Reservoir Characterization and Rock Typing Assessment Utilizing Hydraulic Flow Unit (HFU) and Global Hydraulic Elements (GHE) Of Sanga Sanga Early-Middle Miocene Sandstone, Kutai Basin, Indonesia
Eki Komara1, Nita Ariyanti1,2, Muhammad Zayem Ghifari Rizal1, Wien Lestari1, Numair Ahmed Siddiqui3, and Muhammad Faiz Nugraha1
1Geophysical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2Geological Engineering, Gadjah Mada University, Yogyakarta, Indonesia
3Department of Geoscience, Universiti Teknologi Petronas, Perak, Malaysia
*Corresponding address: [email protected]
Abstract. Sandstone reservoirs in particular are challenging to characterize of the variety of the rock within them, which yield in compositional, and porosity changes.
This study incorporates Multi-Resolution Graph-Based Clustering (MRGC) to spread the rock type. The uncored well data and additional validation method for rock typing.
Permeability predictions, derived from Hydraulic Flow Units (HFU) and Global Hydraulic Elements (GHE) methods, demonstrate robust correlations with regression trendlines HFU yields a coefficient correlation value of 0.88, while GHU exhibits a slightly higher value of 0.9418. The classification of rock types based on Hydraulic Flow Units (HFU) tends to exhibit a more positive outlook, whereas the classification based on Global Hydraulic Elements (GHE) presents a more negative appraisal. This discrepancy underscores the importance of employing multiple methods for more comprehensive reservoir understanding. The findings provide valuable insights into reservoir quality, and permeability prediction accuracy with values above 85%, essential for effective reservoir management and hydrocarbon production optimization.
1. Introduction
The development of sandstone reservoir obstacles in the form of unpredictability and contamination (Raza, 2015). Global Hydraulic Elements (GHEs) are a priori systematic series of Flow Zone Indicator (FZI) values that can be used to define petrophysical elements such as porosity, density, and permeability. The GHEs are determined based on the FZI values, which calculated from the permeability and porosity data. The GHEs used to divide the reservoir into a series of hydraulic units (HUs). The HUs used to create a basemap for navigating through permeability and porosity data for reservoir comparison and permeability prediction. The use of GHEs and HUs allows for a more accurate and detailed analysis of the reservoir, which can lead to better decision-making in reservoir management. The HFUs approach involves dividing the reservoir into a priori systematic series in reservoir characterization. The HFUs approach involves dividing the reservoir into a priori systematic series of FZI values, known as GHEs, which are used to define petrophysical elements, known as petrotyping, and create the HFU.
2. Geological Setting
The formation of the Kutai Basin can be attributed to extensional processes that occurred in the Middle Eocene, which were then followed by a period of elevation of the basin floor that culminated in the Late Oligocene. The elevation of the basin floor in the Kutai Basin was caused by heightened
pressure resulting from plate collisions. This upward movement towards the northwest led to the formation of significant regressive cycles of clastic sedimentation. The state of uplift has experienced minimal disturbance from the Late Oligocene epoch to the present era. This rifting resulted in the formation of a sequence of fault systems that exhibited eastward-dipping extensional characteristics.
These fault systems created half-grabens, which subsequently were filled with sediment originating from terrestrial sources and deposited into the adjacent sea. By the end of the Oligocene, there is evidence of regional contractional deformation and uplift in the western part of the Kutai Basin. In the Early to Middle Miocene, there was a major tectonic adjustment due to the rotation of Kalimantan (20- 10 Ma), resulting in deformation and uplift of Kalimantan and the influx of volcanogenic clastics into the Kutai Basin from terranes to the west. The convergence between the microcontinental block and the subduction zone along the northwest margin of Kalimantan (Palawan Trench) led to uplift, giving rise to the Central Kalimantan Mountains. In the Sangasanga area, this coincides with a shelf composed of Miocene sandstones and carbonates overlain by an outer shelf, which in turn is covered by sandstones and shales from the progradational system of the Miocene Mahakam Delta.
The beginning of the Middle Miocene (~14.0 Ma) marks the onset of a major period of basin inversion in Sangasanga. Progressive deformation migrated from west to east, with east-dominant reverse faults and the migration of depocenters within the Kutai Basin. The collision of the Banggai- Sula area in Sulawesi during the Late Middle Miocene (10.5 Ma) led to increased depocenter shifting and inversion in the eastern section of the Kutai Basin. In the Early Pliocene (6.5 Ma), structural inversion and progressive eastward shifting of depocenters continued. During the Pliocene- Pleistocene, inversion and uplift of the Southern Meratus Mountains from the Kutai Basin indicate ongoing contraction. The Mahakam Fold Belt had a phase of late tightening and thrusting, which predominantly took place inside the anticlines located in the Sangasanga Block. The primary tectonic compressional phase is understood to be the consequence of the collision between the Indo-Australian plate and the Banda Arc. Throughout the history of Kutai Basin inversion from the Middle Miocene to the present, a dominant northwest-directed trend is deduced from relative plate movements and the major fold structure trends. This trend is observable both onshore and offshore and has been confirmed through drilling studies in the Kutai Basin.
Figure 1. (A) Regional Geology of Indonesia (B) Regional Geology of Kalimantan (Borneo) (C) Stratigraphic Chart Time with Pink Boxes as the Age of The Research Early-Middle Miocene
Sandstone Facies Of Balikpapan and Kampung Baru Formations
The petroleum systems of the Kutai Basin are characterized by hydrocarbon source rocks consisting of shale and coal deposits dating from the Early Miocene to Middle Miocene, primarily found within the Pamaluan, Pulubalang, and Balikpapan Formations. The lithological types serving as reservoir rocks within the Sangasanga Block are sandstone facies formed as a result of regressive sedimentation within the deltaic system. These specific sandstone facies, known as the Balikpapan and Kampung Baru Formations, have been accurately dated through biostratigraphic analysis to encompass an age range spanning from the Middle Miocene to the Late Miocene/Early Pliocene.
3. Reservoir Geology
Referring to various existing studies, these balikpapan group reservoir sandstone facies typically exhibit good to excellent porosity, with porosity values ranging from 15% to 30%. The impermeable rocks acting as cap rocks in the Sangasanga Block are relatively thick shale beds, generally deposited in environments within Late Miocene deltaic system, which are younger than the sandstone layers acting as reservoir rocks.
The hydrocarbon trapping systems in the Kutai Basin are dominated by structural traps formed by anticlines and upthrust and normal or inversion faults, as well as combinations of these structural elements. Hydrocarbon migration is believed to occur through two main mechanisms. First, vertical migration from older source rocks stratigraphically located deeper, such as the upper portions of the Pulubalang Formation, which can migrate through fault pathways. Second, lateral migration, primarily originating from organic-rich shale layers of the same age as the reservoir rocks, sourced from the Balikpapan Formation itself.
4. Method
4.1 Hydraulic Flow Unit
A Hydraulic Flow Unit (HFU) is defined as a volume unit of rock whose members exhibit somewhat similar fluid flow properties that differ from those in other units [8]. The concept of HFU differs from lithofacies as it aims to group similar fluid pathways within a reservoir rather than being based on lithological distribution. HFUs can be determined by utilizing the reservoir quality index, pore matrix ratio, and flow zone indicator [1]. After the HFU parameters calculation, the results of HFUs parameters used to determine rock type of the reservoir.
√
∅Ke=(
F τ12S2gv) ( (
1−∅∅e e) )
RQI=0.0314
√
φkφz= φ 1−φ
(1) FZI=RQI
φz FZI¿2
( (1−ϕϕe3e)
2)
k=1014¿
4.2 Discrete Rock Type
[18] suggested the discrete rock type (DRT) approach, which involves categorizing reservoirs into different RRTs based on the FZI parameter. In this approach, the reservoir is partitioned into different rock types using the formula below [9].
HFU or DRT=Round (2 ln (FZI)+10.6)) (2) where, DRT is the discrete rock type and FZI is the flow zone indicator in μm.
4.3 Global Hydraulic Elements
Hydraulic features refer to a predetermined and organized set of Flow Zone Indicator (FZI) values that serve the purpose of delineating petrophysical features, commonly referred to as petrotyping. The Global Hydraulic Elements were determined based on the FZI values, which are calculated from the permeability and porosity data of the reservoir. The Global Hydraulic Elements are used to divide the reservoir into a series of hydraulic units (HFUs). The HFUs were used to create a basemap and atlas for navigating through permeability and porosity data for reservoir comparison and permeability prediction. Therefore, the Global Hydraulic Elements are determined based on the FZI values, which were calculated from the permeability and porosity data of the reservoir. To characterize a particular set of petrophysical rock types, Corbett et al. proposed the word ‘petrotype’, which refers to a set of global hydraulic elements (GHE) characterized by a systematic arrangement of FZI values and prescribed graphs according to the Kozeny-Carman formula, as
K=ϕ((FZI)x(ϕ/(1-ϕ))/0.0314)^2 (3)
The GHE technique utilizes an ordered arrangement of FZI values to effectively cluster data and discern distinct fields that have similar petrophysical features and geological significance. Trends can be easily determined by plotting plug data on the GHE’s ‘basemap’ and these trends can convey geologic meaning shown on Figure 2. To distinguish between various rock types, one can also achieve this differentiation by grouping them based on their corresponding FZI values cutoff of each GHE rock type, as exemplified in Table 1.
Figure 2. Global Hydraulic Elements Basemap 4.4 Multi Resolution Graphed based Clustering
The MRGC framework is a comprehensive methodology that utilizes a combination of geostatistical and neural network techniques to analyze changes in log behavioral responses through a multi- dimensional dot pattern idea. This method relies on parameters such as k-nearest neighbors and data graphs, as shown in Figure 3 . The objective of the clustering process is to categorize data points that exhibit similarities into a single cluster, while ensuring that dissimilar data points are allocated to separate clusters. In the Minimum Redundancy Maximum Gain Clustering (MRGC) algorithm, the process of partitioning the data into clusters is performed in an automated manner, with the objective of optimizing the clustering based on the density, size, and shape characteristics of the input data.
Within the context of MRGC (Multi-Resolution Graph Clustering), the aforementioned clustered points, referred to as dot clusters, are organized into distinct groups known as electrofacies. These electrofacies are subsequently linked to variations in rock attributes and validated by the comparison with rock type findings derived from core data [23].
Figure 3. K-Nearest Neighbors Algorithm works in MRGC Method 5. Result
5.1 Core Data HFU
Before processing the core data, it's important to note that based on the SCAL and RCA data readings, the core data falls into the "water" category. This data range spans from 890.60 m to 905.65 m.
According to the core data, within the studied Balikpapan Formation, three lithological categories were identified; a) depth 890.60 m to 898.19 m, with combination of sandstone and claystone, b) depth 898.19 m to 898.74 m, with claystone and thin silt interbeds, and c) depth 898.74 m to 906.65 m, with sandstone.
In the core data processing based on porosity and permeability data, calculations of hydraulic flow unit parameters were performed using the formula on method chaper formula in Chapter III.
Results of the hydraulic flow unit parameter processing shows what explain these results as these are main resultson Table 2 :
Table 2. Hydraulic flow Unit parameter Results
Upon performing the calculation of the hydraulic flow unit, discrete values for rock types were acquired and are presented on Table 2 and Figure 7A. The determination of rock types is not exclusively reliant on the discrete rock type equation, since it necessitates supplementary validation to reinforce the accuracy of the assigned rock type values. Following this, a correlation was established between core porosity and core permeability, as well as between Normalized porosity and reservoir quality index on Figure 7B, and . This procedure was conducted in order to get insight into the distribution patterns of fundamental data values and to segment the data distribution by visually delineating gradient lines across the data dispersion. Furthermore, the verification of rock type identification was conducted by generating scatter plots that compared the outcomes of Mercury Injection Capillary Pressure study with wetting phase saturation. The outcomes obtained from the analysis of these three parameters for the purpose of rock type determination will provide the optimal quantity of rock types.
(A) (B)
(C)
Figure 4. Rock Typing Determination Parameters (A) Discrete Rock Type (B) Porosity Core and Permeability Core Plot (C) Normalized Porosity and Reservoir Quality Index Plot
Subsequently, based on the DRT (Discrete Rock Type) classification with a limit of 4 rock types determined by the scatter plot of the HFU parameter, the following results were obtained, The
10 20 30 40 50 60 70 80 90 100 110
0 50 100 150 200 250
Air Brine Capillary Test
103_891.8 Meters 104_894 Meters 106_893.8 Meters 108_894.9 Meters 112_898 Meters 119_902.8 Meters Sw
Pc
four identified rock types are categorized, with RT-1 representing the most favorable (best) reservoir rock type, and RT-4 denoting the least favorable (poorest) reservoir rock type :
Table 3. Final Results of Rock Type Classification based on HFU Method
Depth Flow Zone Indicator Rock type
891.8 4.927037463 1
891.85 4.087517531 1
900.8 2.999028721 1
894.5 2.389385173 2
898 2.249827488 2
900.3 2.172868078 2
902.8 2.002755813 2
905.5 1.974040011 2
893.8 1.772153838 2
891.3 1.732095477 2
897.3 1.617605378 2
902.7 1.315938329 3
894.77 1.276528656 3
894.9 1.144270458 3
902.4 1.167890076 3
899.8 1.086434039 3
904.7 1.132175401 3
903.8 1.057014691 3
896.5 0.974672064 3
893 0.890609658 3
893.78 0.782084322 3
896.1 0.770995674 3
903.3 0.492342788 4
899 0.438586632 4
890.8 0.381003537 4
893.5 0.351720887 4
Porosity vs Permeability Core
1000
100 y = 53805x4.3485
R² = 0.803
5x20.579
.9243
10
1
RT 1 RT 2 RT 3 RT 4
0.10.1 0.15 0.2 0.25
Porosity (Fraction)
222x5.7733
0.8155 y = 164
R² = y = 1227.4x3.8506 R² =
0.781
y = 4E+1 R² = 0
(A)
(B)
After completing calculations and rock type determination, to validate the results, permeability prediction based on hydraulic flow unit was calculated using the formula on method . The results of the coefficient correlation value is relatively high it means that the correlation of permeability prediction based on regression trendline is near perfect with 0.8758 shown on figure.
1 10 100
1 10 100
1000 f(x) = 1811.02 x^-1.91 R² = 0.76
Permeability Core vs Permeability Prediction
Permeability Core
Permeabilty HFU
Figure 5. Correlation Between Permeability HFU and Permeability 5.2 Core Data GHE
The identification of rock types in the global hydraulic elements (GHE) approach is based on the assessment of porosity and permeability, which are then utilized to construct a plot using the GHE formula. In order to differentiate between different types of rocks, the graph is cross-referenced with the basemap, and the table presents the maximum and lowest values of the Flow Zone Indicator (FZI) for each GHE rock type, as depicted in Chapter III. It is worth mentioning that, akin to the Hydraulic Flow Unit methodology, the categorization of rock types is conducted into four distinct classifications.
The findings derived from both the global hydraulic flow unit and hydraulic flow unit approaches
Normalized Porosity vs Reservoir Quality Index
10
1
0.1
RT 1 RT 2 RT 3 RT 4 0.01
0.1 0.2
Normalized Porosity
0.4
exhibit a high degree of similarity, with only a single core sample displaying variation between the two rock typing methodologies, as illustrated in the table and graphs provided thereafter on Table 4 and Figure 6 below.
Table 4. GHE Parameters Results
Figure 6. Distributiion of Rock Type on Core Data Parameters
Upon completing the calculations and rock type determination, a validation step was carried out by predicting permeability based on global hydraulic elements using the formula outlined in Chapter III. The results indicated a high coefficient correlation value, with a value of 0.9418. This high correlation coefficient on Figure 7 suggests that the permeability prediction based on the regression trendline is nearly perfect, indicating that the permeability prediction based on global hydraulic elements outperforms the hydraulic flow unit method in terms of accuracy and reliability.\
Figure 7. Correlation Between Permeability GHU Prediction with Permeability Core 5.3 Validate Thin Section
After obtaining the rock type classifications, a scatter plot was created to visualize the distribution of the resulting values based on the core data and rock type assignments. The scatter plot revealed that, as shown in the figure, the rock type distribution, when analyzed with the provided regression trendline, yielded fairly good coefficient values. Therefore, the rock type assignments based on the core data appear to be quite relevant. Subsequently, an interpretation was conducted, incorporating correlations between thin section data (petrography) obtained from core reports (SCAL & RCA).
(A)
(A)
Figure 8. Scatter Plot between Porosity and Permeabilty with Thin Section each Rock Type Method (A) Hydraulic Flow Unit and (B) Global Hydraulic Unit
(A) (B)
(C)
(D)
Figure 9. Thin Section of Each Rock Type (A) I (B) II (C) III (D) IV 5.4 MRGC
Subsequently, in Figure 8 and 9, the correlation between thin section data and petrography results was obtained, along with the rock type classifications in the scatter plot. Here are the findings for each rock type; (1) RT-1: It was determined to be medium-grained sandstone, well-sorted, classified as subarkose with subangular to rounded grains, mostly exhibiting contacts followed by planar contacts, (2) RT-2: Identified as fine-grained and well-sorted sandstone, classified as sublitharenite. It is composed mainly of quartz with rock fragments (chert, metaquartzite, granitic type, schist, sandstone, volcanic type, and claystone), plagioclase, K-feldspar, and organic material, (3) RT-3: Found to be very fine-grained and well-sorted sandstone, classified as feldspathic litharenite. It contains quartz with rock fragments (chert, sideritic clast, metaquartzite, schist, granitic and volcanic type, sandstone, calcareous debris), plagioclase, K-feldspar, and other minerals such as muscovite, zircon, and hematite. (4) RT-4: Identified as fine-grained and well-sorted sandstone, classified as lithic arkose. It consists of subangular to rounded grains with some points indicating planar contact boundaries. Th3 composition includes quartz, rock fragments (cherty, granitic type, metaquartzite, calcareous debris, and sandstone), plagioclase, K-feldspar, and additional minerals like muscovite, zircon, and organic material. These findings were then combined with the descriptions of other core data that had petrographic information.
Table 5. Summary of Rock Typing Results of GHU Method
Table 6. Summary of Rock Typing Results of HFU Method
Roc k type
Parameter
Value
Lithology Texture
Min Mean Max
1
Porosity 0.225 0.23 0.237
Subarkose Massive
Permeabilit y
173 357.6666 7
563
FZI 2.9990287 2
4.004527 9
4.9270374 6
2
Porosity 0.181 0.20925 0.228
Bioturbated, Massive,
& Faintly Laminated Permeabilit
y
26.9 63.15 98.1
Feldsphatic Litharenite &
Sublitharenite FZI 1.6176053
8
1.988841 4
2.3893851 7 Porosity 0.16 0.192363
6
0.222
Sublitharenite
Massive & Faintly Laminated Permeabilit
y 4.67
13.66363
6 23.5
3 FZI 0.7709956
7
1.054419 4
1.3159383 3
Porosity 0.118 0.1455 0.17
Lithic Arkose Massive
Permeabilit
y 0.265 0.80575 1.05
4 FZI 0.3517208
9
0.415913 5
0.4923427 9
Subsequently, after obtaining the hydraulic flow unit and rock type data, the distribution of rock types in the 'MZG' reservoir marker and the distribution across all other wells was conducted using the Multi- Resolution Graph Based Clustering method. This distribution process was carried out in two stages. The first stage involved the distribution with the marker alone from petrophysics analysis, and the second stage involved spreading the distribution to all wells, both horizontally and
vertically.
(A)
(B)
(C)
Figure 10. Results of MRGC Distribution HFU and GFE on (A) X-1 Well (B) X-2 Well (C) X-3 Well Based on the distribution of rock types, as depicted in the provided figure, it is evident that the rock type predictions using the Multi-Resolution Graph Based Clustering (MRGC) method, particularly in the MZG-1 well, or any well with core data, closely match the pre-identified rock type data with an accuracy exceeding 75%. The rock type prediction derived from global hydraulic elements (GHE) exhibits a somewhat superior performance in comparison to the rock type prediction derived from hydraulic flow units (HFU), since GHU yields one less distinct prediction than HFU.. As we can see on figure 10 the rock type classification and prediction using GHU is more pessimistic than using HFU, but we know that both of RT-1 and RT-2 is considered good quality reservoir, therefore it is fine despite the differences of the prediction. Indeed, it's observed that the rock type classification and prediction using the Global Hydraulic Elements (GHU) approach may be more pessimistic compared to using the Hydraulic Flow Unit (HFU) method. However, it's important to note that both RT-1 and RT-2 are considered to represent good quality reservoirs.
These differences in prediction between GHU and HFU may arise from variations in the underlying assumptions and parameters used in each method. Despite the disparities in prediction, the fact that both RT-1 and RT-2 are identified as good quality reservoirs is reassuring. The characterization of rock typing based on the presented log parameter data, including Gamma ray, Bulk density, Neutron Porosity, Vshale, Vcoal, Total Porosity, and Effective Porosity, can have variations and may not always result in 100% similarity between rock types. Several factors contribute to this variation;1) Core Data VariabilityThe core data obtained may represent various electrofacies, and in some cases, the samples may not have been collected under optimal conditions (e.g., damaged or fragmented samples). 2) Algorithmic Approach: The use of the K-Nearest Neighbors algorithm can result in selecting the best-fit values based on the propagation of log parameters and rock typing. This means that for certain depth intervals, where multiple core-based rock typing data points exist (e.g., within the depth range of 893 m to 894 m), the algorithm selects the best-suited rock typing values, leading to narrower ranges.
In Figure 10 (B & C), specifically in wells MZG-2 and MZG-3, the rock type determination aligns well with the presence of hydrocarbon-bearing oil. However, for MZG-1, the determined rock type suggests the presence of fresh water. In the characterization of reservoirs, it's observed that Rock Type 1 yields excellent reservoir capacity, which is consistent with Table 4.6, where lower rock type values indicate poorer reservoir quality (very poor). When examining the results of the three figures,
the reservoir characterization of RT-4 results in the lowest quality, with the majority of the depth interval being classified as coal or dominated by shale. Hence, when analyzing the correlations of the MZG reservoirs, it has been determined that MZG-3 exhibits the highest reservoir capacity as per the designated marker. However, in terms of the prevalence of sand bodies relative to the oil-water interface, MZG-2 demonstrates superior performance.
6. Conclusion
Permeability predictions based on the HFU method were yielding a relatively high coefficient correlation value of 0.8758. This result indicates a strong correlation between permeability predictions and the regression trendline, confirming the accuracy of HFU-based permeability estimations.
Whereas, GHE permeability predictions results in an exceptionally high coefficient correlation value of 0.9418. They provide essential insights into reservoir quality, capacity, and behavior, offering valuable guidance for reservoir management strategies. Variability in core data, including different electrofacies and sample conditions, can impact the accuracy of rock typing predictions. The K- Nearest Neighbors algorithm was used to select the best-fit values, particularly in cases with multiple core-based data points.
References
[1] Abdulelah, H., Mahmood, S. and Hamada, G. (2018, June). Hydraulic flow units for reservoir characterization: A successful application on arab-d carbonate. In IOP Conference Series:
Materials Science and Engineering (Vol. 380, No. 1, p. 012020). IOP Publishing.
[2] Abuhagaza, A.A., El Sawy, M.Z. and Nabawy, B.S. (2021). Integrated petrophysical and petrographical studies for characterization of reservoirs: a case study of Muglad Basin, North Sudan. Environmental Earth Sciences, 80, pp.1-18.
[3] Adrianto, R.D. (2017). Penentuan Flow Unit Batuan Reservoir pada Lapangan Rn. PETRO:
Jurnal Ilmiah Teknik Perminyakan, 6(1), pp.21-31.
[4] Amaefule, J. O., Altunbay, M., Tiab, D., Kersey, D. G., & Keelan, D. K. (1993, October).
Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. In SPE annual technical conference and exhibition. OnePetro.
[5] Asquith, G.B., Krygowski, D. and Gibson, C.R. (2004). Basic well log analysis (Vol. 16). Tulsa:
American Association of Petroleum Geologists.
[6] Bambang Bintarto, B. and Dewi Asmorowati, D. (2014). Penentuan Reservoir Rock type Berdasarkan Metode Hydraulic flow unit (HFU) Dj Reservoir Batuan Karbonat. In Prosiding Seminar Nasional Kebumian-IX 2014 (pp. 175-179). Fakultas Teknologi Mineral UPN" Veteran" Yogyakarta.
[7] Belhouchet, H.E. and Benzagouta, M.E. (2019). Rock Typing: Reservoir Permeability Calculation Using Discrete Rock Typing Methods (DRT): Case Study from the Algerian BH Oil Field Reservoir. In Advances in Petroleum Engineering and Petroleum Geochemistry: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 9-12). Springer International Publishing.
[8] Ebanks Jr, W.J. (1987). Flow unit concept-integrated approach to reservoir description for engineering projects. AAPG (Am. Assoc. Pet. Geol.) Bull.;(United States), 71(CONF870606-).
[9] El Sawy, M.Z., Abuhagaza, A.A., Nabawy, B.S. and Lashin, A. (2020). Rock typing and hydraulic flow units as a successful tool for reservoir characterization of Bentiu-Abu Gabra
sequence, Muglad basin, southwest Sudan. Journal of African Earth Sciences, 171, p.103961.
[10] Kakemem, U., Ghasemi, M., Adabi, M.H., Husinec, A., Mahmoudi, A. and Anderskouv, K.
(2023). Sedimentology and sequence stratigraphy of automated hydraulic flow units–The Permian Upper Dalan Formation, Persian Gulf. Marine and Petroleum Geology, 147, p.105965.
[11] Gorrell, H.A. (1958). Classification of formation waters based on sodium chloride content.
AAPG Bulletin, 42(10), pp.2513-2513.
[12] Glover, P.W. (2000). Petrophysics. University of Aberdeen, UK.
[13] Guo, G., Diaz, M.A., Paz, F., Smalley, J. and Waninger, E.A. (2007). Rock typing as an effective tool for permeability and water-saturation modeling: a case study in a clastic reservoir in the Oriente Basin. SPE Reservoir Evaluation & Engineering, 10(06), pp.730- 739.
[14] Novikri, I. (2018). Permeability Prediction in Uncored Wells Using an Advanced Statistical Technique.
[15] Rider, M.H. (2002). The geological interpretation of well logs.
[16] Schlumberger (2009). Log Interpretation Chart. Houston: Schlumberger.
[17] Shalaby, M.R., Binti Sapri, S.H. and Islam, M.A. (2020). Integrated reservoir characterization and fluid flow distribution of the Kaimiro Formation, Taranaki Basin, New Zealand.
Journal of Petroleum Exploration and Production Technology, 10(8), pp.32633279.
[18] Shenawi, S.H., White, J.P., Elrafie, E.A. and El-Kilany, K.A. (2007, March). Permeability and water saturation distribution by lithologic facies and hydraulic units: a reservoir simulation case study. In SPE Middle East Oil and Gas Show and Conference (pp. SPE-105273). SPE.
[19] Sritongthae, W. (2016, April). Petrophysical rock typing: Enhanced permeability prediction and reservoir descriptions. In AAPG Geosciences Technology Workshop, Characterization of Asian Hydrocarbon Reservoirs (pp. 1-52).
[20] Sukmono, S. (2000). Seismik Inversi Untuk Karakterisasi Reservoir. Departemen Teknik Geofisika ITB. Bandung.
[21] Thabrani, R., Fahrudin, R.S. and Salni, N.R. (2019). Reservoir Characterization Based on Probability and Flow Zone Indicator (FZI) Method Using Multi-Resolution Graph-Based Clustering (MRGC) in The Kais Formation, Brani Field, Salawati Basin, West Papua.
[22] Tiab, D., & Donaldson, E. C. (2015). Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Gulf Professional Publishing.
[23] Ye, S. J., & Rabiller, P. (2000, June). A new tool for electro-facies analysis: multi resolution graph-based clustering. In SPWLA Annual Logging Symposium (pp. SPWLA-2000).
SPWLA.
[24] Yilmaz, Ö. (2001). Seismic data analysis: Processing, inversion, and interpretation of seismic data. Society of Exploration Geophysicists.