Integrated Petrophysical Analysis and Reservoir Characterization of Shaly Sands in the Srikail Gas Field, Bengal Basin, Bangladesh

1. Introduction

Wireline logging data can provide valuable information for oil and gas exploration, such as reservoir characteristics, hydrocarbon potential, and formation evaluation. However, interpreting wireline logging data can be challenging, because economic and efficient oil and gas production is highly dependent on understanding key properties of reservoir rock, such as porosity, permeability, and wettability [1].

The determination of reservoir quality largely depends on quantitative and qualitative evaluation of petrophysical properties. Petrophysical parameters, i.e., Effective Porosity (Φeff), Effective Water Saturation (Sw), Formation Water Resistivity (Rw), Hydrocarbon Saturation (Shc-eff), and Permeability, all of them may be evaluated using well log data, thus performing a correct reservoir characterization.

This study addresses the formation evaluation and reservoir characterization of shaly sands at the Srikail Gas field (Bengal Basin), based on a detailed analysis of to all available mud log data and wire line log data. The analysis used the Symandox Method [2,3], with empirical mathematical equations appropriate for shaly sands. Numerous previous studies on the petrophysical analysis and reservoir characterization of Miocene sand [4,5,6,7,8] reservoirs across various gas fields within the Bengal Basin have contributed to the interpretation and analysis presented in this study.

2. Geological Framework

The study area lies in the east-central part of the Bengal Basin (Figure 1) [9,10]. The study was done in the Srikail gas field located about 60 km east of Dhaka city and approximately 100 km away from the Surma sub-basin.

The Bengal Basin formed during the continental extension of the eastern part of Gondwana, during the Late Mesozoic and is still ongoing [11]. During the Cenozoic, the Indian plate rifted northwest and then northwards from the combined Antarctica–Australia part of Gondwana, resulting in a major collision between India and Asia in the Miocene. As a result, the Himalayas in the north and the Indo-Burman range in the east were gradually uplifted as the Tethys Sea closed [12]. Tectonically, the Srikail anticline is located on the western part of the folded belt of Bengal Foredeep within the Tripura Uplift of the Bengal Basin (Figure 2) [13].

The existing stratigraphic system for the Bengal Basin was based exclusively on lithostratigraphic association with the type sections described by Evans [14] in Assam, northeastern India, along the fold belt in the basin's eastern portion. Evans' stratigraphic age estimates for the Assam sequences are questionable since they are based on long-distance correlations between brackish marine macrofauna and vertebrate findings. While some parts of Evans' scheme may be usable in the regional lithostratigraphic or seismic correlation (e.g., the boundary between the Surma and Tipam Groups), other parts of his classification (e.g., the contact between the Bhuban and Bokabil Formations or the internal units of these formations) are difficult to apply to the lithostratigraphic succession throughout the basin. For the Central Basin, which includes the Surma Basin and the Srikail Gas Field, the proposed lithostratigraphy is depicted in Figure 3 [13].

The focus of this study is the Miocene to Early Pliocene Surma Group, which does not outcrop in the study area. The Surma Group sediments were deposited in a large mud-rich prograding delta system, in response to the western encroachment of the Indo-Burma range and rising Himalaya, the [15]. Most authors have traditionally divided the Surma Group into two units based on Evan's stratigraphic scheme [14], with the younger Bokabil Formation covering the older Bhubhan Formation [16,17,18].

The Bhuban Formation has been interpreted by Johnson and Alam [19] as prodelta to delta front deposits of a mud-rich delta system, while te Bokabil sediments correspond to subaerial to brackish sandy deposits [16]. Sultana and Alam [20] interpreted the sediments of this group as shallow marine to tide-dominated coastal deposits within a transgressive - regressive regime based on extensive logging of core samples from the Sylhet Trough. The top of the Surma group is dominated by a shaley unit known as the "Upper Marine Shale" (UMS) [16].

The thickness of the Surma Group ranges from 2700 m to over 3900 m. Based on seismic data, mud logs, and wireline log responses, multiple prospective sand zones (named A to K,

from top to bottom) have been identified and explored by the operating oil and gas companies in the Srikail Gas Field. A detailed description of these sand zones is provided below (Table 1 and Table 2).

3. Materials and Methods

3.1. Materials

This study involves the analysis of four wells (Figure 4), where distinct reservoir zones have been identified in each (Table 1 and Table 2).

Wireline log data have been analysed to calculate petrophysical parameters and characterize the reservoirs. Srikail wells have been covered by all suites of resistivity and porosity logs, along with GR and SP logs. The open hole composite logs which are used in this study were conducted in different stages are as follows (Table 3):

All the stages of logging were conducted by China Petroleum Logging Company Ltd. (CPL) and covered all sets of logs. Overall quality of logs was found to be satisfactory.

3.2. Methods

3.2.1. Petrophysical Workflow

Petrophysical analysis was conducted on all the identified reservoirs, based on mud log and wireline log data. The following methodological steps were undertaken:

• Geological and geophysical data were thoroughly reviewed.
• Wireline log data were compiled for both manual interpretation [23] and imported into petrophysical software (Techlog).
• Porosity data were corrected to ensure accuracy in subsequent calculations.
• An initial manual interpretation of the logs was performed.
• A second interpretation was carried out using Techlog software for comparative analysis.
• The results of the manual and software-based interpretations were compared and found to yield broadly consistent petrophysical parameters.
• Cross-plots of the petrophysical data were generated in Microsoft Excel to support reservoir characterization.

In Srikail wells 1, 2, 3, and 4, the calculation of True Vertical Depth (TVD) and True Vertical Depth Subsea (TVDSS) has been determined using measured depth (MD), borehole deviation, and azimuth data.

In this study, digital well-log data in Log ASCII Standard (LAS) format, free from the common limitations of analog records, have been utilized. The dataset includes Caliper, Self-Potential (SP), Gamma ray, Resistivity logs (shallow, medium, and deep), and Porosity logs (Neutron, Density, and Sonic), following the guidelines outlined in “Log Interpretation Principles” of Schlumberger [24,25,26,27].

Potential hydrocarbon zones were identified based on the interpretation of GammaRay logs, quick-look techniques using Resistivity logs, and the presence of significant negative separation between Neutron and Density porosity logs. Furthermore, well-to-well correlation was performed using the available wireline log data. The Gamma Ray log, often referred to as a facies indicator, served as the primary tool for lithological identification and correlation across the studied wells.

3.2.2. Petrophysical Parameters

Water Resitivity (Rw)

Two of the most common methods of determining Rw from logs are the Inverse-Archie method and the SP method. The inverse-Archie method of determining Rw works under the assumption that water saturation (Sw) is 100%. It is necessary, therefore, that the inverse-Archie method be employed in a zone that is obviously wet. Furthermore, it is desirable to calculate Rw from the Inverse-Archie method in a clean formation with relatively high porosity. The following equation (Inverse-Archie method) is used here to determine water resistivity.

$${\mathrm{R}}_{\mathrm{wa}} = {\displaystyle \frac{{\mathsf{\Phi }}^{\mathrm{m}} \times {\mathrm{R}}_{\mathrm{t}}}{\mathrm{a}}}$$

(1)

where

m = Cementation exponent = 2

a = Tortusity facto r= 1

Shale Volume (Vsh)

Nawab and Islam [28] estimated shale volume in Miocene Bhuban sandstone for selected gas field of Bangladesh using gamma and porosity logs. In this study gamma is used for shale volume. Because shale is usually more radioactive than sand or carbonate, Gamma Ray logs can be used to calculate volume of shale in porous reservoirs.

The volume of shale expressed as a decimal fraction or percentage is called V_shale

V_shale from Gamma ray:

$${\mathrm{V}}_{\mathrm{s}\mathrm{h}}={\displaystyle \frac{\mathrm{G}{\mathrm{R}}_{\log }-\mathrm{G}{\mathrm{R}}_{\min }}{\mathrm{G}{\mathrm{R}}_{\max }-\mathrm{G}{\mathrm{R}}_{\min }}}$$

(2)

Vsh = Volume of shale

GR _log= Gamma Ray Reading of Formation

Gr_min=Minimum Gamma Ray (Clean Sand or Carbonate)

Gr_max=Maximum Gamma ray (Shale)

Effective Porosity (ɸ_{DN- eff})

Porosity is the fraction of a rock that is occupied by pores. Effective Porosity rtefers to the fraction of the total volume in which fluid flow is effectively taking place and includes catenaries and dead-end pores (as these pores cannot be flushed, but they can cause

fluid movement by release of pressure like gas expansion) and excludes closed pores (or non-connected cavities). Porosity is one of the most important rock properties to be determined in petroleum geology, and to determine it, three porosity tools and/or a resistivity tool are used [29].

Effective Porosity ɸe = Interconnected Pore space / Bulk Volume

Porosity determination from density and neutron logs.

Porosity from density log (ɸd)

φ_d = Porosity from density log, fraction

ρ_{ma =} Density of formation matrix, g/cm³

ρ_{b =} Bulk density from log measurement, g/cm³

ρ_{f =} Density of fluid in rock pores, g/cm³

Effective Porosity from Neutron and Density Log:

$${\Phi }_{n-\mathrm{corrected}}={\Phi }_n-\left(V_{cl}\times {\Phi }_{sh}\right)\hspace{1em}{\Phi }_{d-\mathrm{corrected}}={\Phi }_d-\left(V_{cl}\times {\Phi }_{sh}\right)$$

(4)

These values of neutron and density porosity corrected for the presence of clays are then used in the equations below to determine the effective porosity (Φeffective​) of the formation of interest.

$${\Phi }_{effective}={\left[{\displaystyle \frac{{\left({\Phi }_{n-\mathrm{corrected}}\right)}^2+{\left({\Phi }_{d-\mathrm{corrected}}\right)}^2}{2}}\right]}^{0.5}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{a}\mathrm{s}$$

(5)

Effective Water Saturation (Swe)

Water saturation is the ratio of water volume to pore volume. It can be expressed as

Sw = ( ɸw/ɸ) *100

(6)

where

Sw = water saturation of the uninvaded zone

ɸw =conductivity derived or water fill porosity

ɸ = True porosity from the porosity log

Effective water saturation (SWe): is the ratio of free water volume to effective porosity (PHIe). Since the Archie equation [30] is only applicable to clean sands, it has not been utilized in this study to determine the water saturation in the hydrocarbon-bearing zones. A significant advancement in the 1950s was the realization that shale introduces "excess conductivity," which leads to deviations from the assumptions in Archie's original equations [31]. Therefore, the saturation of water and hydrocarbon has been calculated here using well used formulae for shaly sands provided by Simandoux [2]:

$$S_{we}={\displaystyle \frac{C\times R_w}{{\Phi }_{eff}^2}}\left[{\displaystyle \frac{5\times {\Phi }_{eff}^2}{R_w\times R_t}}+{\left({\displaystyle \frac{V_{sh}}{R_{sh}}}\right)}^2-{\displaystyle \frac{V_{sh}}{R_{sh}}}\right]$$

(7)

where

Swe= Effective (clay-corrected) water saturatio

Φeff=Effective porosity (corrected for clay/shale content)

Rsh= Resistivity of adjacent shale

Rw=Formation water resistivity (resistivity of the water in the reservoir)

Rt=True resistivity of the uninvaded zone (actual formation resistivity)

Vsh=Volume of shale (fraction or percentage of shale in the rock)

C=Empirical constant: typically, 0.40 for sandstones, 0.45 for carbonates

Effective Hydrocarbon Saturations (Shc-eff)

Hydrocarbon saturation can be determined by the difference between unity and water saturation and effective hydrocarbon saturation can be determined by the difference between unity and effective water saturation.

Shc-eff =1- Sw_e

(8)

Bulk Water Volume (BWV) and Permeability (K)

Bulk volume water (BWV) is the percentage of the total rock volume that is occupied by water. It compares to the more commonly used water saturation term in that water saturation is the percent of the total pore space occupied by water. It is a critical input to estimating fluid mobility in the reservoir.

BWV= Sw_e x ɸ

(9)

Irreducible water saturation (SWirr) is the ratio of immobile or irreducible water volume to effective porosity.

Permeability is a measurement of fluid mobility, usually expressed in one thousandths of a Darcy or millidarcy. It is a key component of net pay, as well with low or no permeability will not produce economic quantities of hydrocarbons.

Timur method is used here to determine the permeability.

K= (93* ɸ^2.2/ Swirr) ²

(10)

4. Data Analysis

In this study, data were analyzed in two different ways, for comparison. The first procedure has used Techlog software, whereas the second has been done manually, collected data at each depth and then calculating the petrophysical parameters using the above-mentioned equations. These calculated Parameters have also been cross/plotted, from a simple Excel spreadsheet, to analyze their eventual positive or negative correlation in different wells and reservoir zones.

4.1. Computed Analysis (Techlog Software)

Petrophysical analysis (Figure 5, Figure 6, Figure 7 and Figure 8) was initially conducted using the Techlog software The Gamm Ray (GR) log was first corrected for borehole size and subsequently normalized. Since GR readings tend to be elevated in wells with KCl-based mud systems, normalization was performed using data from Srikail-2, the only well drilled without KCl mud. Density log corrections were applied using the Gardner equation. Lithological components and Porosity were quantified using the Quanti.Elan module, which performs mineralogical and fluid inversion. Shale volume was estimated from both Neutron and Density logs.

To confirm the presence of gas, shallow (MSFL), medium (LLM), and deep (LLD) resistivity logs were analyzed. Hydrocarbon-bearing zones and gas-water contacts were identified using the quick-look interpretation technique, with particular emphasis on large negative separation between porosity logs, as described by Rider (2000). Gas-bearing zones were identified by observing Neutron-Density crossover behavior.

Total Porosity (PHIT_ND) and Effective Porosity (PHIE_ND) were computed based on Neutron-Density data. Effective water saturation (SWE_SIM) and Effective Bulk Volume of Water (EBV_SIM) were calculated by Techlog software, using the Symandoux equation and method, respectively, and plotted as part of the interpretation results. The parameters applied in this analysis were selected based on previous studies, well reports, and core analysis data.

4.2. Raw Data Analysis

Lithology, Shale Volume, Porosity, Water Saturation, Hydrocarbon Saturation, Permeability, and Hydrocarbon Movability were determined using relevant well log data available in both digital and hard copy formats. These parameters were calculated manually using empirical equations in Microsoft Excel to assist in the characterization of the reservoir rocks.

4.2.1. Petrophysical Characteristics

In this paper all the producing and non-producing sands are characterized by different petrophysical parameters. All the parameters are shown in the following tables.

Table 4. Minimum and Maximum Gamma Ray values (GR_min, GR_max) and Average Shale Volume (Av. V_sh).

Sand	Well_1		Well_2		Well_3		Well_4
	Grmin	Grmax	Grmin	Grmax	Grmin	Grmax	Grmin	Grmax
	Av. Vsh (%)		Av. Vsh (%)		Av. Vsh (%)		Av. Vsh (%)
A	73	134	77	142	80	135	96	126
	36%		42%		48%		37%
B	94	159	67	146	80	144	96	150
	43%		35%		31%		45%
C	97	133	68	146	70	134	65	149
	56%		60%		46%		83%
Dupper	-		80	150	80	150	99	186
			48%		49%		34%
Dlower	-		73	148	77	154	90	161
			51%		44%		60%)
E	-		74	155	80	137	90	158
			35%		43%		45%
F	-		-		-		100	171
							39%
G	90	159	-		-		100	171
	52%						50%)
H	74	113	-		-
	41%
I	80	124	-		-
	57%
J	90	134	-		-
	42%
K	85	134	-		-
	37%

Table 4. Minimum and Maximum Gamma Ray values (GR_min, GR_max) and Average Shale Volume (Av. V_sh).

Sand	Well_1		Well_2		Well_3		Well_4
	Grmin	Grmax	Grmin	Grmax	Grmin	Grmax	Grmin	Grmax
	Av. Vsh (%)		Av. Vsh (%)		Av. Vsh (%)		Av. Vsh (%)
A	73	134	77	142	80	135	96	126
	36%		42%		48%		37%
B	94	159	67	146	80	144	96	150
	43%		35%		31%		45%
C	97	133	68	146	70	134	65	149
	56%		60%		46%		83%
Dupper	-		80	150	80	150	99	186
			48%		49%		34%
Dlower	-		73	148	77	154	90	161
			51%		44%		60%)
E	-		74	155	80	137	90	158
			35%		43%		45%
F	-		-		-		100	171
							39%
G	90	159	-		-		100	171
	52%						50%)
H	74	113	-		-
	41%
I	80	124	-		-
	57%
J	90	134	-		-
	42%
K	85	134	-		-
	37%

Table 5. Average Effective Porosity(ɸ_DN-eff) from Effitive Density ( ɸ_{D_eff} ) and Effective Neutron Porosity (ɸ_{N_eff}).

Sand	Well_1		Well_2		Well_3		Well_4
	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff
	ɸDN-eff (%)		ɸDN-eff (%)		ɸDN-eff (%)		ɸDN-eff (%)
A	15	14	19	20	11	17	17	21
	15%		21%		15%		20%
B	13	17	15	21	12	24	14	19
	13%		18%		19%		17%
C	07	08	07	09	9	10	02	05
	08%		8%		7%		04%
Dupper			12	11	12	14	12	20
			12%		14%		15%
Dlower			10	14	11	15	12	11
			12%		14%		12%
E			11	18	12	14	13	17
			15%		15%		16%
F							08	19
							14%
G	09	10					04	07
	10%						7%
H	11	09
	11%
I	11	05
	10%
J	14	09
	11%
K	13	11
	11%

Table 5. Average Effective Porosity(ɸ_DN-eff) from Effitive Density ( ɸ_{D_eff} ) and Effective Neutron Porosity (ɸ_{N_eff}).

Sand	Well_1		Well_2		Well_3		Well_4
	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff	ɸD_eff	ɸN_eff
	ɸDN-eff (%)		ɸDN-eff (%)		ɸDN-eff (%)		ɸDN-eff (%)
A	15	14	19	20	11	17	17	21
	15%		21%		15%		20%
B	13	17	15	21	12	24	14	19
	13%		18%		19%		17%
C	07	08	07	09	9	10	02	05
	08%		8%		7%		04%
Dupper			12	11	12	14	12	20
			12%		14%		15%
Dlower			10	14	11	15	12	11
			12%		14%		12%
E			11	18	12	14	13	17
			15%		15%		16%
F							08	19
							14%
G	09	10					04	07
	10%						7%
H	11	09
	11%
I	11	05
	10%
J	14	09
	11%
K	13	11
	11%

Table 6. Average Effective Water (S_w-eff ) and Hydrocarbon Saturation (S_hc-eff).

Sand	Well_1		Well_2		Well_3		Well_4
	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)
A	35	65	4	96	40	60	33	67
B	40	60	25	75	31	69	23	77
C	42	58	45	55	40	60	45	55
Dupper	-		26	74	20	80	14	86
Dlower	-		22	78	17	83	26	74
E	-		40	60	22	78	34	66
F	-		-		-		40	60
G	46	54	-		-		46	54
H	17	83	-		-		-
I	16	84	-		-		-
J	19	81	-		-		-
K	22	78	-		-		-

Table 6. Average Effective Water (S_w-eff ) and Hydrocarbon Saturation (S_hc-eff).

Sand	Well_1		Well_2		Well_3		Well_4
	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)	Sw-eff (%)	Shc-eff (%)
A	35	65	4	96	40	60	33	67
B	40	60	25	75	31	69	23	77
C	42	58	45	55	40	60	45	55
Dupper	-		26	74	20	80	14	86
Dlower	-		22	78	17	83	26	74
E	-		40	60	22	78	34	66
F	-		-		-		40	60
G	46	54	-		-		46	54
H	17	83	-		-		-
I	16	84	-		-		-
J	19	81	-		-		-
K	22	78	-		-		-

Table 7. Average Permeability (Timur method).

Sand	Well_1	Well_2	Well_3	Well_4
	K (mD)	K (mD)	K (mD)	K (mD)
A	83	206	117	120
B	46	158	161	227
C	.08	0.11	0.13	.05
Dupper	-	111	102	129
Dlower	-	355	117	291
E	-	40	66	58
F	14	-	-	18
G	1.01	-	-	2.6
H	61	-	-	-
I	67	-	-	-
J	64	-	-	-
K	22	-	-	-

Table 7. Average Permeability (Timur method).

Sand	Well_1	Well_2	Well_3	Well_4
	K (mD)	K (mD)	K (mD)	K (mD)
A	83	206	117	120
B	46	158	161	227
C	.08	0.11	0.13	.05
Dupper	-	111	102	129
Dlower	-	355	117	291
E	-	40	66	58
F	14	-	-	18
G	1.01	-	-	2.6
H	61	-	-	-
I	67	-	-	-
J	64	-	-	-
K	22	-	-	-

Table 8. Bulk volume of water (BVW, Range) for wells 1, 2, 3 and 4.

Sands	(BVW)
A	0.040-0.060
B	0.030-0.050
C	0.037-0.040
Dupper	0.020-0.027
Dlower	0.020-0.024
E	0.035-0.080

Table 8. Bulk volume of water (BVW, Range) for wells 1, 2, 3 and 4.

Sands	(BVW)
A	0.040-0.060
B	0.030-0.050
C	0.037-0.040
Dupper	0.020-0.027
Dlower	0.020-0.024
E	0.035-0.080

4.2.2. Cross Plots of Petrophysical Parameters

Petrophysical analysis plays an important role in reservoir characterization. To explain the reservoirs, the petrophysical data was cross plotted using Excel sheets. Plots of volume of shale vs depth have been compiled. To further comprehend the reservoirs, porosity vs. permeability was plotted, as well as volume of shale vs. permeability, saturation, and porosity.

Vshale values vary with depth along each individual sand show, with scattered data points suggesting heterogeneous lithologies, characterized by alternating or interbedded sand-shale layers (Figure 9). This heterogeneity results from varying depositional conditions, possibly from fluctuating energy levels in a transitional or marginal marine environment (e.g., deltaic or estuarine system). Looking at individual sands, most of them tend to become more shaly (higher Vshale values) towards the top, probably resulting from a rapid input of clean sandy lobes, followed by an increasing mix with settling clays. Such heterogeneities have direct implications for reservoir quality, as higher Vshale values are typically associated with reduced porosity and permeability, thereby lowering hydrocarbon storage and flow potential.

In Sand A, porosity increases with decreasing Vsh (Figure 10). Both porosity and permeability demonstrate a strong inverse relationship with Vsh, indicating that decreasing shale content increases reservoir quality. A positive linear correlation exists between porosity and permeability.

Sand B shows lower porosity in shale-rich zones and higher porosity where Vsh is reduced (Figure 11). Both porosity and permeability are negatively affected by increasing Vsh, and a strong linear relationship persists between porosity and permeability.

In Sand C Porosity and permeability are lower with higher Vsh (Figure 12). Permeability improves with lower shale content and elevated porosity. Hydrocarbon saturation changes gradually throughout the interval. Strong inverse correlations are noted between Vsh and both porosity and permeability. The porosity-permeability relationship is again linear and well-defined.

Sand D_upper shows higher porosity and permeability in zones with lower Vsh (Figure 13). Permeability increases with decreasing Vsh, and hydrocarbon saturation rises progressively. The data confirm the significant role of Vsh in reducing reservoir quality. A consistent linear relationship exists between porosity and permeability.

In Sand D_lower, porosity and permeability improve where Vsh is lower and effective porosity is higher (Figure 14). A clear negative correlation is evident between Vsh and reservoir quality indicators. Vsh remains a critical controlling factor, showing a strong inverse correlation with both porosity and permeability. The linear relationship between porosity and permeability is maintained.

In Sand E, porosity is higher in cleaner sands and reduced in zones with increased shale content (Figure 15. Permeability shows a rising trend with decreasing Vsh. Hydrocarbon saturation improves gradually with decreasing Vsh. Shale volume continues to negatively influence reservoir properties. A clear positive linear trend is observed between porosity and permeability.

In Sand F Porosity increases in sands with lower Vsh, (Figure 16). Hydrocarbon saturation shows a slight increase with improved porosity. Shale content remains a limiting factor for reservoir quality, and porosity-permeability correlation is positive and linear.

In Sand G, porosity increases with shale volume (Vsh) decrease, and permeability also improves (Figure 16), similar to Sand F. The negative influence of Vsh is evident, and a strong linear relationship exists between porosity and permeability.

From the above analysis, it becomes clear that D Sands are effectively the best reservoirs [32]. Sand D_upper shows good porosity, permeability, and Hydrocarbon saturation, all of them reducing in shale-rich zones, probably due also to poorer sorting. Sand D_lower presents good porosity and permeability in cleaner intervals in both wells, The consistency in these patterns reinforces the controlling influence of Vsh and depositional sorting on reservoir quality.

5. Results and Discussion

In previous works, Alam et al. [32] characterized the producing quality of three deep sandy zones (D-Upper, D-Lower and E-Sand) in three Srikail wells, based in Vsh, Porosity, Permeability and net-to-gross. Hossain et al. [33] also characterized three sandy units (named Zones 1, 2 and 3) in a single well (Srikail #3), indicating only average values for Vsh, Porosity, Water and Hydrocarbon Saturation, for each Zone. In this work, in-depth petrophysical evaluation and reservoir characterization of all the sandy intervals of the Srikail Gas Field was conducted through a combination of manual analysis and automated interpretation using Techlog software. Petrophysical parameters were derived through both empirical equations and software-based computations, and the results were compared to assess consistency and interpretation accuracy.

From the 11 previously identified reservoir zones (named A to K, from top to bottom), Srikail-1 well has intersected a total of nine reservoir layers (A to C and F to K) whereas Srikail-4 has intersected the seven reservoir layers (A to G), and both Srikail-2 and Srikail-3 wells only five (A to E).

The proportion of clay in reservoirs is a crucial parameter influencing its producing quality. The calculated average shale volume (Vshale) varies across different reservoir layers and among these, Zone C exhibits the highest shale content, whereas Zone B shows the lowest. Within each sandy interval, Vshale variability at specific depths indicates heterogeneous, interbedded sand-shale lithology from fluctuating depositional conditions. An overall increase in Vshale towards the top indicates a transition to finer-grained, lower-energy environments. This heterogeneity impacts reservoir quality by reducing porosity and permeability, affecting hydrocarbon flow.

Porosity estimation is a fundamental step in reservoir evaluation [34], and along with permeability, it dictates the fluid storage capacity and productivity of the reservoir (Dickey, 1986). Effective porosity was derived from a combination of effective density and neutron porosities. The overall effective porosity ranges from 4% to 20%. Sands A, B, D, and E exhibit favorable porosity values and are considered good quality reservoirs.

Hydrocarbon saturation (Shc) values further confirm reservoir potential. Based on the criteria of Asquith and Gibson [35], zones with Shc above 60% are considered hydrocarbon-bearing, making Zones A, B, D, and E the most promising. Zones C, F, G, H, I, J and K in contrast, demonstrate higher shale content, lower porosity, and limited hydrocarbon potential.

Mud log analysis revealed that C and G sands are highly calcareous with poor porosity. In Well-01, the G, H, I, J and K intervals also exhibited significant calcareous content [36] and sub-optimal porosity. Calcareous tests during mud logging and a lack of gas flow during DSTs (except in Sand I) suggest these sands have low permeability.

Permeability was estimated using the Timur equation for wells 1 through 4. D sands demonstrate the highest permeability and are already in production. Sands A, B, and E also show good permeability and are viable for hydrocarbon extraction. Zone E, in particular, is confirmed as a productive interval in Well-04. C and G sands have very poor permeability. Sands F, H, I, J and K show poor to moderate porosity and permeability, maybe producible with minimal stimulation (e.g. fracturing). Permeability values obtained from DST data in the deeper tested sands range from 0.6 to 0.01 mD [37], indicating a very tight reservoir with limited fluid flow capacity without stimulation.

Petrophysical parameters Cross Plots clearly point to an inverse relationship between Vshale and Porosity. Higher shale content corresponds with reduced porosity, as shale occupies pore spaces and obstructs pore throats. In this study, Zones C, G, F, H, I, J, and K are identified as low-quality reservoirs due to permeability, although some of them have good hydrocarbon saturation.

The relationship between Vshale, porosity, and permeability was further assessed. Both porosity and permeability decrease as Vshale increases. Cross-plotting porosity versus permeability reveals a strong linear correlation: well-sorted, clean sandstones with low shale content show high porosity and permeability. Conversely, zones with high clay content may display elevated porosity but reduced permeability due to matrix blockage. Considering that in most sands Vsh increases towards the upper layers, it may be concluded that permeability is lower on these more shaly layers. Moreover, the good correlation of Porosity decrease with Vhs increase allows us to consider that also Hydrocarbon saturation tends to decrease towards the upper layers. Overall, each sand tends to have better reservoir properties at its lower and intermediate layers, than at its upper more shaly layers.

Bulk Volume Water (BVW) values offer further insight into grain size and reservoir texture. According to Fertl and Vercellino [38], BVW values between 0.035 and 0.07 are indicative of fine to very fine-grained sands. The results (Table. 8) suggest that Sands A, B, and C are fine to very fine-grained, D sands are coarse-grained, and Sand E comprises fine-grained and silty material.

6. Conclusions

This research presents an integrated petrophysical evaluation and reservoir characterization of the Srikail Gas Field, utilizing both manual techniques and advanced digital interpretation through Techlog software. A combination of digital and hardcopy well log data was used to estimate essential petrophysical properties such as shale volume, porosity, fluid saturation, permeability, and bulk volume of water (BVW). The study employed empirical cross-plots, analytical models, and automated processing to improve the precision and consistency of interpretations. A quick-look methodology aided in identifying key reservoir intervals and gas-bearing zones.

The analysis reveals that reservoir quality within the field varies from poor to good. Net reservoir thicknesses range from 8 to 66 meters, with shale content between 31% and 83%. Effective porosity spans from 4% to 20%, hydrocarbon saturation ranges from 54% to 96%, and effective water saturation falls between 4% and 46%. Permeability estimates vary widely from 0.05 to 355 mD, while BVW values are between 0.02 and 0.08.

Both qualitative and quantitative interpretations were carried out to assess the characteristics of individual reservoir layers. The identified reservoirs predominantly consist of shaly sandstones. Reservoirs A, B, and E show moderate to good reservoir properties, characterized by fine to very fine-grained, silty sandstones with fair to good permeability. Conversely, Reservoirs C and G exhibit poor quality, highly calcareous composition (from mud logging) and extremely low permeability, consistent with tight sandstone classification. Deeper sands such as F, H, I, J, and K display tight to semi-conventional characteristics and are likely to benefit from targeted stimulation and advanced completion strategies.

The most productive zones - Upper D and Lower D - are identified by their cleaner and coarser grain size and superior permeability, contributing significantly to gas production. Meanwhile, DST-derived permeability values from deeper sands (e.g., G, H, J) confirm extremely tight conditions, ranging from 0.6 to 0.01 mD.

Regardless of the different sands and burial depths, there is a clear pattern of upwards decrease in hydrocarbon production properties towards the top layers of each producing sand. This fact indicates the importance of initial sandy inputs, gradually “contaminated” by clay inputs into the depositional environment. In other words, each sand seems to represent a 4^th order finning upwards cycle [39].

Integrating the previous analysis, Reservoir Zones A, B, D, and E clearly appear as high-quality reservoirs due their favorable porosity, permeability, hydrocarbon saturation, and low shale content. Among these, D and E sands are already productive, supporting their classification as effective hydrocarbon-bearing reservoirs. In contrast, Zones C and G exhibit poor reservoir characteristics and may be classified as tight sands due to their calcareous nature, poor porosity and limited permeability. On the other hand, F, H, I, J, and K are tight to semi-conventional and might require partial stimulation and proper completion.

To improve future reservoir understanding and development, it is recommended that further studies incorporate core analysis and 3D seismic data in conjunction with petrophysical log interpretation. This integrated approach will provide a more comprehensive insight into reservoir heterogeneity and enhance resource recovery strategies across the Srikail Gas Field.