysicists define rock types based on pore geometry that relates allstatic and dynamic petrophysical properties (Archie, 1950 and 1952). Reservoir and production engineers group rock types as


2/15

2 SPE 166178

flow units that are stratigraphically continuous intervals of similar geologic and petrophysical features to upscale reservoir

grids for efficient fluid-flow simulation (Gunter et al., 1997b). Noteworthy is that reservoir characterization teams are

working toward a common objective: to construct a verifiable reservoir model populated with accurate petrophysicalproperties for reserves estimation and production forecasting (Lucia, 1999). Therefore, developing new rock classification

schemes and workflows that serve multiple characterization purposes from different disciplines remains a challenging but

important task.

For subsurface studies, whole core samples and well logs are the main sources of high-resolution geological data. Routinegeological work infers grain size and bed thickness from slabbed core samples. However, in most cases whole core spans a

very limited segment of the reservoir. Log-core integration is therefore necessary to extrapolate geological properties fromcored depth intervals to remaining wells and into the reservoir. Rock classification based on core data is relatively simple

because petrophysical properties are rigorously measured under the same protocol. However, rock classification using

downhole well logs is more challenging and is subject to great uncertainty. Four technical issues immediately arise when

moving from core data to well logs for rock typing, namely data quality, indirect measurements, variable reservoirconditions, and scale discrepancy (Xu, 2013). These compounded technical problems were initially defined by Archie as the

geometrical problem (1950). In the present paper, we aim to address the issue of indirectness. Indirectness lies in using

physical measurements to estimate petrophysical properties and interpret geological attributes. In fact, most well logs are

simultaneously sensitive to grains, texture, structure, and fluids. Table 1.1 summarizes the sensitivity of various well logs to

rock petrophysical properties and geological attributes. It is found that none of the logs are directly sensitive to grain size andonly a few logs have good sensitivity to bedding structure. Grain size information can only be indirectly inferred from the

pore geometry formed in-between grains. Moreover, well logs highly sensitive to pore geometry and bedding structure arealso significantly influenced by fluid content, as in the case of resistivity logs. Therefore, fluid effects on well logs need to be

cautiously accounted for before inferring grain and bed properties.

Table 1Sensitivity of well logs to petrophysical properties and geological attributes (modifiedfrom Serra and Abbot, 1980).

Composition Texture Structure Fluid

Gamma Ray High Low Low Low

PE Factor High Low Low Low

Neutron Porosity Medium Medium Low High

Bulk Density Medium Medium Low High

Electrical Resistivity Low Medium High High

Acoustic Slowness High Medium Medium High

Magnetic Resonance Medium High Low High

The ultimate goal of rock classification is to associate petrophysical rock types with geological framework and reconcilethem with seismic facies to propagate rock-type associated petrophysical properties into the reservoir model. This endeavor is

difficult without understanding the cause-and-effect relationship between pore geometry and rock geological attributes such

as grain size and bed thickness. Hence, it is necessary to investigate the sensitivity of well logs to those geological attributes

and the associated pore geometries in the presence of different fluid-saturation conditions resulting from either hydrocarbon

migration or mud-filtrate invasion. In this paper, we show the causative and correlational impacts of grain size on various

types of logs. In addition, the relationships between gamma ray log and other logs are interpreted to reveal the underlying bedthickness information as emphasized in Thomas-Stieber diagrams. We propose an integrated petrophysical workflow and a

dual-attribute rock classification scheme to better relate rock types to geological facies in deepwater turbidite reservoirs. A

field case from the central Gulf of Mexico is used to test the workflow and classification schemes.


3/15

SPE 166178 3

Grain-Size Analysis from Well Logs

Grain size is directly associated with depositional energy. Large grains are deposited under highly energetic flow conditions,whereas small grains are deposited under low flow energy (Reading, 1996). Grain size information provides key data for

geologists in their facies interpretation work (Glaister and Nelson, 1974). In addition, grain-size distribution determines the

pore-size distribution of most clastic rocks when diagenesis effects are not significant. Consequently, grain size information

becomes a good indicator of reservoir quality in terms of hydraulic capacity.

Grain size is typically measured from core samples or outcrops with a laser particle size analyzer (LPSA). To date, there

exists no effective physical measurement capable of measuring grain size directly at downhole conditions. However, there aresome causative and correlational effects between grain size and physical logs. In the following section, we briefly review the

relationships between grain size and gamma ray, neutron, density, resistivity, and NMR logs.

Gamma Ray Log. Gamma ray logs are sensitive to the natural radioactivity of rock formations. The radioactive elements (U,K, Th) are predominantly associated with small grains such as silt or clay (Serra and Serra, 2003). Consequently, often there

is a correlation between gamma ray log and grain-size distribution. In fact, the gamma ray log has been long used as a single

indicator of grain size in many geological field studies because the gamma ray log is more readily available than other logs

(Pirson, 1983; Rider, 1990). In clastic reservoirs, the gamma ray log is sufficient to separate clean sands, shaly sands, and

shales (or siltstones). Figure 4 Track 2 shows the typical gamma ray log responses of clean sands, shaly sands, and shales (orsiltstones). However, the gamma ray log alone cannot effectively distinguish clean sands of different grain sizes.

Neutron Porosity and Bulk Density Log. Both neutron porosity and bulk density logs are dominantly sensitive to total pore

volume and pore-filing fluids in clean sands. In many clastic reservoirs, the porosity formed by grain deposition has small

variability. However, the total pore volume will be significantly changed by cementation which is mainly comprised of silts

and clays (Neasham, 1977). As a result, low porosity rock tends to be associated with silt- or clay-size grains. In addition, the

presence of silts or clays will increase the bound water volume that remains in the rock pore system after hydrocarbonmigration. The hydroxyl in clay minerals gives rise to a high hydrogen index which increases the apparent neutron porosity.

On the other hand, clay or silt cementation increases the bulk density of rocks when solid cement replaces the original fluid in

the pore space. As a result, neutron and density porosity logs separate when they are plotted on the same track using the

correct matrix. The separation or difference between neutron and density porosity is an indicator of clay volume. Figure 4,Track 3 shows the typical neutron porosity and bulk density logs across clean sands, shaly sands, and shales. Neutron-density

separation is smallest in clean sands and largest in shales. However, neutron porosity and bulk density logs still lack the

ability to differentiate sandstones of different grain sizes but with the same porosity.

Resistivity and NMR Logs.We use a simple pore network model to study the cause-effect chain connecting a series of rockproperties including grain size, pore size, capillary pressure, water saturation, electrical conductivity, and NMR transverse

relaxation time T2. Figure 1 shows two thin sections of pore network systems formed by large (Type A) and small (Type B)

grain sizes, respectively. Large grain sizes generate large pore sizes accordingly, resulting in a T2distribution with large T2values (Fig. 2). When these two rocks are subjected to the same capillary pressure induced by hydrocarbon migration at

reservoir conditions, rock type A exhibits both lower capillary-bound water saturation and clay hydration water saturation

(Hill et al., 1979), and consequently lower electrical conductivity (Fig. 3). Therefore, in a hydrocarbon-bearing zone,resistivity logs become sensitive to pore size, i.e., petrophysical rock types. Rock types with higher hydraulic conductivity

tend to have lower electrical conductivity (or higher resistivity) in hydrocarbon-bearing zones in the presence of capillary

pressure. Figure 4 Track 4 describes the typical resistivity log responses observed across clean sands, shaly sands, and shalesin a hydrocarbon zone.

Figure 1Thin sections describing pore network systems formed by different grain sizes. Left Panel: large grain size; Right Panel:small grain size (Xu et al., 2013).


4/15

4 SPE 166178

Figure 2Modeling of pore-size (left panel) and NMR T2-distributions (right panel) for two different rock types (Xu et al., 2013).

Figure 3Modeling of drainage capillary pressure and resistivity-height relation for two different rock types. Pc: capillary pressure(left panel); HAFWL: height above the free water level (FWL) (right panel); Rt: bulk rock resistivity (Xu et al., 2013).

Figure 4Simulated well logs across different rock types. Track 1: Depth; Track 2: Gamma ray log; Track 3: Neutron porosity(sandstone porosity units) and bulk density logs; Track 4: Resistivity log; Track 5: Rock types. Logs are simulated with softwareUTAPWeLS

1.

1The University of Texas at Austins Petrophysics and Well Log Simulator


5/15

SPE 166178 5

Correction of Free Water in Capillary Transition Zones.The presence of free water in a capillary transition zone disrupts

the correlation between electrical conductivity and hydraulic conductivity. Xu and Torres-Verdn (2012) proposed a

correction method by linking Leveretts RQI with in-situ capillary pressure (Pc) and initial connate-water saturation (Sw)using the empiricalJ-function model (Darling, 2005; Torres-Verdn, 2012):

1

( ) ( )b w wirr bw wirr wS S

S S aJ J S a

= + =

, (1)

together with Leveretts capillary pressure model (1941), given by:

1( ) cos

RQI cos ( )w w wirr b

c c

J S S Sk

P a P

= = =

, (2)

where Swis initial water saturation, Swirris irreducible water saturation (which is set to 0.01 lower than the minimum watersaturation in the entire reservoir column, Darling, 2005); aand bare constants derived from core-measured capillary pressure

curves; cos is the product of interfacial tension and contact angle, which is assumed constant in the same reservoir; Pcis

in-situ reservoir capillary pressure in psi.

Shale Distribution and Bed Typing from Well Logs

Bedding structure together with bed thickness is often a key parameter for geologists to interpret sedimentary facies

(Campbell, 1967). Thinly bedded or laminated formations are persistent only in some special depositional facies (Passey etal., 2006). High-resolution image logs provide the best estimation of bedding structures but are not commonly available in all

wells. Thomas-Stiebers diagram (Thomas and Stieber, 1975 & 1977) is a widely applied petrophysical interpretation toolused to diagnose shale distribution in siliciclastic reservoirs as being laminated, dispersed, or structural (Fig.5; Torres-Verdn,2012). Thomas-Stieber diagram assumes two end members: clean-sand and pure-shale. Several trends are also defined to

reveal different geological processes yielding different shale distributions which are associated with different mixing laws

reflected on logging measurement physics and the underlying petrophysical properties (Xu and Torres-Verdn, 2013). As a

consequence, rocks with different values of shale volumetric concentration and distribution appear in different locations

when projected onto a Thomas-Stieber diagram (Fig. 6). Xu and Torres-Verdn (2013) proposed a graphical clusteringmethod to classify petrophysical zones of different bed thickness in a 3D Thomas-Stieber diagram (Fig. 7).

(a) (b) (c)

Figure 5Three nominal forms of shale distributions in siliciclastic reservoirs: (a) laminated shale, (b) dispersed shale, and (c)structural shale.


6/15

6 SPE 166178

Figure 6Thomas-Stieber diagram constructed with bulk density and gamma ray logs. Red circle dots: pivoting points; Blue dashed

lines: trend lines; shaded elliptical regions: uncertainty bounds.

Figure 7Rock classification and bed typing via k-means cluster analysis (Press et al., 2007)on a 3D Thomas-Stieber diagramconstructed with gamma ray, bulk density, and resistivity logs.


7/15

SPE 166178 7

Figure 8Integrated dual-attribute rock classification workflow based on well logs and core data.

Dual-Attribute Rock Classification Workflow

Rock types defined by grain size and bed thickness should be integrated to achieve a better reservoir description in multi-well

field studies,. Figure 8 shows an integrated dual-attribute rock classification workflow based on standard petrophysical log-analysis and core-calibration. Grain size and bed thickness information are propagated from cored intervals to log-covered

intervals via step-by-step calibration and validation. Therefore, the final rock types derived from well logs in multiple wells

are geologically consistent for facies interpretation and stratigraphic reservoir modeling. Furthermore, rock type information

needs to be returned to standard petrophysical analysis for more accurate rock-type-specific processing.

Field Case: Marco Polo Field, Gulf of Mexico

The Marco Polo field is a Miocene turbidite oil field located in the central Gulf of Mexico (Fig. 9; Contreras et al., 2006).Marco Polo minibasin resides in a tectonically active salt environment (Fig. 10; Jackson et al, 2008). The depositional system

is interpreted as a submarine fan complex developed in a mini-basin with stacked progradational turbidite lobes. Reservoirsconsist primarily of sandy turbidite facies interbedded with some siltstone and muddy debris-flow facies. Reservoir rocks are

mainly unconsolidated sandstones of very fine to fine- to medium-size grains. Thin-bedded zones are common across the

entire reservoir. In this study, we evaluate only the two hydraulically connected, hydrocarbon-bearing sandstone units: M40

and M50 in three wells. These sands are buried at depths between 12,000 and 13,000 ft TVD and dip toward the west (Fig.11). Core measurements indicate that the structureless and plane-parallel laminated sandstones exhibit porosities as high as

35%, as well as 100 2000 md of nominal permeability. Well logs from three wells are used for rock typing. Well no. 1 wasdrilled with oil-base mud and penetrated a capillary transition zone and the FWL. Wells nos. 2 and 3 were drilled with water-

base mud where the M40-50 sands are located more than 500 ft above the FWL (Fig. 11).


8/15

8 SPE 166178

Figure 9Geographic location of the Marco Polo field in the Gulf of Mexico (Contreras, 2006).

Figure 10Salt tectonics background of the Marco Polo minibasin (Jackson et al., 2008).

Figure 11Seismic cross-section with the three well trajectories considered in this paper.


9/15

SPE 166178 9

Core-Based Rock Typing and Grain Size/Bed Thickness Interpretation

Petrophysical rock types are first established using routine core porosity-permeability measurements. Six petrophysical rock

types were identified from the histogram distribution of Leveretts RQI (Fig. 12). Table 2 summarizes the statistical

distributions of petrophysical properties for each rock type. We observe that rock types 1 - 3 exhibit a largely overlappingporosity range. Their average horizontal permeability exhibits a ratio around 2 2.5, which indicates that their average pore-

throat radius ratio is close to 1.5. Laser grain size measurements for rock types confirmed that the controlling factor for

permeability was mean grain size. Reservoir quality decreases with decreasing grain size. Grain size measurements for rock

types 2, 4, and 5 are listed in Table 3 for comparison. Different bed types identified from core photographs are compared inTable 4 for facies interpretation.

Figure 12Core-based hydraulic rock typing via Leveretts RQI.

Table 2 Statistics of porosity, permeability, and RQI for eachhydraulic rock type.

Rock

Type

Porosity (p.u.) Permeability (md) RQI

(m)

RRT1 33.6 1.8 1099 291 56.8 7.2

RRT2 33.3 2.1 549 130 40.4 4.9

RRT3 31.5 4.4 179 63.7 23.4 3.4

RRT4 24.1 6.1 42.9 19.1 13.1 2.1

RRT5 19.7 3.9 11.3 3.9 7.5 1.1

RRT6 19.6 3.7 4 1.5 4.4 0.86


10/15

10 SPE 166178

Table 3Comparison of grain-size distribution data for rock types 2, 4, and 5.

Rock Type Grain Size Distribution Description Facies Interpretation

2

Dominated by sand-size

grains with a narrow

distribution in the large-

size mode (i.e., goodsorting).

Turbidite channel infill

including channel lag.

4

Dominated by silt-sizegrains with only a small

proportion of sand-sizegrains. Overall

distribution is widely

spread (i.e., relatively

poor sorting).

Channel levee or channelmargin with thin, fine-

grained beds.

5

Dominated by silt-sizegrains with an overall

wide distribution (i.e.,

poor sorting).

Overbank depositspossibly including thin

debris flows.

Table 4: Comparison of core photographs of different bed types.

Rock Type Core Photos Description Facies Interpretation

Thick sand

Structureless coarse-grained

sandstone with no clearinternal lamination.

Amalgamated turbidite

channel deposits.

Laminated sand-shale

Interbedded sand-shale

sequence with variable net-to-gross ratio.

Channel levee or margin

deposits.

Thick shale or siltstone

Fine-grained shale or

siltstone.

Overbank deposits, possibly

with debris flow.

Sand Silt

Sand

Silt

Sand Silt


11/15

SPE 166178 11

Log-Based Rock Classification and Bed Typing

Core-calibrated, conventional well-log analysis is first performed to provide relevant petrophysical properties, such as

volumetric concentration of shale, total porosity, and connate-water saturation. We classify rock types via k-means cluster

analysis (Press et al., 2007) on several relevant petrophysical attributes including shale volume, total porosity, neutron-density separation, water saturation in the irreducible zone, and well-log-derived RQI as in Eq. (2) in the capillary transition

zone. The classified rock types are consistent with core data concerning both petrophysical properties and grain size. Figure

13 shows an interval with log-based rock classification results. A clear fining-upward grain-size trend is observed in both

core and the log-derived rock types in the interval of 13,574 13,580 ft. The high-resolution grain-size information providesgeologists useful data for facies interpretation.

Figure 13Example of a fining-upward unit identified from vertical stacking of petrophysical rock types. Track 1: Depth; Track 2:Gamma ray log; Track 3: Neutron (in apparent sandstone porosity units) porosity and bulk density logs; Track 4: Inductionresistivity logs; Track 5: Rock types of different grain-size distributions; Track 6: Core permeability.

Relating Rock Types to Geological Facies in a Single Well

In a single well, we analyze the stacking pattern or trend of the classified rock types that bear grain-size and bed-thickness

information. Similar neighboring rock types are grouped as distinct facies from the adjacent rock type groups. Figures 14 and15 show two intervals with their sedimentary facies interpretations. Amalgamated channel facies are composed mainly of

thick and coarse-grained sandstones. Both bed thickness and grain size decrease in the levee or channel margin facies.Overbank deposits are mainly shales or siltstones. The interpretation is consistent with observations contained in Table 3 and

Table 4.


12/15

12 SPE 166178

Figure 14Rock typing and facies interpretation in the key well of the Gulf-of-Mexico case. Tracks 1-4: basic well logs; Tracks 5-6:standard well-log analysis, saturation-height for rock types 1 to 3; Tracks 7: distribution of rock types; Track 8: interpretedsedimentary facies.

Figure 15Rock typing and facies interpretation in the key well of the Gulf-of-Mexico case. Tracks 1-4: basic well logs; Tracks 5-6:standard well-log analysis, saturation-height for rock types 1 to 3; Tracks 7: distribution of rock types; Track 8: interpretedsedimentary facies.

8

8


13/15

SPE 166178 13

Stratigraphic Correlation with Multi-Well Logs. After rock classification, bed typing, and facies interpretations are

performed in individual wells, a multi-well cross-sectional view can be constructed for stratigraphic analysis. Figure 16

shows the corresponding results from the three well penetrations. The locations of these wells in a submarine fan complexwith stacked progradational lobes are consistent with the interpretation made from seismic horizons. Stratigraphic analysis

together with the estimated petrophysical properties can then be integrated with seismic amplitude data for populating a

reservoir model.

Figure 16Use of rock-type and bed-type distributions in multiple wells to assist sedimentological and stratigraphic interpretation.The upper panel shows the Thomas-Stieber diagram constructed with bulk density and gamma ray logs in each well below. Thelower panel shows the log tracks in each well with the following information: Track 1: Depth; Track 2: Gamma ray log; Track 3:Neutron (in apparent sandstone porosity units) porosity and bulk density logs; Track 4: Induction resistivity logs; Track 5: Bed type;Track 6: Rock type of different grain-size distribution.

ConclusionsGrain size and bed thickness are two geological attributes that can be interpreted from conventional logs in turbidite

reservoirs. The petrophysical quality of young deepwater turbidite reservoirs is controlled chiefly by sedimentary grain sizes.

Therefore, a link exists between petrophysical rock types and depositional facies which can be effectively applied in reservoir

development efforts. Thomas-Stieber diagrams are useful in identifying thinly bedded or laminated zones which provide

additional aid for facies interpretation. We propose an integrated petrophysical workflow that combines grain-size and bed-thickness attributes to reduce uncertainty in stratigraphic reservoir modeling. Reservoir capillary transition effects on

resistivity logs need to be corrected for more accurate estimation of grain sizes. In the deepwater Gulf-of-Mexico field case,

we confirmed that multi-well rock typing can assist sedimentary and stratigraphic studies which have potential applicationsfor exploration in frontier basins or in deepwater environments, where subsalt or pre-salt reservoirs cannot be clearly imaged

with seismic data and well drilling is limited due to high rig costs.

30 40 50 60 70 80 90 100

1.8

2

2.2

2.4

2.6

2 2

2886

255

55 2255

2555 55556

65

5555

55

22 22

2566666

4 1 665

50

50 7

75

0

0500 53

7 7

7737

7755 7

3

75

5000 0 5

5 5 7556

66 6

6 55

9

7

5

555 5

22222 222 2222

222225 2 2225

222

222 222 222

2

5

54

6

2 2 2222 222 2 222

26

66

5

7

599 9

77

97777 7

7

7

9 9

977777 9

7

7

77

75 7

7 7

77

77

77

77

9 99

99

999

99997

3 3

7

7

7

3

00

0 0 77 7

333

00

0000

0000

0 00 0

000 70

0 3 7

5 777

75

733 300 335000

5

5

6

5

500 0

000 5555 77

7

00 330

7

5 97

7

5

9

9

77

5

7

777

505 5 7

9

9

77

9 9

25

77

73

5 500000

05

5 5 55

55

5555

56

6 55

2 2222 2222222 22222

222 2222

2 22 2 22222

GR (GAPI)

RHOB(G/CM3)

30 40 50 60 70 80 90 100

1.8

2

2.2

2.4

2.6

2.8

222

2226 2

2255 25 525

5 2 22225 22 22666 666

7

773330000073 70030

0 7 7

773333

377

57

337

3300

000 000

3 333 370 777

05

770 7

7

5

6 222

55

5 595

755

5

5

62 2222 2 2

2 2222 22222 2222 222

2

22

2222 2222

2 2222228

5

55777 7

7 9 99

9 9977 9 997

7

77

7 99

99977 997

79

97

977 7

7 9

7 7

779

999 99 999

99999

979

77775

775 7

73 7 777333300 0050 50 5055 557

50 7

7

7703 37

5

5

6 5 2 22

2 2222 2 22

2222222

2222

22 22 22 222 2

GR (GAPI)

RHOB(G/CM3)

30 40 50 60 70 80 90 100

1.8

2

2.2

2.4

2.6

2.8

2228 22 2 22

22

22 22 2

222 222 226

2822

55 2 22 2222 22 2 222

222 22

2 2

29

5

77

000 00000 5

500000000 5 7700

30 700 05 5

7705 5

550 500 5 55500 550 5 55555 9

2

5

2222 2

2

2255

29

9

6

6 6 6 5 2 222

2222

55

5 5

66

5 9995 5555555

56 5

97

6 5 5555

9555 5

55

9

97

5

5

555

99

6

1 6 5

29

97 775

5 5555500

65

97775 5 977

55

775555 555000 550 55

5555505 7

555 7

9997 9

977 7 999

599

9 95

9955

5 22 2

22 22 2

-12

22

222

2222

222 22 2

-1

-1

9

7

GR (GAPI)

RHOB

(G/CM3)


14/15

14 SPE 166178

ACKNOWLEDGMENTSWe would like to thank Anadarko Petroleum Cooperation for providing the field data used for analysis and verification. The

work reported in this paper was funded by The University of Texas at Austins Research Consortium on FormationEvaluation, jointly sponsored by Afren, Anadarko, Apache, Aramco, Baker-Hughes, BG, BHP Billiton, BP, Chevron, China

Oilfield Services, LTD., ConocoPhillips, ENI, ExxonMobil, Halliburton, Hess, Maersk, Marathon Oil Corporation, Mexican

Institute for Petroleum, Nexen, ONGC, OXY, Petrobras, PTTEP, Repsol, RWE, Schlumberger, Shell, Statoil, Total,

Weatherford, Wintershall and Woodside Petroleum Limited.

Nomenclaturea : Linear coefficient in empiricalJfunction, []b : Exponent in empiricalJfunction, []

J : LeverettsJfunction, []

k : Permeability, [mD]p : Density function of pore-throat size, []Pc : Capillary pressure, [psi]

R : Pore-throat radius, [m]

Rt

: Bulk rock resistivity, [ohm.m]

Rw : Connate water resistivity, [ohm.m]Sw : Water saturation, [frac]Swirr : Irreducible water saturation, [frac]

T2 : NMR Transverse relaxation time, [ms]

: Total porosity, [frac]

: Interfacial tension, [dyne/cm] : Contact angle, [degree]

List of Acronyms and Log Curve MnemonicsAHT1-9 : Array Induction Log (H) with Two Feet Resolution and 10-90 inch Depth of Investigation

BS : Bit Size LogDRHO : Density Correction Log

FWL : Free Water Level

GR : Gamma Ray Log

HAFWL : Height Above Free Water Level

HCAL : Caliper LogHGR : Gamma Ray Log (High Resolution)

K : Potassium

K_Klink : Permeability Corrected with Klinkenberg EffectLPSA : Laser Particle Size Analyzer

MICP : Mercury Injection Capillary Pressure

NMR : Nuclear Magnetic Resonance

NPHI : Neutron Porosity LogPcow : Capillary Pressure between Oil and Water

PE : Photo-Electric

PHI_LOG : Porosity from Log

Por : Core Porosity

RHOB : Bulk Density LogRHOZ : Bulk Density Log (High Resolution)

RQI : Reservoir Quality Index

RRT : Reservoir Rock TypeRT : Rock Type

SH : Shale

SS : SandstoneSw_c : Core Water Saturation

Sw_RT : Water Saturation for Rock Type

Swt_LOG : Water Saturation from Logs

T2LM : Algorithmic Mean of NMR Transverse relaxation timeT2

Th : ThoriumTVD : Total Vertical Depth

U : Uranium

UTAPWeLS : The University of Texas at Austins Petrophysics and Well Log Simulator


15/15

SPE 166178 15

References

Archie, G.E. 1950. Introduction to Petrophysics of Reservoir Rocks.AAPG Bulletin, 34(5): 943 - 961.Archie, G.E. 1952. Classification of Carbonate Reservoir Rocks and Petrophysical Considerations.AAPG Bulletin, 36(2): 278-298.Campbell, C. V. 1967. Lamiae, Laminaset, Bed and Bedset. Sedimentology, 8(1967): 7-26.Contreras A. 2006. Spatial Delineation, Fluid-Lithology Characterization, and Petrophysical Modeling of Deepwater Gulf of Mexico

Reservoirs through Joint AVA Deterministic and Stochastic Inversion of 3D Partially-Stacked Seismic Amplitude Data and WellLogs. Ph. D. Dissertation, The University of Texas at Austin.

Contreras A., Torres-Verdn, C., Chesters, W., Kvien, K., and Fasnacht T. 2006. Extrapolation of Flow Units away from Wells with 3DPre-Stack Seismic Amplitude Data: Field Example. Petrophysics, 47(3): 223-238.

Darling, T. 2005. Well Logging and Formation Evaluation. Gulf Professional Publishing.Fisher, W. L. 1982. Depositional Systems and Environments, in Encyclopedia of Science and Technology. New York, McGraw-Hill, 5th

ed., 104-108.Glaister, R.P., and Nelson, H.W. 1974. Grains-Size Distributions, an Aid in Facies Identification.Bulletin of Canadian Petroleum Geology,

22(3):203-240.Gunter, G.W., Pinch, J.J., Finneran, J.M., and Bryant, W.T. 1997a. Overview of an Integrated Process Model to Develop Petrophysical

Based Reservoir Descriptions. Paper SPE 38748 presented at the SPE Annual Technical Conference and Exhibition, San Antonio,Texas, 5-8 October.

Gunter, G.W., Finneran, J.M., Hartmann, D.J., and Miller, J.D. 1997b. Early Determination of Reservoir Flow Units Using an IntegratedPetrophysical Method. Paper SPE 38679 presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5-8

October.Hill, H.J., Shirley, O.J., and Klein, G.E. 1979. Bound Water in Shaly Sands Its Relation to Qv and Other Formation parameters. The Log

Analyst, May-June, 3 -19.

Jackson, M.P.A., Hudec, M.R., and Dooley, T.P. 2010. Some Emerging Concepts in Salt Tectonics in the Deepwater Gulf of Mexico:Intrusive Plumes, Canopy-Margin Thrusts, Minibasin Triggers and Allochthonous Fragments. Vining, B.A. and Pickering, S.C. (eds)Petroleum Geology: From Mature Basins to New Frontiers Proceedings of the 7th Petroleum Geology Conference, 899912.

Kerans, C., and Tinker, S. 1999. Sequence Stratigraphy and Characterization of Carbonate Reservoirs. SEPM short course no. 40.Leverett, M.C. 1941. Capillary Behavior in Porous Solids. Transactions of the AIME, 142(1): 159172.

Lucia, F.J. 1999. Carbonate reservoir characterization. Springer-Verlag, Berlin.Muto, T., Steel, R., and Swenson, J. 2007. Autostratigraphy: a Framework norm for Genetic Stratigraphy. Journal of Sedimentary

Research, 77: 2-12.

Neasham, J.W. 1977. The Morphology of Dispersed Clay in Sandstone Reservoirs and Its Effect on Sandstone Shaliness, Pore Space, andFluid Flow Properties. Paper SPE-6858 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 9-12October.

Passey, Q.R., Dahlberg, K.E., Sullivan, K., Yin, H., Brackett, B., Xiao, Y., and Guzman-Garcia, A.G. 2006. Petrophysical Evaluation ofHydrocarbon Pore-Thickness in Thinly Bedded Clastic Reservoirs. AAPG Publications, Archie Series, No.1.

Pirson J.S. 1983. Geologic Well Log Analysis. Third Edition, Gulf Professional Publishing.Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 2007. Section 16.1: Gaussian Mixture Models and k-Means Clustering,

Numerical Recipes. The Art of Scientific Computing, 3rd Ed. New York: Cambridge University Press.

Reading, H.G. 1996. Sedimentary Environments: Processes, Facies and Stratigraphy. 3rd

Edition, Blackwell Science.Rider, M.H. 1990. Gamma-Ray Log Shape Used as a Facies Indicator: Critical Analysis of an Oversimplified Methodology. Geological

Society, London, Special publications, 48: (27-37).Serra, O., and Abbott, H. T. 1980. The Contribution of Logging Data to Sedimentology and Stratigraphy. Paper SPE 9270 presented at 55th

SPE Annual Fall Technical Conference and Exhibition, Dallas, Texas, 21-24 September.Serra, O., and Serra, L. 2003. Well Logging and Geology. Technip.Slatt, R.M. 2007. Stratigraphic Reservoir Characterization for Petroleum Geologists, Geophysicists, and Engineers . Elsevier Publication.Thomas, E. C., and Stieber, S. J. 1975. The Distribution of Shale in Sandstones and Its Effect Upon Porosity. Paper T presented at the 16th

Annual Logging Symposium of the SPWLA, New Orleans, Louisiana, 4-7 June.Thomas, E. C., and Stieber, S. J. 1977. Log Derived Shale Distributions in Sandstone and Its Effect upon Porosity, Water Saturation, and

Permeability. 6th Formation Evaluation Symposium of the Canadian Well Logging Society, Calgary, Alberta, 24-26 October.Torres-Verdn, C. 2012. Integrated Geological-Petrophysical Interpretation of Well Logs. Lecture Notes for Fundamental Well Logging

Course in the University of Texas at Austin, Unpublished.

Xu, C. 2013. Reservoir Description by Well-Log-Based and Core-Calibrated Petrophysical Rock Classification. Ph. D. Dissertation, TheUniversity of Texas at Austin.

Xu, C., and Torres-Verdn, C. 2012. Saturation-Height and Invasion Consistent Hydraulic Rock Typing Using Multi-Well ConventionalLogs. Paper KK presented at SPWLA 53rd Annual Logging Symposium, Cartagena, Colombia, 16-20 June.

Xu, C., Torres-Verdn, C., and Gao, S. 2013. Electrical versus Hydraulic Rock Types in Clastic Reservoirs: Pore-Scale Modeling Verified

with Field Observations in the Gulf of Mexico, U.S. 83rd

SEG Annual Meeting, Houston, 22 27 September.Xu, C., and Torres-Verdn, C. 2013.Quantitative Rock Type Codification and Interpretation Using Log-Attributes Derived from Thomas-

Stieber Diagrams in Siliciclastic Reservoirs. SEG Interpretation Journal (submitted for review).

spe-166178_geological attributes from conventional well logs

Documents