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American Journal of Oil and Chemical Technologies

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

Geological and Petrophysical Evaluation for an Oil WellHawez, 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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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).

Depth (m) Description Interpretation Sandbodies Seals/sources

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Figure 1. Block diagram with possible depositional environment.


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 areChannelized

Laterally discontinuous baffling shales and drapping sheet floods deposits unlikely to provide long term traps but as barriers will hinder production/recovery higher permeability sands.

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

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


Old Depth (m) New Depth (m) Shift (m)1636.55 1636.55 0.01638.15 1637.8 -0.351639.45 1639.45 0.01688.25 1688.25 0.01689.4 1688.4 -1.01690.6 1690.6 0.0

4.2.2. Volume of Shale

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Figure 2. Core to Log


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−GRminGRmax−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−ρbρma−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−V sh∗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 RwRt

(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:

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Rw= RtF

(eq. 3b)

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

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Figure 3. Kh vs Core plug

Figure 4.

VShale vs


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

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Figure 5. SwE

vs Effective

TABLE 3.

Statistical


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Figure 6.

Shows

Figure 7.

Stratigraphy


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Figure 9.

Semivariogra

Figure 8. M-N plot shows


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

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

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