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

Abstract

This study offers a comprehensive petrophysical evaluation and reservoir characterization of the Srikail Gas Field, situated on the Tripura Uplift in the east-central Bengal Basin. Utilizing well log data from four wells (Srikail-1 to Srikail-4), the analysis targets the Bhuban and Bokabil formations of the Surma Group. Standard log suites, including gamma ray, spontaneous potential, caliper, resistivity, neutron, density, and sonic logs, were interpreted using both manual techniques and digital analysis through software. Key petrophysical properties, including shale volume, effective porosity, fluid saturations, permeability, and bulk volume of water, were estimated using a combination of empirical modeling and automated interpretation workflows. Cross-plot methodologies were applied to assist in reservoir evaluation. The study integrated both qualitative and quantitative approaches to characterize each reservoir unit in detail. Results demonstrate significant heterogeneities in reservoir quality across the field. While some intervals exhibit favorable properties suitable for commercial gas production, others are characterized by high carbonate content, poor porosity, and very low permeability (Sand C with 0.05 to 0.08 mD), indicative of tight to semi-conventional reservoirs. The most productive zones, identified as the D sands, are cleaner sands with excellent permeability (102 mD to 355 mD). In contrast, deeper intervals generally exhibit tighter characteristics, with DST-derived permeability values ranging from 0.6 to 0.01 mD. The study recommends integrating core analysis, advanced petrophysical modeling, and 3D seismic interpretation with well log data to enhance reservoir delineation in the Srikail Gas Field. This combined approach would reduce uncertainties, improve input parameter accuracy, and offer a more comprehensive understanding of the Bhuban Formation’s heterogeneity, ultimately supporting more effective reservoir evaluation and hydrocarbon recovery planning.

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].

Previous studies in the Srikail Gas Field have focused on limited sandy zones or single wells, often providing only average petrophysical values. In contrast, this study presents a comprehensive evaluation of all identified sandy intervals across four wells in the field. A combination of manual interpretation and automated analysis using Techlog (2024.2) software was employed to derive key petrophysical parameters. These were calculated using both empirical equations and software-driven methods to ensure consistency and improve interpretation accuracy. This integrated approach allows for a more detailed understanding of formation heterogeneity and overall reservoir quality.

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, may all 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 conducted 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 [15]. Most authors have traditionally divided the Surma Group into two units based on Evan’s [14] stratigraphic scheme, 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 the 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 shaly 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).

The absence of sand D and E in Well 1 is due to presence of a thick (over 200 m) canyon fill, which eroded those sands. This erosion is identified in seismic sections and also on the log [21]. Probably this absence is a result of erosion caused by a canyon incision and muddy infill, detected in seismic lines.

Table 1. Total depth of reservoir zones [22,23].

Well Name	Total Depth		Identified Reservoir Zones
	MD	TVDSS
Srikail Well_1	3583 m	3572 m	A, B, C, F, G, H, I, J, K
Srikail Well_2	3214 m	3198 m	A, B, C, Dup, Dlower, E
Srikail Well_3	3350 m	3178 m	A, B, C, Dup, Dlower, E
Srikail Well_4	3512 m	3360 m	A, B, C, Dup, Dlower, E, F, G

Table 1. Total depth of reservoir zones [22,23].

Well Name	Total Depth		Identified Reservoir Zones
	MD	TVDSS
Srikail Well_1	3583 m	3572 m	A, B, C, F, G, H, I, J, K
Srikail Well_2	3214 m	3198 m	A, B, C, Dup, Dlower, E
Srikail Well_3	3350 m	3178 m	A, B, C, Dup, Dlower, E
Srikail Well_4	3512 m	3360 m	A, B, C, Dup, Dlower, E, F, G

Table 2. Thickness of reservoir zones.

Zone		Well_1	Well_2	Well_3	Well_4
A		11 m	11 m	10 m	08 m
B		31 m	38 m	35 m	33 m
C		24 m	14 m	14 m	10 m
D	Dupper	-	60 m	52 m	66 m
	Dlower	-	26 m	20 m	27 m
E		-	17 m	26 m	37 m
F		15 m	-	-	24 m
G		39 m	-	-	40 m
H		18 m	-	-	-
I		38 m	-	-	-
J		08 m	-	-	-
K		15 m	-	-	-

Table 2. Thickness of reservoir zones.

Zone		Well_1	Well_2	Well_3	Well_4
A		11 m	11 m	10 m	08 m
B		31 m	38 m	35 m	33 m
C		24 m	14 m	14 m	10 m
D	Dupper	-	60 m	52 m	66 m
	Dlower	-	26 m	20 m	27 m
E		-	17 m	26 m	37 m
F		15 m	-	-	24 m
G		39 m	-	-	40 m
H		18 m	-	-	-
I		38 m	-	-	-
J		08 m	-	-	-
K		15 m	-	-	-

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 analyzed 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 that are used in this study were conducted in different stages and are as follows (

Table 3. Open hole composite logs.

Hole Section	Scale	Description
12.25″	1:200/1:500/1:1000 MD	Composite Log
12.25″	1:200/1:500/1:1000 MD	GR/SP/Resistivity
12.25″	1:200/1:500/1:1000 MD	Density/Porosity/Cali/GR
12.25″	1:200 MD	Composite Plot
8.5″	1:200/1:500/1:1000 MD	Composite Log
8.5″	1:200/1:500/1:1000 MD	GR/SP/Resistivity
8.5″	1:200/1:500/1:1000 MD	Density/Porosity/Cali/GR
8.5″	1:200 MD	Full Wave Sonic/GR

):

All the stages of logging were conducted by China Petroleum Logging Company Ltd. (CPL) (Beijing, China) and covered all sets of logs. The overall quality of logs was found to be satisfactory. Before the petrophysical analysis was started, key quality checks and corrections were applied to ensure data reliability. This included borehole corrections (e.g., for mudcake and hole size), merging the best log passes, removing noisy spikes, and checking repeat log consistency. Intervals with poor data were either corrected or excluded. Environmental corrections for resistivity and porosity logs were also applied during vendor processing to account for mud properties.

Across the four study wells, a total of eight drill stem tests (DSTs) and one production test were conducted. Unfortunately, quantitative permeability results were retrievable for only one well (Well 1). A summary table (

Table 4. Summary of drill stem test (DST).

Well	Zone	Test Type	Perforation Interval (m)	Result
Well-1	G	Conventional Testing	3334–3341 m and 3343–3350 m	Maximum gas flow 1.2 MMSCFD. Permeability 0.0l mD.
	H	DST-2	3410–3413 m, 3428–3430 m and 3439–3442 m	Highly calcareous. No visual porosity. Gas show 1.49% at 3408 m. Tight. Non-measurable flow. Permeability 0.0149 mD.
	J	DST-1	3530–3534 m	Highly calcareous. No visual porosity. Water and minor gas flow.
Well-2	D upper	DST-2	2963.55–2969 m	Commercial gas flow.
	D lower	DST-1	3026.8–3029.8 m and 3030–3036.5 m	Commercial gas flow.
Well-3	D upper	DST-2	3097–3104 m	Commercial gas flow.
	D lower	DST-1	3185–3190 m	Commercial gas flow.
Well-4	E	DST-1	3220–3231 m	Commercial gas flow.

) lists, for each DST, well name, tested depth interval, test type, and data availability (quantitative vs. qualitative) and permeability (when reported).

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. Wireline log data were compiled for both manual interpretation [24] and imported into petrophysical software (Techlog). The following flow chart (Figure 5) shows the steps that were undertaken:

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 [25,26,27,28].

Potential hydrocarbon-bearing zones were identified through the integration of gamma ray log interpretation, quick-look analysis of resistivity logs, and the observation of significant negative separation between neutron and density porosity logs. The presence of gas within the reservoirs was corroborated by drill stem test (DST) data and supported by regional geological context. Gas-bearing intervals indicated by neutron–density crossover were further verified using additional wireline log responses to improve interpretation reliability.

Moreover, 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.

The analysis applied the Symandoux model, utilizing empirical equations specifically suited for evaluating shaly sandstone formations [2,3]. Although advanced approaches such as FEM-based resistivity simulation, proposed by Wu et al. [29], provide greater accuracy in laminated shaly systems, the use of the Simandoux model was adopted here due to the absence of high-resolution formation imaging or core-scale shale distribution data.

3.2.2. Petrophysical Parameters

Water Resistivity (R_w)

Two of the most common methods of determining R_w from logs are the inverse-Archie method and the SP method. The inverse-Archie method of determining R_w works under the assumption that water saturation (S_w) 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 R_w 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.

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

(1)

where

m = cementation exponent = 2;

a = tortuosity factor = 1.

Shale Volume (V_sh)

Nawab and Islam [30] estimated shale volume in Miocene Bhuban sandstone for selected gas fields 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)

V_sh = 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).

The GR_min and GR_max values used for V_shale calculation were determined directly from well log data, by visually inspecting the gamma ray (GR) curve in each well on a meter-by-meter basis. For each zone, GRmin was selected from the cleanest (lowest-GR) sand intervals, and GRmax from the highest-GR shale intervals, ensuring that the selected points reflected representative lithologies within the respective formation.

Effective Porosity (ϕ_DN-eff)

Porosity is the fraction of a rock that is occupied by pores. Effective porosity refers 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 [31].

Effective porosity ϕe = Interconnected pore space/bulk volume

Porosity determination from density and neutron logs.

Porosity from density log (ϕd)

$$\mathrm{\varphi }\mathrm{d}={\displaystyle \frac{\left({\mathrm{\rho }}_{\mathrm{m}\mathrm{a}}-{\mathrm{\rho }}_{\mathrm{b}}\right)}{\left({\mathrm{\rho }}_{\mathrm{m}\mathrm{a}}-{\mathrm{\rho }}_{\mathrm{f}}\right)}}$$

(3)

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:

$${\mathrm{\varphi }}_{\mathrm{n}\text{-}\mathrm{corrected}}={\mathrm{\varphi }}_{\mathrm{n}}-\left({\mathrm{V}}_{\mathrm{c}\mathrm{l}}\times {\mathrm{\varphi }}_{\mathrm{s}\mathrm{h}}\right)\hspace{1em}{\mathrm{\varphi }}_{\mathrm{d}\text{-}\mathrm{corrected}}={\mathrm{\varphi }}_{\mathrm{d}}-\left({\mathrm{V}}_{\mathrm{c}\mathrm{l}}\times {\mathrm{\varphi }}_{\mathrm{s}\mathrm{h}}\right)$$

(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 ($\mathrm{\varphi }$effective) of the formation of interest.

$${\mathrm{\varphi }}_{\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}}={\left[{\displaystyle \frac{{\left({\mathrm{\varphi }}_{\mathrm{n}\text{-}\mathrm{corrected}}\right)}^2+{\left({\mathrm{\varphi }}_{\mathrm{d}\text{-}\mathrm{corrected}}\right)}^2}{2}}\right]}^{0.5}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{a}\mathrm{s}$$

(5)

Effective Water Saturation (Swe)

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

S_w = (ϕw/ϕ) × 100

(6)

where

S_w = water saturation of the uninvaded zone;

ϕ_w = conductivity derived or water fill porosity;

ϕ = true porosity from the porosity log.

Effective water saturation (S_We): is the ratio of free water volume to effective porosity (PHIe). Since the Archie equation [32] 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 [33]. Therefore, the saturation of water and hydrocarbon has been calculated here using well used formulae for shaly sands provided by Simandoux [2]:

$${\mathrm{S}}_{\mathrm{w}\mathrm{e}}={\displaystyle \frac{\mathrm{C}\times {\mathrm{R}}_{\mathrm{w}}}{{\mathrm{\varphi }}_{\mathrm{e}\mathrm{f}\mathrm{f}}^2}}\left[{\displaystyle \frac{5\times {\mathrm{\varphi }}_{\mathrm{e}\mathrm{f}\mathrm{f}}^2}{{\mathrm{R}}_{\mathrm{w}}\times {\mathrm{R}}_{\mathrm{t}}}}+{\left({\displaystyle \frac{{\mathrm{V}}_{\mathrm{s}\mathrm{h}}}{{\mathrm{R}}_{\mathrm{s}\mathrm{h}}}}\right)}^2-{\displaystyle \frac{{\mathrm{V}}_{\mathrm{s}\mathrm{h}}}{{\mathrm{R}}_{\mathrm{s}\mathrm{h}}}}\right]$$

(7)

where

Swe = effective (clay-corrected) water saturation

ϕ_eff = effective porosity (corrected for clay/shale content)

R_sh = resistivity of adjacent shale

R_w = formation water resistivity (resistivity of the water in the reservoir)

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

V_sh = volume of shale (fraction or percentage of shale in the rock)

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

In this study, a value of C = 0.40 was applied in the Simandoux equation, consistent with standard practice for shaly sandstone formations. This selection is supported by regional analogs, including Rahman & Sarker [34,35,36], who reported similar values for semi-consolidated deltaic sandstones in the Bengal Basin. However, recent work by Wan Zairani Wan Bakar et al. [37] highlights the benefits of adopting this approach to better account for shale content and heterogeneity in similar lithologies. While localized carbonate presence was noted in certain intervals, it was not laterally extensive or volumetrically dominant. Therefore, the adopted C value was considered appropriate for representing the overall lithological character of the Bhuban Formation.

Effective Hydrocarbon Saturations (S_hc-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.

S_hc-eff = 1 − S_we

(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 = S_we × ϕ

(9)

Irreducible water saturation (S_wirr) 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; low or no permeability will not produce economic quantities of hydrocarbons.

Timur method [38] is used here to determine the permeability.

K = (93 × ϕ2.2/S_wirr)²

(10)

Timur’s empirical equation relates porosity and irreducible water saturation to permeability. While this method is widely used due to its simplicity and reliance on standard log data, it has known limitations in formations with significant shale content and complex pore structures. Although more advanced models may better account for shale effects and complex pore geometries, their application was limited in this study due to the lack of sufficient core or NMR data for calibration. Nonetheless, the Timur-derived permeability profiles show reasonable trends when compared qualitatively with formation lithology, Vshale content, and the available DST-derived permeability from Well 1.

4. Data Analysis

In this study, data were analyzed in two different ways, for comparison. The first procedure used Techlog software, whereas the second was performed manually, by collecting data at each depth and then calculating the petrophysical parameters using the above-mentioned equations. These calculated parameters were also 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 6, Figure 7, Figure 8 and Figure 9) was initially conducted using the Techlog software. The gamma 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 [24] 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.

The petrophysical analysis presented in this study is subject to uncertainties due to the complex lithology of the Bhuban Formation and reliance on empirical parameters in models such as Simandoux. In the absence of core or advanced log data, fixed values for key inputs (e.g., m, n, a, C) were used based on regional analogs. Future studies should integrate probabilistic modeling and sensitivity analysis to better constrain parameter ranges and improve confidence in reservoir evaluations.

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 10). 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 11). 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 12). 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 13). 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 14). 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 15). 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 16). 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 17). Hydrocarbon saturation shows a slight increase with improved porosity. Shale content remains a limiting factor for reservoir quality, and the porosity–permeability correlation is positive and linear.

In Sand G, porosity increases with shale volume (Vsh) decrease, and permeability also improves (Figure 17), 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 [39]. 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. [39] 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. [40] 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 were 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), the 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 the 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 (

Table 5. 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%

) 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 [41], and along with permeability, it dictates the fluid storage capacity and productivity of the reservoir. Effective porosity was derived from a combination of effective density and neutron porosities. The overall effective porosity ranges (

Table 6. Average effective porosity (ϕ_DN-eff) from effective density (ϕ_{D_eff}) and effective neutron porosity (ϕ_{N_eff}).

Sand	Well_1		Well_2		Well_3		Well_4
	ϕD_eff	ϕN_eff	ϕD_eff	ϕN_eff	ϕD_eff	ϕN_eff	ϕD_eff	ϕN_eff
	ϕDN-eff (%)		ϕDN-eff (%)		ϕDN-eff (%)		ϕDN-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%

) from 4% to 20%. Sands A, B, D, and E exhibit favorable porosity values and are considered good-quality reservoirs.

Hydrocarbon saturation (Shc) values (

Table 7. 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	-		-		-

) further confirm reservoir potential. Based on the criteria of Asquith and Gibson [42], 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 [43] 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 (

Table 8. 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	0.08	0.11	0.13	0.05
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	-	-	-

). 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 [44], 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 hydrocarbon saturation also 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 [45], BVW values between 0.035 and 0.07 are indicative of fine to very fine-grained sands. The results (

Table 9. 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

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.

The calculated average petrophysical parameters vary across different wells in the reservoir layers. Given the deltaic depositional environment of the Bhuban Formation and the observed variability in petrophysical parameters across wells, future work could benefit from machine learning approaches that integrate seismic attributes. For instance, recent studies [46] have demonstrated the successful application of a probabilistic neural networks (PNN) combined with multi-attribute seismic analysis to predict porosity in complex deltaic reservoirs. Applying similar techniques in the Srikail Gas Field may improve reservoir property prediction, especially where core data are unavailable.

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 hard copy 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 m, 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 fourth order finning upwards cycle [21].

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.

The results of this study contribute to a better understanding of reservoir heterogeneity, fluid flow behavior, and fracture network characterization in unconventional gas systems. Moreover, the outcomes of this research can be applied to other gas fields in and around the Bengal Basin, where similar unconventional sand reservoirs are present.

Future studies should adopt a more integrated and comprehensive approach to enhance reservoir characterization in the Srikail Gas Field. In particular, the use of multiple shaly sand models—following the comparative methodology of Johanna & Kusumah [47]—is recommended to better assess the impact of shale distribution and geometry on key petrophysical parameters. Additionally, incorporating core-based analyses such as porosity and permeability measurements, capillary pressure data, and XRD-derived clay mineralogy would significantly strengthen the calibration and validation of log-derived models. This integration of core data, advanced modeling, and seismic interpretation would reduce uncertainties, refine input parameters, and ultimately provide deeper insights into the reservoir heterogeneity of the Bhuban Formation, thereby supporting more accurate reservoir evaluation and improved hydrocarbon recovery strategies.