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American Journal of Oil and Chemical Technologies: Volume X. Issue X. XXX

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American Journal of Oil and Chemical Technologies

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Geological and Petrophysical Evaluation for an Oil Well

Hawez, H. , Ahmed, Z. , M.Salih, W.

Department of Petroleum Engineering, The Faculty of Engineering, Erbil, Kurdistan Region, Iraq.

Abstract:

In this paper, a sequence stratigraphy and reservoir petrophysical analysis of a single oil well (Well H) has been prepared by using an

available core data and wireline logging data with a view to characterizing the reservoir. In addition, petrophysical analysis initiated

with lithology identification and lithological panels interpreted from well log data show that the study area is characterized by sand-

shale interbedding. Moreover, appropriate logs have been used to interpret the reservoir for their fluid content, as are result;

hydrocarbons versus water bearing zones were outlined. Furthermore, the reservoir interval generally presents vertical anisotropy and

great heterogeneity with thick sand filled channel layers. The core involves mostly of red bed of sandstone with some structure-less

which is deposited in fluvial environment system. However, the core data are relatively collated to wireline data to assess and survey

the reservoir rock petrophysical properties. A hydrocarbon water contact (HWC) was obtained from 1655 m. In addition, an average

of 78.5 m of gross rock was decomposed with a counted 17.35 m of net pay exhibit average values of 0.2832 for water saturation and

0.1785 for porosity.

Keyword: Formation Evaluation, Net-pay Reservoir, Gross Rock.

1. Introduction

It is believed that most reservoir hydrocarbons have been existed in microscopic pore spaces or high conductive fractures of

sedimentary rocks, such as: sandstones. Therefore, detailed information about geological, petrophysical properties of the reservoir are

required to optimize of hydrocarbon recovery and improvement of reservoir performance and to guide the placement of production

platforms and well paths [7].

According to [10] the studying of the spatial uniformity of the saturating reservoir fluids can be crucial to oil and gas production.

Several researches which carried done by [1], [3] showed that the estimation of lithology, fluid content, porosity as well as shaliness

(a measure of cleanliness of the reservoirs) has a significant consideration in the evaluation of clastic reservoirs.

In the present study, an oil field has been quantitatively evaluated by interpreting wireline logs and core data from single well. The

main objective of the work is to determine and identify the hydrocarbon bearing sand bodies (gross rock) as well as to estimate the

amount/type of hydrocarbons in the reservoir for calculation of the reserves. This study is also to demonstrate the understanding of

the petrophysical application of wireline logs in hydrocarbon evaluation and its significance in oil exploration and exploitation in

regions similar to the study area.

The aim of this paper is to interpret the depositional environment and making a comparison between core data and petrophysical data

which used to verify log interpretation. The main techniques used to represent and describe the petrophysical and geological studies

required to construct a static model of the subsurface of the reservoir. The other objectives of this study however is to evaluate the

hydrocarbon potential in the well by petrophysical inference and analysis, and also to identify and describe the depositional

environments and the relationship between physical properties of rocks (from petrophysical analysis) and the depositional

environment of the area.

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Authors /American Journal of Oil and Chemical Technologies X (XXXX) XX-XX

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2. Literature Review

Formation Evaluation (FE) has been identified by [5] and [11]: as the process of interpreting a combination of measurements taken

inside a wellbore to detect and quantify oil and gas reserves in the rock adjacent to the well. FE data can be gathered with wireline

logging instruments or logging-while-drilling tools. Study of the physical properties of rocks and the fluids contained within them.

The researcher [4] have specified that the presence of clay particles or shale within the sand is a parameter which must be considered

in the evaluation of a clastic reservoir. Since, both formation characteristic and logging tool response can be affected by the existed

shaliness in the sand formation. In the other hand, limestone and dolomite are the characterization of carbonates, non-clastic reservoirs

and their importance should not be under estimated as reservoirs rocks [10]. In addition, the chemical nature of matrix and pore fluids

primarily impact on the response of well logging tools. Any porous network is related to its host rock fabric, therefore petrophysical

parameter, such as porosity (φ), permeability (K) and saturation (S), for any given (type of rock) are controlled by pore sizes and their

distribution and interconnection. In order to predict the spatial distribution of such petrophysical parameter on a field scale, the

reservoir characterization must be studied [4]. According to [6] in the interpretation of reservoir geophysics observation, petrophysics’

theory and rock physics data should be analyzed carefully and purposefully.

Petrophysical evaluation has been identified [8] as the continuing process of integrating and interpreting geological, petrophysical,

fluid and performance data of a reservoir sand body to form a unified, consistent description of reservoir properties throughout the

field. Furthermore, the quality, quantity, recoverability of hydrocarbon in a reservoir can be determined by applying petrophysical

evaluation within the rock proportion of the reservoir. Therefore, the potential and performance of a reservoir include porosity,

permeability and fluid saturation which are fundamental parameters of a reservoir that has the capacity to store fluid and the ability to

release and flow in it. A reservoir can be evaluated and identified by knowing the relationships among these properties. Moreover,

shaliness which is a measure of the cleanliness of the reservoir is a parameter to be considered in the evaluation of clastic reservoirs

as it can give a wrong impression of estimated petrophysical values, such as: porosity and hydrocarbon saturation when they are not

corrected for [3].

This research work has been done for a single oil well based on the use of wireline logs and core data from the well to identify and

quantify hydrocarbon reserves and evaluate rock properties in the subsurface. The petrophysical analysis with wireline logs provides

reservoir qualities (porosity, permeability, and fluid saturation), which were integrated with other data provided a guide and enhanced

exploration and development of the reservoir sand bodies. This can consequently help to optimize hydrocarbon recovery, and to

improve predictions of well and reservoir performance [2]. Each sequence can be sub-divided into smaller sediment packages called

systems tracts on the basis of characteristic well-log patterns [9]. Sequence analysis and system tract study can allow us to predict the

environment of deposition and this can be related to the petrophysics value obtained.

3. Materials and Methods

The following data were available while studying this paper:

Available following logging tools: Caliper (Cal), Gamma Ray (GR), Micro Spherical Focused Log (MSFL), Deep lateral

log (LLD/ILD), Deep Induction Log ( ILD), Shallow Lateral Log ( LLS), Bulk Density Log (RHOB), Compensated Neutron

Log (NPHI), Sonic Log (SONI).

Core plugs data (horizontal and vertical permeability and porosity).

Mud log data (mud filtrate resistivity etc.)

Core data (from 1610 to 1680 meters)

Of the total 70 m length of the well was available from 1610 to 1680 meters. A detailed description of specific 10m interval (from

1644 to 1654 meters) was picked in Table 1. A reservoir geological study should start by the supervision of recovered core and core

samples. The stratigraphic and sedimentalogical analysis of the subsurface are used to construct a faces model and regenerate the

environment of deposition. A petrophysical analysis is then performed. Core plugs are used to find out the quantity of permeability

and porosity. The core data is then planned in a composite log along with earlier processed wireline log data. The matching of depth

is momentous between core data and wireline data like errors which is caused by different measuring means might exist. Then data

should be studied together after depth shifting quality control of data and the proceeding of calculation of shale volume, porosity,

water resistivity, water saturation, net pay, reservoir rock and prediction of permeability.


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4. Results and Interpretation

4.1. Sedimentalogical and environmental description

From the core analysis, we can represent sandstone with block diagram showing fining upward sandstone with some coarser grains at

the bottom which include calcite, carbonate and mud clasts by description of core sample (Table 1). In addition, it is noticed that the

structure-less beds of sandstone are observed with thin mudstone layers. As a result of being precipitation of minerals in carbonate

rich, carbonate cement probably is noted through the core during digenesis. The red sandstone is created when iron oxidize with

minerals. As far as logged bodies representing fining upward trends with some cross bedding at the bottom, it will be assumed that

the main depositional environment is fluvial, with some potential flood plain which is represented by thin layer of mudstone. As a

result of mixed Aeolian/fluvial depositional environment, many structures-less bodies of sandstone were logged. The block diagram

in Fig.1 shows a possible depositional environment for faces body association which included structure-less medium to coarse

sandstone, thin layer of mudstone, and medium to coarse cross bedding sandstone. In addition, some of the quartz sand might follow

from fluvial reworking of Aeolian sandstones.

4.2. Petrophysical Analysis

4.2.1. Core Shifting/ Correlation

It is noted that analysis of core and wireline log data must be checked after depth matching. Sedimentary logs were not observed

strong markers, the gamma ray log were recorded and compared against zones with high mud content where the radiation values were

read. According to match theses depths, the shifting core was 1.35 m and for further analysis plotted with log data. In addition, there

are no special outings of GR to show either clean or sandy shale between intervals 1628 to 1637 and also there are two obvious

Gamma Ray excursions showing dirty sands which are explained to have lower porosity and permeability at about 1627 and 1645 as

well as two minimum porosity and permeability are noticed at about 1642 and 1760 (Fig.2).

Figure 1. Block diagram with possible depositional environment.


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Depth (m)

Description

Interpretation

Sandbodies

Seals/sources

1654-

1651.87

1651.87-

1651

1651-1649

1649-1648

1648- 1647

1647- 1644

Erosive surface with fining upward

sandstone, interbedded calcite with

coarse grain sandstone at the bottom

overlain by intraformation mudclasts in

the middle with fine grain sand at the tp.

( between 1652.33 to 1652.44, sample

removed).

2 Erosive based successions. Erosive

surface generally overlain by thick

calcite grains followed by interbedded

fine sandstone with intra formation

mudclasts.

2m interval consisting of 3 erosive based

successions. Interbedded mudstone with

sand at the bottom. Erosive surfaces

generally overlain by thin granule grains

(conglomerate) followed by coarse

sandstone.(between intervals 1649.25-

1640.37 and 1650- 1650.37, sample

removed)

Stacked very fine sandstone at the

bottom. Interbedded mudastone with

sand followed by interbedded mica with

sand at the top. (1648.12- 1648.33,

sample removed)

No recovery.

3m interval consisting of stacked

coarsening upward sandstone. Fine

grained sand at the bottom followed by

interbedded intraclasts of mud. (Between

intervals 1646.58- 1646.5, 1645.20-

1645.09, and 1644.38- 1644.21 samples

removed), (Between 1646to 1845.96 are

no recovery).

coarse grained

lateral accretion

surfaces are

observed.

Braided river

closely in sequence

with thin sheet flood

that's fine grained

deposits.

In upper parts, sheet

floods and coarse

grained lateral

accretion surfaces

are observed.

Arid terrestrial

sands.

Laterally

discontinuous and

vertically

heterogeneous

arkosic sandbodies

are likely to show

better but porositiy is

variable.

Sandbodies are

Channelized

Laterally

discontinuous

baffling shales

and drapping

sheet floods

deposits unlikely

to provide long

term traps but as

barriers will

hinder

production/recov

ery higher

permeability

sands.

TABLE 1. The description of the core (from 1644 to 1654 m).


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Old Depth (m) New Depth (m) Shift (m)

1636.55 1636.55 0.0

1638.15 1637.8 -0.35

1639.45 1639.45 0.0

1688.25 1688.25 0.0

1689.4 1688.4 -1.0

1690.6 1690.6 0.0

TABLE 2. Shift table applied for depth matching after shifted down +1.35 m linearly for whole interval.

Figure 2. Core to Log Depth Matching using Porosity data.


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4.2.2. Volume of Shale

In general, volume of shale is used to accurate especially for bound water in porosity and the calculation of water saturation in later

stages.

The main log which gives information about the presence of shale is the Gamma Ray log as well as volume of shale is calculated by

Gamma Ray log (eq.1)

IGR = 𝐺𝑅𝐿𝑜𝑔−𝐺𝑅𝑚𝑖𝑛

𝐺𝑅𝑚𝑎𝑥−𝐺𝑅𝑚𝑖𝑛 (eq. 1)

IGR illustrates shale index volume. GRmax is read from Gamma Ray log which shows the maximum value of shale and GRmin

shows clean interval of sand. Clean sand was selected because the sandstone includes high value of potassium, feldspar) which leads

to a rise in reading of Gamma Ray log. The following value GRmin =82 and GRmax =186 were used for calculating the shale volume

curve.

4.2.3. Porosity

The main log which used for calculation porosity was density tool in the following formula:

∅ = 𝜌𝑚𝑎− 𝜌𝑏

𝜌𝑚𝑎− 𝜌𝑓 (eq.2)

Where:

𝜌ma is the matrix density which is given by mudlog data , 𝜌ma= 2.71.

𝜌f is the density of fluid and is given by mudlog data, 𝜌f= 1.00.

𝜌b is the bulk density from the log.

Because of the considerable presence of shale in the reservoirs, the measured porosity was corrected for the volume of shale using

Dewan (1983):

𝜑𝑐𝑜𝑟𝑟 = 𝜑 − 𝑉𝑠ℎ ∗ 𝜑𝐷𝑠ℎ

Where:

φcorr = shale corrected density porosity

φ =Density porosity

Vsh =Shale volume

φDsh=density porosity of nearby shale

The correlation between core porosity and calculated core porosity is poor as seen in Fig.9. As a result of structure or digenetic

features in the rock, the crossplot scattering points show worse correlation. The changes between recording porosity by wireline tools

and testing porosity in the laboratory by fluid injection may cause by the amount of mineral dissolution.

4.2.4. Water Resistivity

In water bearing zone, Archie's equation uses to calculate the amount of water resistivity. The following equation use to calculate

water resistivity (Rw):

𝑆𝑤𝑛 =

𝐹 𝑅𝑤

𝑅𝑡 (eq. 3a)

Where, Sw is the water saturation, n is the saturation exponent.

In water bearing zones assumed water saturation is a 100 percent (Sw=100%), we get:

𝑅𝑤 = 𝑅𝑡

𝐹 (eq. 3b)


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Where F is the formation factor and calculated using Humble formula (eq.3c), a: is the constant value (a=0.62 in sandstone), m: is

the cementation factor (m=2.15 in sandstone).

F = 𝑎

∅𝑚 (eq. 3c)

The density log measured porosity which is used in Humble formula using equation 2.

Where: at depth 1723m water resistivity (Rw) and were obtained and then it is calculated by using Gen-6 chart. In this process, where

temperature is 136℉ at 1816m at the bottom hole depth (given from the mud-log) and the mean surface temperature was considered

to be around 9℃ (or 48℉). The final temperature was 135℉.

So, Rw is 0.033 ohm at 135℉ and 1723 m deep. After calculating Rw, the amount of salinity of the water formation can be found out

using from Gen-9 chart. The Salinity of the water formation must be about 130,000 ppm at 134℉ and Rw = 0.0343.

Water saturation based on equation 3a and it was calculated using Archie model.

4.2.5. Permeability prediction from porosity

The density tool can be used to calculate porosity for correlation with permeability in the absence of core porosity data.

It may be a difficult exercise in wells where the core data does not exist for permeability prediction. In addition, some additional data

from log and core plugs should be taken from another well to evaluate the reservoir characteristic behavior better.

4.2.6. Lithology

It is a bit trivial in lithology determination for the logged interval. After calculation of shale volume as previously mentioned, lithology

composition is calculated with linear equation for the chosen curves, i.e., density, neutron, and delta-t. This is iterated repeatedly until

the smallest error. Cross-plot of density neutron for particular interval was used as double check with the result along with sedimentary

log from core. Figure 8 shows the result is confirmed each other and the calculation can be used for the rest of interval. Yellow

coloured scattered point in the cross plot is from the whole interval, while the red coloured is from particular interval of 1630 – 1650

m to have range of 10 m in the core logged.

4.3. Net-Pay Reservoir Calculation

The knowledge about the net pay is significant for the volumetric hydrocarbon estimation, a practice that supports the merit of the

petroleum industry. There is not general definition of net pay yet, there is not universal approval of its role in integrated reservoir

analysis, there is not identified way for estimating it, and there are different survey on how to make use of it. Partially for these

reasons, net-to-gross pay makes up a main source of doubt in volumetric reserves estimates, second merely to gross rock volume. The

process of the recognition of net-pay cutoffs discuss over the years. The access is data-driven, in that it uses what is known, and also

fit-for-purpose, in that it receives statement of reservoir conditions. The result is a sounder basis for united net pay into volumetric

evaluates of extreme recovery and therefore resources of hydrocarbon.

4.3.1. Porosity cut-off

While logarithmic core permeability plotted versus core porosity, the porosity cutoff is chosen for 5.0 mD of permeability value as

shown in Fig.3.

Porosity cutoff ≥ 13

4.3.2. Shale cut-off

Porosity is first found by using density tool and is plotted against shale volume curve line. The shale cutoff is then specified according

to previous porosity selected cutoff as seen Fig. 4.

Volume of shale cutoff ≤45


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4.3.2. Water saturation cut-off

The water saturation cutoff is selected using porosity plot versus water saturation as shown Fig.5. Furthermore, water saturation is

found by water resistivity first and porosity is specified by density tool.

Water saturation cutoff ≤ 73

Figure 3. Kh vs Core plug Phi to determine Porosity cutoff (0.13).

Figure 4. VShale vs Effective Phi to determine VShale cutoff (0.45).


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5. Discussion

5.1. Petrophysical Summary

The sharp decrease of reading resistivity tool shows the hydrocarbon water contact while comparing the signal of Neutron and Density

tools at about 1655 m depth. Although, with decrease of both Neutron and Density tool can detect gas zones, there is not gas present.

Totally, logged interval was around 170 m and the gross interval was estimated 120 meters and net to gross ratio was 0.5 m. The

resistivity curves was run to provide total resistivity (Rt) and flushed zone resistivity (Rxo) and then to evaluate water resistivity (Rw).

After borehole correction using down-hole electrical logs, washout was noticed of permeable zone around 1730m and 1755m as well

as unconsolidated clays observed around 1768m. Porosity was found by density tool and permeability was predicted by various

permeability predictors.

5.2. Statistical Summary

In future study, heterogeneity, the variation of permeability and anisotropy are analyzed with the assist of statistical analysis.

Therefore, the well data is demanded for spatial information to help the growth reservoir model and further simulation.

The convenient average should be chosen for analyzing horizontal permeability (Arithmetic, Geometric or Harmonic average). In

fact, this based on the distribution of geological layers and bed geometry. There are available core data between 1610 to1680 meters

and the statistical analysis was created to measure the degree of heterogeneity in the environment system. The variance of coefficient

shows a high degree of heterogeneity in the permeability data, whereas the variance of coefficient illustrates a low degree of

heterogeneity (Table 3).

As far as the histogram is concerned, porosity illustrates symmetrical distribution that means as a single population, whilst

permeability histogram shows a skewed distribution and includes two population evidence data (Fig.6). In addition, the Lorenz plot

obviously shows high degree of heterogeneity (Fig.7).

Permeability Porosity

SD (mD) 1035.51 9.776

Variance 1071225 95.570

Cv 1.790 0.4

Figure 5. SwE vs Effective Phi to determine Sw cutoff (0.6).

TABLE 3. Statistical data for Kh and porosity heterogeneity determination.


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Figure 6. Shows histogram for porosity and horizontal permeability.

Figure 7. Stratigraphy modified Lorenz plot ordered plug data.


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Figure 9. Semivariogram

Figure 8. M-N plot shows lithology


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6. Conclusions

Analyzing the data of an available core and a suite of well logs has resulted in detailed Petrophysical analysis and well-log sequence

stratigraphy of the well. Adequate lithological interpretation and description was also carried out with the delineation of hydrocarbon

bearing reservoir sands.

In general, the grain size is medium sand porosity which is laid down in an arid environment like a braided fluvial environmental

system. The sand bed is laterally and vertically anisotropic and heterogeneous, while horizontally flow dominating. The body of faces

are baffled by heterogeneous subsurface.

Although, the net pay is around 17.3502 meters, it may be increase significantly laterally for the layers. There were no significant

signatures for faulting.

Three depositional environments have been interpreted namely: the channel and shoreface environment, fluvial channels and shoreface

sands and the reworked sandstone units. Porosity estimates is highest observed in the channel and shoreface environment.

To improve the information about the subsurface and reservoir flow unit areas should be more data collected from other wells in the

same area to better understanding of the reservoir characteristic behavior.

7. References:

[1] Adeoye, T.O. and Enikanselu, P. (2009): Reservoir Mapping and Volumetric analysis using Seismic and Well Data. Ocean Journal

of Applied Sciences, Vol. 2, Issue 4 p. 66 – 67.

[2] Adeoye, O.T. and M.O. Ofomola. 2013. “Reservoir Characterization of “Meri_T” Field (South Western, Niger Delta) from Well

Log Petrophysical Analysis and Sequence Stratigraphy”. Pacific Journal of Science and Technology. 14(1):571-585.

[3] Aigbedion, J.A. and Iyayi, S.E. (2007): Formation Evaluation of Oshioka Field, using geophysical well logs’, Middle-east Journal

of Scientific Research, 2(3 – 4) p.107 – 110.

[4] Archie, G.E., 1950. Introduction to Petrophysics of reservoir rocks. Bulletin of AAPG, Tulsa, 34(5): 943-961.

[5] Bowman, M.B.J., McClure, N.M., and Wilkinson, D.W. (1993) ”Wytch Farm oilfield: deterministic reservoir description of the

Triassic Sherwood Sandstone“, Petroleum Geology of Northwest Europe: Proceedings of the 4th Conference (edited by J. R. Parker),

Petroleum Geology '86 Ltd, The Geological Society, London, pp. 1513-1517.

[6] Dewan, J. 1983. Essentials of Modern Open Hole Log Interpretation. Penwell Publishing: Tulsa, OK. 361.

[7] Imasuen, O.I. and Samuel, O. (2013) ‘FORMATION EVALUATION OF WELL X, Y AND Z IN G-FIELD ONSHORE, NIGER

DELTA, NIGERIA’, Emerging Academy Resources, 02(06), pp. 413-417.

[8] Newell, A.J., (2006) “Formation SW England fluvial sandstone aquifers (Otter Sandstone Calcrete as a source of heterogeneity

in Triassic)”, Geological Society, p119-127, v.263, Geological Society, London, Special Publications.

[9] Ola-Buraimo, A.O, J.E. Ogala, and O.F. Adebayo. (2010) “Well-Log Sequence Stratigraphy and Paleobathymetry of Well-X,

Offshore Western Niger Delta, Nigeria”. World Applied Sciences Journal. 10(3):330-336.

[10] Schlumberger, (1985) ‘Well evaluation conference, Lagos-Nigeria, 3: 4-7.

[11] Stat Oil Research Group (2003): Geological Reservoir Characterization. Research and Technology Memoir 4.

[12] Worthington, P., (2010) Net Pay—What Is It? What Does It Do? How Do We Quantify It? How Do We Use It?. SPE Res. Eng.

12(5): 812-822. SPE-123561-PA. http://dx.doi.org/10.2118/123561-PA

http://dx.doi.org/10.2118/123561-PA

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