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Abstract The Upper Cretaceous carbonate reservoir oils from the Abadan Plain and Dezful Embayment in southwest Iran underwent analysis utilizing GC, GC-MS, and stable carbon isotope methods to elucidate the regional genetic oil family distribution. The study encompassed oil-source rock correlation through biomarker and stable carbon isotope ratios of Sargelu, Garau, Kazhdumi, and Pabdeh rock extracts to assist in establishing the best relevant source rock. Steranes and hopanes thermal maturity parameters were employed to define early maturity to the peak of oil generation level in the potential source rock. Equivalent vitrinite reflectances, estimated using aromatic compounds, suggest these paraffinic-naphthenic to aromatic-intermediate oils expelled from source rocks thermally matured from 0.68 to 0.85% Ro. The findings categorized the studied oils into families A and B. Family A oils, primarily associated with the Abadan Plain reservoirs, demonstrated a prevalence of more reducing conditions with a carbonate input predominance in early mature source rock. Family B oils originated from marl/shale facies in the Dezful Embayment, where the deeper source rock matured up to the peak of oil generation. Both hopanes and regular sterane distribution patterns reveal a contribution of terrigenous organic matter for both families. However, the δ13Csat and δ13Caro ratios demonstrate marine organic matter input predominance. The analysis emphasized the potential significance of the Kazhdumi Formation as the primary source rock for Family B oils in the Dezful Embayment. The Garau Formation was identified as the main candidate source rock, with the Sargelu and Kazhdumi Formations playing secondary roles to some extent in the generation of Family A oils. Additionally, the study underscored the impact of source rock maturity, organofacies, and trap formation timing as crucial factors influencing the geochemical characteristics of these reservoir oils across the studied area.

Abstract The primary purpose of this study is to identify the high reservoir quality area for drilling and perforation of new wells using interrelated geological, petrophysical, and seismic data. Various datasets such as, core analysis data (two wells), petrophysical logs (twenty wells) and 3D seismic cube were used to consider different scenarios for optimum well placement based on the availability and accuracy of the data. Considering four criteria (structure, reservoir property, fracture network, and heterogeneity), various data were combined to assess reservoir potential. A static reservoir model was constructed to reduce the uncertainty of the well location determination. A range of scenarios were implemented based on this static reservoir model or by relying only on the available seismic data. Electrofacies analysis was conducted to establish representative facies with which to guide the forecasting of locations suitable for drilling the new wells. Six facies were determined based on multi-resolution graph-based clustering (MRGC) method. In parallel, 3D seismic data was applied to determine seismic facies and seismic attributes to identify high-quality reservoir areas for well placement. As high fracture intensity is an essential feature required for high oil/gas production rates, a continuous fracture model (CFM) was constructed, and average intensity fracture maps were developed to determine the best production locations and to avoid low-productivity (or potential loss-of-circulation zones) from a drilling perspective. Some structural issues, such as distances from transition zones and/or faults, were also considered for well-placement purposes. Coefficient of variation (as a heterogeneity criteria) was also taken into account for well-placement decision-making. The results indicate that applying the developed integrated approaches reduces well-placement uncertainty and delineates high-quality reservoir zones (or sweet spots) with more confidence.

Abstract The Asmari succession is among the greatest productive hydrocarbon-bearing rock units in the Middle East. This paper examines the biostratigraphy, sedimentary environment, and depositional sequence of the Asmari reservoir in the Haft-Kel Field. This succession in the Haft-Kel Field contains limestones, dolostones, anhydrites, and argillaceous limestones and is bounded between the Pabdeh and Gachsaran formations. Paleontological research has led to the creation of three assemblage zones in this region. These zones present an age range from the late Oligocene (Chattian) to the early Miocene (Aquitanian). Also, detailed petrographic examinations and facies analysis led to the recognition of 14 types of microfacies, ranging from open marine environments to intertidal and supratidal systems. The distribution and association of recognized microfacies proposed carbonate ramp-type platform deposition for the Asmari Formation. In addition, three third-order depositional sequences (sequences 1, 2, and 3) have been recognized through the Asmari Formation in the Haft-Kel Oil field that is well correlated with sequences IV, V, and VI proposed by van Buchem et al. (2010), respectively. Using the Hydraulic Flow Unit (HFU) concept, six rock types have been identified for the Asmari reservoir. Finally, three reservoir zones, comprising none, moderate, and good reservoir zones, were introduced for the Asmari reservoir in Haft-Kel Field. No reservoir zones are visited in the lower part of the formation in depositional sequence 1 and to some extent in the Low Stand Systems Tract (LST) part of depositional sequence 2 and 3, while moderate to good reservoir zones are developed in the upper intervals of the formation, especially in the High Stand Systems Tract (HST) parts of depositional sequence 2 and 3.

The upper Dalan and Kangan or Permian-Triassic carbonate formations in the central the Persian Gulf are considered as world's giant gas reservoirs. The primary purpose of this research is to model and evaluate the relationship between hydraulic flow units (HFUs), electrofacies and microfacies with systems tracts of Permian-Triassic sequences. By integrating the results of core data, petrographic studies, and petrophysical logs of the studied formations, hydraulic flow units and electrofacies were identified. Based on the results of petrographic studies, twelve microfacies were identified in terms of textural and depositional characteristics. Based on depositional setting, sedimentary facies and INPEFA values obtained from gamma ray log and gamma deviation log (GDL) in the context of sequence stratigraphy, zonation of Dalan and Kangan reservoirs is carried out. The zonation boundaries correspond to the key stratal surfaces (sequence boundary and maximum flooding surface). Seven petrographic rock types (PRT) were identified for the upper Dalan-Kangan reservoirs based on sedimentary texture, diagenetic process and dominant pores. Using porosity and permeability data from the core analysis, five hydraulic flow units were identified based on the flow zone indicator (FZI) method. Using multi-resolution graph-based clustering (MRGC) four electrofacies were detected from petrophysical data (gamma, neutron, density and acoustic logs). Subsequently, the INPEFA, GDL and electrofacies were spatially modeled using the sequential indicator simulation (SIS) and sequential Gaussian simulation (SGS) geostatistical methods. Finally, a clear agreement was revealed between the reservoir zones and the stratigraphic sequence framework. It this regard, the microfacies belonging to the high-energy and grain-dominated settings (packstone, grainstone) of leeward shoal, shoal and seaward shoal belts have the best reservoir units due to the influence of dissolution and dolomitization. The best reservoir units in the Permian-Triassic deposits in the middle of the upper Dalan and lower Kangan are developed in UDS4, upper KS2 and middle KS1 units. On the other hand, mud-dominated facies (mudstone, wackestone) and anhydrite textures are mostly associated with the low-energy lagoonal environments, between tidal flat and Sabkha. Non-reservoir units have been formed in the upper Dalan/Kangan and in the transgressive systems tract of UDS3-a, KS2-a and the lower and upper part of KS1 transgressive-highstand systems tract.

Abstract The oil and gas industry’s reliance on well cementing practices to ensure well integrity and productivity is a pressing concern, particularly in exploratory wells where unpredictable conditions can lead to subpar cement bonding efficiency. This study addresses the pressing issue of optimizing cementing practices in Iran’s exploratory wells, ensuring better well integrity and productivity. The preliminary objective is to investigate the impact of hole size and bottom hole temperature variations on cement bonding efficiency in these wells. We analyzed cement quality logs from 21 exploratory wells in reservoir zones between 2012 and 2022 to achieve this. The study employed a systematic approach, using CBL/VDL and cement formulation to assess the quality of each wellbore section separately. We designed a cement formulation based on changes in bottom hole temperature and gas migration control and implemented it using three different contractors, each with unique additives. It allowed us to compare the quality of cement resulting from different phases and additives. Our results show that cement designed with dominant liquid additions in the 5-inch liner phase exhibits better bonding than formulations with increased powder form additives, specifically those containing a liquid dispersant, liquid fluid loss controller, liquid anti-gas migration, and liquid H.T. retarder for LNR 5 inch. Additionally, smaller hole sizes demonstrate better cement bonding quality due to reduced fluid flow and less turbulence. These findings have significant implications for optimizing cementing practices in the petroleum industry, particularly in Iran’s exploratory wells. Overall, this investigation provides valuable insights for improving wellbore integrity and productivity by optimizing cementing practices, which can be applied to future drilling operations in Iran’s exploratory wells.

Abstract This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input logs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI). A total of 57 models were constructed using combinations of these logs and tested using five machine learning methods: Extreme Learning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique permeability predictions. RF had the highest correlation coefficient (0.925) and average error (0.196), indicating a precision-correlation trade-off. The ELM approach had the lowest average error, 0.083, and a correlation value of 0.871. Testing on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings. The findings emphasize the need of using appropriate machine learning algorithms and input data to improve model accuracy and reliability.

Due to the lack of rainfall potential in Central Iran, the water scarcity is still the main problem of production in the country. Accordingly, the use of unconventional waters will be inevitable. This study presents the amount of water required by the Chadormalu mine from the water wells of the Sepidan Plain, as well as the deficit or excess for exploiting the complex and the water treatment plant by reverse osmosis (RO) method. These studies were carried out to manage the groundwater resources of the Sepidan and Saghand basins and include the water level variations of piezometers, the water consumption of exploitive wells, aqueducts, and the Sepidan River every month. Furthermore, a sample of complete chemical analysis of the aqueducts' Saghand basin and the water entering the RO and leaving it for environmental evaluations have been carried out each season. The average discharges of water wells in the Sepidan Plain were 86.45 and 82.28 l/s in 2221 and 2023, respectively. Moreover, the amount of water exploitation has been lower than the renewable (dynamic) reserves, which in the heart of the water intake has a uniform increase in the monthly hydrograph's water level. At the same time, the reduction of total dissolved solids (TDS) entering from exploitation water intakes to the amount of 580 mg/l indicates the principled exploitation of dynamic reserves. The RO unit's output had no effect on the quality of the Saghand Playa's water but improved it qualitatively.

Abstract Previous attempts to classify flow units in Iranian carbonate reservoirs, based on porosity and permeability, have faced challenges in correlating the rock's pore size distribution with the capillary pressure profile. The innovation of this study highlights the role of clustering techniques, such as Discrete Rock Type, Probability, Global Hydraulic Element, and Winland's Standard Chart in enhancing the reservoir's rock categorization. These techniques are integrated with established flow unit classification methods. They include Lucia, FZI, FZI*, Winland R35, and the improved stratigraphic modified Lorenz plot. The research accurately links diverse pore geometries to characteristic capillary pressure profiles, addressing heterogeneity in intricate reservoirs. The findings indicate that clustering methods can identify specific flow units, but do not significantly improve their classification. The effectiveness of these techniques varies depending on the flow unit classification method employed. For instance, probability-based methods yield surpassing results for low-porosity rocks when utilizing the FZI* approach. The discrete technique generates the highest number of flow unit classes but provides the worst result. Not all clustering techniques reveal discernible advantages when integrated with the FZI method. In the second part, the study creatively suggests that rock classification can be achieved by concurrently clustering irreducible water saturation (SWIR) and porosity in unsuccessful flow unit delineation cases. The SWIR log was estimated by establishing a smart correlation between porosity and SWIR in the pay zone, where water saturation and SWIR match. Then, the estimated saturation was dispersed throughout the reservoir. Subsequently, the neural network technique was employed to cluster and propagate the three finalized flow units. This methodology is an effective recommendation when conventional flow unit methods fail. The study also investigates influential factors causing the failure of flow unit classification methods, including pore geometry, oil wettability, and saturation in heterogeneous reservoirs.

Abstract Pore types in carbonate reservoirs are more complex than their sandstone counterparts due to the wide spectrum of their depositional environments and their more complicated post-depositional processes. This means that a good knowledge of pore types is vital in determining carbonate formation’s elastic and reservoir properties. This study aims to develop a multi-physics approach to determine pore-type variations in a carbonate reservoir using well log information from one of the oilfields of the Abadan Plain in southwest Iran. Firstly, we determined lithology, porosity, and fluid content by interpreting conventional well logs (gamma ray, resistivity, density, neutron, photoelectric, and P-wave sonic). Then, nuclear magnetic resonance data were used to determine different pore types within the Main Ilam and Sarvak formations. We distinguished between clay, micro, meso, and macro pore types. We confirmed our interpretation results using thin sections, scanning electron microscopy photographs, and pore-size distribution on the available core plugs. Finally, a carbonate rock physics model was employed to model sonic velocities using petrophysical interpretation along with pore-type determination results. A good match between modeled and measured sonic velocities confirmed that using nuclear magnetic resonance data for pore-type determination can reasonably estimate pore-type variations needed for rock physics modeling. The standard industry procedure for carbonate rock physics modeling uses sonic logs, core data, or thin sections to determine pore types. We offer a substitute approach with reasonable accuracy for pore-type modeling needed for carbonate rock physics modeling. We modeled pore types independently from sonic velocity and used them to predict P-wave velocity with a correlation coefficient of 92 and 64 percent accuracy in the Main Ilam and Sarvak formations. Graphical abstract

Many new and developed oilfields are confined to carbonate reservoirs with a complex void space structure. In this study, the behavior of carbonates as a function of decreased reservoir pressure was examined. The study was based on actual production data from wells rather than just on model data or results from computational experiments. Well-test data (170 well tests) and laboratory core samples (45 samples) results were used in the analysis. The problem of reservoir deformation has been comprehensively addressed at the micro and macro levels. The effect of reservoir permeability throughout the entire reservoir volume on reservoir pressure was determined. With a decline in reservoir pressure, the permeability of fractures and pores in carbonates decreases. The deformation coefficients, which characterize the actual rate of decrease in permeability with decreasing reservoir pressure, were also calculated. The deformation coefficients and data from core studies using μ-CT and SEM were compared. Based on micro-CT data, the effective diameters of the voids were calculated to be 93 μm, 109 μm, and 140 μm for the Vinnikovskoe, Sofinskoe, and Sukharev fields, respectively. Based on μ-CT and SEM data, the predominant geometric shapes of the voids were assessed. The deformation coefficient of voids was found to depend on their size and geometric shape. The study's findings revealed deformation coefficients of 0.172, 0.205, and 0.748 for the Vinnikovskoe, Sofinskoe, and Sukharev fields, respectively. Maximum deformation coefficients are typical for large voids with a predominant slot-like shape. However, even with the predominance of primary spherical voids in carbonates, the permeability of voids decreases with decreasing reservoir pressure.

Abstract This study presents a pioneering machine learning approach to continuously model fracture intensity in hydrocarbon reservoirs using solely conventional well logs and mud loss data. While machine learning has previously been applied to predict discrete fracture properties, this is among the first attempts to leverage well logs for continuous fracture intensity modeling leveraging advanced ensemble techniques. A multi-level stacked ensemble methodology systematically combines the strengths of diverse algorithms like gradient boosting, random forest and XGBoost through a tiered approach, enhancing predictive performance beyond individual models. Nine base machine learning algorithms generate initial fracture intensity predictions which are combined through linear regression meta-models and further stacked using ridge regression into an integrated super-learner model. This approach achieves significant improvements over individual base models, with the super-learner attaining a mean absolute error of 0.083 and R^2 of 0.980 on test data. By quantifying the crucial fracture intensity parameter continuously as a function of depth, this data-driven methodology enables more accurate reservoir characterization compared to traditional methods. The ability to forecast fracture intensity solely from conventional well logs opens new opportunities for rapid, low-cost quantification of this parameter along new wells without requiring advanced logging tools. When incorporated into reservoir simulators, these machine learning fracture intensity models can help optimize production strategies and recovery management. This systematic stacked ensemble framework advances continuous fracture intensity modeling exclusively from well logs, overcoming limitations of prior techniques. Novel insights gained via rigorous model evaluation deepen the understanding of naturally fractured reservoirs.

Integrated reservoir rock typing in carbonate reservoirs is a significant step in reservoir modelling. The key purpose of this study is the identification of integrated rock types in the Sarvak Formation of an Iranian oilfield. In this study, electrofacies (EFAC) analysis of the Sarvak reservoir was done in detail to determine the reservoir quality and rock types of the Sarvak Formation in the studied field. The core data and conventional petrophysical logs were used for rock typing. Some petrophysical logs such as porosity, sonic, neutron, density, and Photo electric factor were applied as input data for electrofacies analysis. Multi-Resolution Graph-Based Clustering was used among six approaches, resulting in four electrofacies after merging similar clusters. EFAC-1 has the lowest porosity, and EFAC-4 has the highest porosity. In addition, based on Winland and Amaefule approaches, three rock types were determined using core data (porosity and permeability). As a result, three rock types were defined; rock type-1 has the smallest pore-throat size and flow zone indicator while rock type-3 has the highest ones. The correlation coefficient between permeability and porosity in each rock type is more than 80%. Rock type 1 is mainly composed of EFAC-1 and EFAC-2, while rock types 2 and 3 share mostly the EFACs 1, 3 and 4.

Abstract Overpressure is always considered as a severe problem in the oil industry. Besides creating life risks through serious accidents while drilling, failure to correctly identify high-pressure intervals causes a significant increase in costs and prolongs the drilling process. Kazhdumi Formation in the Abadan Plain is considered as a high-pressure formation in several wells. Various reasons can cause overpressure problem. Hydrocarbon generation is one cause of abnormal pressure in source rocks. Understanding hydrocarbon generation potential can be a helpful approach since the Kazhdumi Formation is considered as a probable source rock in this area. In this paper, in order to better understand the problem of abnormal pressure in Kazhdumi Formation, geochemical concepts and tools have been applied. To that way, 1D petroleum system modeling of five wells was done, and the thermal maturity level of Kazhdumi Formation was determined and then, compared with drilling records. The results indicate that in wells where this formation has sufficient organic matter and has reached an early mature level, there is an abnormal pressure problem. Otherwise, this formation does not show abnormal pressure. Also, geochemical data are not available in all drilled wells, which makes impossible the assessing of hydrocarbon generation role. Therefore, petrophysical well logs (sonic (DT), neutron (NPHI), density (RHOB), spectral gamma ray (SGR), and resistivity (RES)) as well as 137 sets of geochemical data belonging to 13 wells from 7 oilfields in the Abadan Plain were used to predict geochemical indicators. Using artificial neural networks, geochemical data of a well in Abadan Plain were predicted. This selected well has a high-pressure problem in Kazhdumi Formation, but no geochemical data are available in this well. The results of predicted geochemical data show that the high-pressure phenomenon in this well may also be due to hydrocarbon generation. The precise understanding of the abnormal pressure, resulting from hydrocarbons generation, requires comprehensive studies and a full investigation of the studied area. However, the results of this paper help to predict approximately the behavior of the source rocks before drilling. Acquiring this overview will aid in reducing drilling hazards and costs.

The gas-lift method is crucial for maintaining oil production, particularly from an established field when the natural energy of the reservoirs is depleted. To maximize oil production, a major field's gas injection rate must be distributed as efficiently as possible across its gas-lift network system. Common gas-lift optimization techniques may lose their effectiveness and become unable to replicate the gas-lift optimum in a large network system due to problems with multi-objective, multi-constrained & restricted gas injection rate distribution. The main objective of the research is to determine the possibility of using the genetic algorithm (GA) technique to achieve the optimum distribution for the continuous gas-lift injection flows in the structure of the network of Zubair, oil field with 10 gas-lift injected wells. This will be done through numerical simulation and modeling studies. The overall enhancement of the filed production rate is found to have increased from 15767 STB/day to 19847 STB/day. The well's reservoir pressure and water cut sensitivity studies are carried out to study the possible impacts of these elements upon the well and its efficiency through the course of the field. Our understanding of the potential benefits of utilizing gas lift techniques in a field from a technical and economical point of view is deepened by the use of examples from economic analysis. Furthermore, even though the idea of employing GA in this manner is not new, this work discusses GA-based optimization methodologies for increasing the oil production rate by using gas lifting in a Zubair oilfield. In order to assign gas injection rates to specific wells in a network throughout the field using limited gas injection rates, the model for optimization will be laid out step-by-step making it simple to understand and employ as a guide, especially for the front-line production technicians involved in the development and design of gas-lift systems.

Abstract This study aims to analyze in situ stresses and wellbore stability in one of the Iranian gas reservoirs by using well log data, including density, sonic (compressional and shear slowness), porosity, formation micro-image (FMI) logs, modular formation dynamics tester (MDT), and rock mechanical tests. The high burial depth, high pore pressure, and strike-slip stress regime of the field require an optimal design of geomechanical parameters based on an integrated data set consisting of static and dynamic data, which is available for this study. Firstly, poroelastic modulus and vertical stress were calculated. Afterward, the Eaton’s equation was used to estimate pore pressure from well logging data. The geomechanical parameters were also calibrated through the interpretation of image data, the use of the modular formation dynamics tester (MDT), and laboratory rock mechanic tests. Employing poroelastic equations, the lowest and highest horizontal stresses were calculated. It was shown that the maximum horizontal stress and minimum horizontal stress correspond to sigma H and sigma h, indicating the strike-slope fault regime. The findings of this research indicated that the equivalent mud weight (EMW) resulted in 10–13 ppg suitable for the Kangan Formation and 11–14 ppg suitable for the Dalan Formation. Additionally, the well azimuth in the NE-SW direction provided the best stability for drilling the encountered formations. Therefore, the results of this study serve as cost-effective tools in planning adjacent wells in carbonate formations of gas field to predict the wellbore stability and safe mud window.

The Upper Cretaceous carbonate successions of the Sarvak Formation host giant oil reservoirs in the Persian Gulf. In this research, a total of 28 oil samples from nine oilfields located in the western, central and eastern parts of the Persian Gulf region were studied to determine the genetic relationships of oils, depositional setting of possible source rocks, thermal maturity, and source-rock ages in the Persian Gulf basin. According to the measured geochemical data, the source rocks facies vary from marine carbonates and marl/carbonates in the central and eastern oilfields to shale/carbonates in the western oilfields. The Pr/Ph ratio, steranes and terpanes suggest anoxic to dysoxic conditions of the depositional environments. The depositional environments experienced both low water stratification/low salinity and normal salinity/unstratified conditions. Evaluation of the saturated and aromatic biomarkers shows that all oil samples are mature and most of the source rocks lie within the beginning of the oil-generation window. The thermal maturity of the central oilfields is higher than that of the other samples, and has gone beyond the oil-generation stage. The C28/C29 steranes ratio suggest that the central oilfields of the Persian Gulf have Paleozoic and Jurassic source rocks, whereas the Sarvak reservoir in other parts of this region is sourced from Cretaceous carbonate rocks.

Abstract The importance of characterizing kerogen type in evaluating source rock and the nature of hydrocarbon yield is emphasized. However, traditional laboratory geochemical assessments can be time-intensive and costly. In this study, an innovative approach was taken to bridge this gap by utilizing machine learning techniques to ascertain key parameters—Organic Oxygen Index (OI), Hydrogen Index (HI), and kerogen type—from petrophysical logs of a well in the Perth Basin, Western Australia. This approach assembled geochemical data from 138 cutting samples of the Kockatea and Woodada formations and petrophysical log data. Subsequently, six machine learning algorithms were applied to predict the OI and HI parameters. The efficacy of these methods was assessed using statistical parameters, including Coefficient of Determination (R2), Average Percentage Relative Error, Average Absolute Percentage Relative Error, Root Mean Square Error, and Standard Deviation. The Support Vector Machines method emerged as the standout performer, with an R2 of 0.993 for the OI and 0.989 for the HI, establishing itself as an optimal tool for predicting these indices. Additionally, six classifiers were employed to determine kerogen types, with accuracy tested using precision, recall, F1-Score, and accuracy parameters.The study's findings highlight the superiority of the Gradient Boosting method in kerogen-type classification, achieving an impressive accuracy rate of 93.54%. It is concluded that when utilized with petrophysical logs, machine learning methodologies offer a powerful, efficient, and cost-effective alternative for determining OI, HI, and kerogen type. The novelty of this approach lies in its ability to accurately predict these crucial parameters using readily available well-log data, potentially revolutionizing traditional geochemical analysis practices. Graphical abstract

Abstract Reservoir quality in carbonate reservoirs is significantly influenced by diagenetic processes. Although diagenesis is studied as a common reservoir quality damaging/enhancing process in many previous studies, literature is limited about the spatial modeling of diagenesis processes using advanced geostatistical algorithms. In the current study, 3D models of the main diagenetic processes which affect the reservoir quality of the Sarvak reservoir in an Iranian oilfield located in the north Dezful Embayment were constructed using geostatistics. According to the petrographic studies, a total of 10 microfacies were identified. In addition, the significant diagenetic processes in this reservoir include dolomitization, cementation, dissolution, and compaction. In this study, diagenetic electrofacies were determined using the “multi-resolution graph clustering” method based on the quantitative results of the petrographic studies. The results of spatial modeling and provided average maps were used to investigate the lateral variation of those properties and their relationship with effective porosity. It shows that trends of the secondary porosity and velocity deviation log (VDL) maps are generally correlatable with the effective porosity maps confirming the impact of dissolution as a main significant diagenetic process on reservoir quality enhancement. The most impact of the dissolution on porosity is observed in Lower Sarvak-E2 zone where the correlation coefficient is 0.75. The correlation coefficient between porosity and VDL in some zones is high indicating the effect of diagenesis on reservoir quality as it exceeds 0.61 in Lower Sarvak-A1 zone. In the occurrence of dolomitization, it has dual constructive and destructive effects on the reservoir quality. The most constructive and destructive effects of dolomitization were observed in Lower Sarvak-E1 and Lower Sarvak-F zones in which the correlation coefficients were 0.476 and − 0.456, respectively. In addition, low porosity zones are correlatable with developing cementation, stylolites, and solution seams.

Abstract Natural fractures play an essential role in the characterization and modeling of hydrocarbon reservoirs. Modeling fractured reservoirs requires an understanding of fracture characteristics. Fractured zones can be detected by using seismic data, petrophysical logs, well tests, drilling mud loss history and core description. In this study, the feed-forward neural networks (FFNN), cascade feed forward neural networks (CFFN) and random forests (RF) were used to determine fracture density from petrophysical logs. The model performance was assessed using statistical measures including the root mean squared error (RMSE), coefficient of determination (R 2), mean absolute error (MAE), Kling Gupta efficiency (KGE) and Willmott’s index (WI). Conventional good logs and full-bore micro-resistivity imaging data were available from three drilled wells of the Mozduran reservoir, Khangiran gas field. According to the findings of this research, the FFNN model showed a higher KGE and WI, and a higher correlation coefficient (R 2) compared to the CFNN model. The CFNN model outperformed the FFNN model with lower neurons. The models' performance was also improved by increasing the number of neurons in the hidden layers from 8 to 35. The findings of this study demonstrate that the measured and FFNN calculated fracture intensity is in excellent agreement with image log results showing a correlation coefficient of 92%. The RF algorithm showed higher stability and robustness in predicting fracture intensity with a correlation coefficient of 93%. The results of this study can successfully be used as an aid in a more successful reservoir dynamic modeling and production data analysis.

Abstract The Cenomanian Sarvak oil reservoirs are distributed over large areas of the Persian Gulf basin. The purpose of this study is analyzing the geochemical characteristics of the Sarvak oil reservoirs and their inter-relationships in the Persian Gulf, classification of the Sarvak oil samples and investigation of the possible causes of the genetic difference in oil families. In the previous studies, limited samples of Sarvak oil reservoir in scarce oilfields were studied individually and local interpretations are made accordingly. The current study employs a more complete set of geochemical from the Iranian part of Persian Gulf and regional interpretations are drawn. To achieve this goal, the geochemical data of 41 oil samples from 10 oilfields were collected and assessed based on gas chromatography (GC), gas chromatography–mass spectrometry (GC–MS), and stable carbon isotope analysis. It was demonstrated with the evaluations that the oils accumulated in the Upper Cretaceous Sarvak reservoirs in the Persian Gulf basin originate from different source rocks. The oil samples are genetically classified into four oil families based on the similarities and differences of parameters related to the depositional environment-dependent parameters using hierarchical cluster analysis (HCA), star diagram, and stable carbon isotope diagram. The source rocks were mainly deposited in anoxic marine carbonate environments. The thermal maturity of the oils was evaluated using steranes and trisnorhopanes. Oil families 2 and 4 (located at the center of the Persian Gulf) have the highest thermal maturity compared to the other samples; in contrast, oil family 1 (located at the west of the Persian Gulf) has the lowest thermal maturity. The possible source rocks of oil family 1 and family 3 (located at eastern Persian Gulf) using C28/C29 steranes are the upper Cretaceous successions; in contrast, the possible source rocks of oil families 2 and 4 are the upper Triassic and Lower Jurassic formations. Results of the study show a high similarity between the oils of families 2 and 4, which are located at the center of the Persian Gulf; these results also recognize the significant difference between these two oil families and oil families 1 and 3. The significant issue raised in this study is to find out the reason for this difference. The structural analysis of the central Persian Gulf shows that huge vertical faults created by salt domes intrusion provided the migration pathways for trapping oil in Sarvak reservoirs. The oils of the Surmeh, Fahliyan, and Dariyan reservoirs have migrated upwards through these faults to be trapped in the Sarvak Formation across the central Persian Gulf.

AbstractIn this paper, a method is proposed for the prediction of thermal maturity in the source rock using indirect methods. The applied data are well logs (neutron, density, resistance, and acoustic) in 13 wells and seismic data in six oil and gas fields in the central part of the Persian Gulf. Well-logs and seismic data are much more abundant than geochemical data and cover an extensive area in the oil and gas fields. These properties compensate for the lack of geochemical data that are scattered and limited to a few wells. This study is carried out in two steps. First, the amount of thermal maturity in the Kazhdumi Formation is calculated from well logs and is presented as an index in each well. Data obtained from organic thermal evaluation analyses are used to validate the results of thermal maturity prediction. These data include Rock-Eval pyrolysis in two wells. Then, seismic data are processed and studied in two-dimensional sections at the location of the target fields. In this step, seismic attributes are extracted from the seismic data using the multi-attribute regression analysis method, and thermal maturity is calculated using these attributes. Prediction is performed by probabilistic neural network analysis, and a seismic section is extracted indicating variations in thermal maturity in the Kazhdumi Formation.Keywords: Source rock, Thermal maturity, Well log, Seismic attribute Introduction The Kazhdumi Formation is an important source rock in the Persian Gulf basin, in the south of Iran (Bordenave Burwood, 1990). Thermal maturity of the Kazhdumi Formation is low in the central Persian Gulf compared to the eastern and western sectors (Rabbani 2008; Ghasemi-Nejad et al. 2009; Rezaie Kavanrudi et al. 2015; Rabbani et al. 2014; Baniasad et al. 2019). This formation is over-mature in proximity to the Hormoz Strait. The TOC content of this formation depends on the variety of depositional environments across the basin, increasing to the northwest with a maximum of 6 wt% in the proximity of the Hormoz strait (Rezaie Kavanrudi et al. 2015; Noori et al. 2016). The kerogen type is mainly ⅡS and type Ⅲ in different areas (Ghasemi-Nejad et al. 2009). The stratigraphic equivalents of the Kazhdumi Formation are the Burgan Formation in the west and south of the Persian Gulf (Kuwait), producing hydrocarbons from the second-largest hydrocarbon oilfield in the world, and the Nahr-Umr Formation in Qatar and Iraq. The Burgan and Nahr-Umr formations consist of fluvial sandstone in the south and east of the Persian Gulf compared to the shale-dominated volume in the north and central part (Ghasemi-Nejad et al. 2009; Noori et al. 2016).The source rock potential of this formation is largely unknown and there is a lack of published reports in the central part of the Persian Gulf. the aim of this study is to use log and seismic data for indirect estimation of thermal maturity in this area. Materials & MethodsIn this study, neutron, density and sonic as well as gamma-ray logs are used to predict thermal maturity. The maturity index (MI) by Zhao et al. (2007) was used to describe the level of thermal maturity based on well logs. The response of the neutron and density logs is affected by the fraction of water in a formation. The hydrocarbon density also decreases due to thermal maturation. The maturity index is calculated based on the equation from Zhao et al. (2007)(Eq. 1) N: the number of log readings or the number of samples.Øn9i: neutron porosity of rock samples with 9% density porosity or higher. The 9% is a cut-off for porosity in the calculations. Values lower than 9% are indicative of very dense formations such as anhydrite or dense dolomite known as non-source shales. The cumulative value of the calculated MI is presented as a maturity index in the Kazhdumi Formation in each well. In the second step, seismic data analysis, inversion of seismic data and log prediction are carried out. Inversion analysis starts with seismic data processing using well logs and 2D post-stack seismic data. An acoustic impedance log is created by the combination of density and sonic logs. Check-shot data are used for depth-to-time conversion resulting to the correlation of well and seismic data. Following this, an initial strata or impedance model is constructed. This model is produced using the seismic volume, available well data and defined horizons. Seismic data, the initial strata model as well as available wells are applied as the input data for inversion analysis, and then the inversion method is selected. Discussion of Results & ConclusionsGenerally, thermal maturity status in the Kazhdumi Formation is between immature to mature in the study area in the central part of the Persian Gulf. According to the presented data in this study, this formation is mainly immature in 2, 3, 5 and 7 fields. Field number 4 is early mature and number 1 is mature. Field 6 shows an immature to mature level indicating that the maturity varies in this field.In the next step, seismic attributes are selected by regression analysis for MI prediction. Attributes selection is a process for the extraction of seismic attributes from the raw seismic data for modeling the target log. Multi-attribute analysis is an automatic procedure for the selection of the most relevant seismic attributes to the target log.Finally, extracted attributes are used for the log prediction by Probabilistic Neural Network analysis (PNN). It is trained based on the selected attributes in the previous step and then predicts the target parameters.Acoustic impedance is recognized as the most important seismic attribute reflecting the geological properties (Chopra and Marfurt, 2005). The optimum number of attributes for MI prediction is four which shows the lowest prediction error, although the training error continuously decreases by adding more attributes. The validation plot of the target log is estimated by excluding the data step by step from the calculation. The reliability of the regression model is tested by comparing the prediction with the actual log values.Thereafter, the 2D seismic section is converted to an MI volume. The variation in MI is continuous laterally and vertically, therefore, can be tracked throughout the basin.A comparison of the computed MI with geochemical data indicated that this method is applicable for thermal maturity prediction in the study area. The increase in MI corresponds to an increase in the Tmax values, thus providing a good indicator of thermal maturity variation.The last point to consider is the significance of the selected seismic attributes and their relationship with the target parameter. Results indicated that acoustic impedance is the most important seismic attribute in MI prediction. Acoustic impedance contains information about the velocity and formation density which are both affected by the formation fluids (Broadhead et al. 2016; Atarita et al. 2017). It is inversely related to the organic matter content (Harris et al. 2019). The computed thermal maturity is inversely related to the neutron porosity and water saturation which are both controlling parameters of the acoustic impedance in a formation.Other attributes that have been used in the log prediction are time and amplitude-weighted frequency. These attributes are related to different geological properties of source/reservoir formations (Taner et al. 1994; Chen and Sidney 1997; Chopra and Marfurt 2005). Amplitude envelope (reflection strength) mainly represents acoustic impedance and is useful for identifying porosity, hydrocarbon and gas accumulation, sequence boundaries, and lithological/depositional environment variations (Chen and Sidney 1997; Hart 2002). Average frequency is defined as a signature of events and is useful for correlation, often reflecting oil and gas reservoirs by seismic attenuation (Taner et al. 1994). Amplitude-weighted frequency is a product of the amplitude envelope and the instantaneous frequency, providing a smooth estimation of instantaneous frequency by removing spikes and noises (Chen and Sidney 1997).To sum up, well logs and seismic attributes are successfully applied to predict thermal maturity in the Kazhdumi Formation in the central part of the Persian Gulf.The analysis shows that the Kazhdumi Formation is mainly immature to early mature in the central part of the Persian Gulf.Seismic data are spatially continuous which is an advantage in source rock evaluation, resulting in continuous predictions of thermal maturity in hydrocarbon fields.Using seismic data is also cost-effective and less time-consuming than geochemical testing.

With the emerge of Artificial Intelligence and Machin learning systems, the petroleum industry has witnessed a significant progress in its different disciplines to optimize decision making, time and costs. Despite the widespread application of using machine learning methods in the petroleum industry, a little attention has been devoted to build a framework to bring the main currents and researches on the topic. The current research is aimed at covering this gap through further analysis of complementary sources of bibliographic information, assessing 3163 bibliometric studies published in Web of Science (WOS) database. The descriptive statistics show that this field has an exponential growth in the last five years, such that more than 62 % of identified articles were published between 2018 and 2022. CHINA, IRAN and US are the pioneer countries with the highest number of publications on the application of artificial intelligence and machine learning in the upstream sector of the petroleum industry. The most influential journal in this field is ‘JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING’ (with 416 articles) (the current journal title is Geoenergy Science and Engineering) and the most productive author is SALAHELDIN ELKATATNY (with 54 articles) in WOS database. Also, the co-occurrence word analysis show that most of the artificial intelligence and machine learning applications in the upstream sector of the petroleum industry was the prediction and optimization in the field of ‘porosity’, ‘well logs’ and ‘permeability’. This paper contributes to the body of knowledge by providing a comprehensive overview of the application of artificial intelligence and machine learning in the upstream petroleum industry.

In southern Iraq, the Yamama Formation has been a primary carbonate resource since the Lower Cretaceous era. This study covers Siba Field, which is located in southeastern Iraq. This paper will be devoted to a YC unit of study. The most crucial step in reservoir management is petrophysical characterization. The primary goal of this research is to assess the reservoir features and lithology of the Yamama (YC) Formation in the Siba region. Accessible excellent logs include sonic, density, neutron, gamma-ray, SP, and resistivity readings. The Interactive Petrophysics (IP4.4) program examined and estimated petrophysical features such as clay volume, porosity, and water saturation. The optimum approach was the neutron density and clay volume calculation using the Gamma Ray Method (VclGR), it was 0.246 in SB-6 since they are not impacted by anything. The Archie method was chosen due to its suitability for limestone. The lithology and mineralogy of the formations were determined using M-N cross plots; the diagram revealed that the Formation was composed of limestone. The Archie parameter was determined using the Pickett plot and formation water resistivity from the Pickett plot and SP log where the results were similar in all wells (RW=0.016, m=2.08, n=2.3, a=1.1). In addition, the higher section of the formation has good reservoir qualities such as density is (2.368g/cc), porosity is (PHIE=0.117) in SB-6.

Abstract Carbonate reservoirs in Iran are the most important and main sources of oil and gas production. Hydrocarbon flow rates from carbonate reservoirs heavily rely on the development of natural fractures. This study focuses on the role of natural fractures on reservoir quality considering the Sarvak carbonate reservoir (southwest Iran) as the second most important oil reservoir of Iran. To identify small-scale fractures around the borehole walls, cores description and image logs were utilized. Due to high cost of coring, lack of orientation and low core recovery in fractured zones indirect methods based on image logs and conventional well logs were exploited for fracture characterizations. For this purpose, a complete set of well data including image logs (FMI), well-log-derived porosity and permeability data together with petrophysical logs were employed. Interpretation of image logs allows different types of natural structures such as open, vuggy, partly-filled and filled fractures, bedding planes and stylolites to be identified. Comparisons between the petrophysical-log and image-log interpretations provide useful relationships with reservoir characteristics. Neutron and sonic logs show a direct relationship with fracture porosity, while the density log displays an inverse correlation with fracture porosity. Neutron-density and neutron-sonic cross-plots show that the dominant lithology of the studied well is limestone with minor fractions of dolomite and shale. Total porosity ranges from 2 to 30%, while the contribution of secondary porosity, on average, is below 3%. Relationships between fracture-frequency diagrams and reservoir permeability show that high permeability zones are well correlated with the high frequencies of open and vuggy fractures and zones that have flowed oil and associated gas in production tests. Natural fracture characterization based on systematic and integrated interpretations of image logs and petrophysical data can play an important role in optimizing field development, resource recovery and production from the Sarvak reservoir. Once calibrated, this method also offers potential as an exploration tool.

Abstract Understanding the fracture patterns of hydrocarbon reservoirs is vital in the Zagros area of southwest of Iran as they are strongly affected by the collision of the Arabian and Iranian plates. It is essential to evaluate both primary and secondary (fracture) porosity and permeability to understand the fluid dynamics of the reservoirs. In this study, we adopted an integrated workflow to assess the influence of various fracture sets on the heterogeneous carbonate reservoir rocks of the Cenomanian–Santonian Bangestan group, including Ilam and upper Sarvak Formations. For this purpose, a combination of field data was used including seismic data, core data, open-hole well-logs, petrophysical interpretations, and reservoir dynamic data. FMI interpretation revealed that a substantial amount of secondary porosity exists in the Ilam and Sarvak Formations. The upper interval of Sarvak 1-2 (3491 m to 3510 m), Sarvak 1-3 (3530 m to 3550 m), and the base of Sarvak 2-1 are the most fractured intervals in the formation. The dominant stress regime in the study area is a combination of compressional and strike-slip system featuring reverse faults with a NW–SE orientation. From the depositional setting point of view, mid-ramp and inner-ramp show a higher concentration of fractures compared to open marine environment. Fracture permeability was modeled iteratively to establish a realistic match with production log data. The results indicate that secondary permeability has a significant influence on the productivity of wells in the study area.

Integrated analysis of oil traps, especially those related to facies changes, is far more complicated than reservoirs related to structural traps. The most useful way to identify these complex subsurface structures directly related to facies change is to use seismic attributes. The main goal of this study is to integrate analysis of traps and their relationship with sedimentary facies, and to achieve this goal, petrographic, petrophysical, and seismic studies were integrated. Based on petrographic studies, five petrofacies, including Micro-Conglomerate, Sandstone, Claystone/ Shale, Sandy Dolomudstone, and Sandy Dolomitic Ooid Grainstone/Hybrid, which were identified in connection with the supratidal, intertidal, lagoon and fluvial environments and the main factor controlling the reservoir quality is primary sedimentary texture. A total of 11 microfacies, including anhydrite (MF1), dolomudstone (MF2), packstone peloid/ooid (MF3), packstone oncoid/peloid (MF4), dolostone (MF5), grainstone peloid/ooid (MF6), grain stone oncoid (MF7). Microbialite (MF8), boundstone (MF9), bioclastic packstone (MF10), and mudstone (MF11) were introduced for the Mozduran formation. The facies’ matching of the Shourije Formation with seismic attributes shows that the energetic parts of the fluvial Channel are related to coarse-grained sandstone and conglomerate facies, and the fine-grained shale sediments have played the role of cap rock and caused the formation of stratigraphic traps along the fluvial Channel. The reef structures identified in the Mozduran Formation indicate two depositional environments: a carbonate shelf and a carbonate bank. Grainstone peloid/ooid, grain stone oncoid. Microbialite and boundstone facies are associated with the reef core, shoal, and lagoon and have good reservoir quality. Therefore, by using seismic attributes, it is possible to examine all types of subsurface structures and their relationship with the Stratigraphic Traps. The results showed that generalized spectral decomposition performed well for the geometric detection of buried channels and reef structures. At the same time, the instantaneous frequency indicates the unconformity boundary, and the variance attribute can effectively be employed for identifying fractures.

The saturation calculation in complex reservoirs remains a major challenge to the oil and gas industry. In simple formations, a tendency towards simple saturation models such as Archie or Simandoux for clean and shaly reservoirs respectively is always preferable. These models were found to be working effectively in homogeneous formations within which the porosity and permeability are linked in the light of a simple facies scheme. Where the rocks show some degrees of heterogeneity, the well-logs are usually affected by different factors. This adversely results in a compromised or averaged log profiles that may affect the saturation calculations. Four wells drilled across a shaly sand of high heterogeneity have been studied in the Perth Basin, Western Australia. The aim is to resolve the hydrocarbon saturation and explain the high productivity results, despite the high water saturation, obtained through a conducted formation well test across the interested reservoir zones. A new integration technique between a suite of conventional and advanced logging tools together with the capillary pressure measurements has been carried out to generate a high-resolution reservoir saturation profile, that is lithofacies dependent. Three different independent methods were used in the studied wells to calculate the saturation and to reduce the uncertainty of the final estimated profiles. The methods are the resistivity-based saturation, the NMR-based irreducible saturation, and a new application through saturation height modeling. Furthermore, through the workflow, an effective calibration for the magnetic resonance T2 cutoff has been applied that is supported by the excellent reservoir production behavior from such complex reservoir. The methodology will help resolve the saturation calculation as one of the most challenging reservoir parameters, particularly where the resistivity logs are affected in complicated shaly sand environments. The effectiveness of the workflow shines the possibility to predict high resolution facies and saturation profiles in the lack of resistivity logs. A further possibility can complete the analysis on real time basis, which can certainly provide facies and saturation profiles extended to the uncored wells. Application of this methodology in the uncored wells has shown very encouraging results in various well trajectories, either vertical, deviated or horizontal long boreholes.

Logarithmic Mean of Transverse relaxation time (T2LM) and total porosity of the Combinable Magnetic Resonance tool (TCMR) are the main parameters of the Nuclear Magnetic Resonance (NMR) log which provide very substantial information for reservoir evaluation and characterization. Reservoir properties, for example, porosity and permeability, free and bound fluid volumes, and clay-bound water, could be calculated through the interpretation of T2LM and TCMR. In this manuscript, an intelligent approach has been used by us to predict NMR log parameters and their corresponding electrofacies from well log data. We define NMR electrofacies as classes of NMR log parameters representing reservoir quality are defined by us. For this purpose, NMR logs and petrophysical data are available for two different formations situated in the Ahvaz field. Data from Ilam formation were applied in order to construct the intelligent models, the same as Asmari formation, data were applied for reliability evaluation of the created models. The outcome results reveal higher performance levels of the Neural Network (NN) technique compared to the neuro-fuzzy (NF) model. The synthetically generated T2LM and TCMR logs are then calculated for the four logged wells from the Ahvaz oilfield using a mathematical function, and they are named Virtual Nuclear Magnetic Resonance (VNMR) logs. Finally, VNMR logs were classified into a set of reservoir electrofacies by cluster analysis approach. Correlations between the VNMR electrofacies and reservoir quality based on porosity and permeability data helped evaluate the reservoir quality quickly, cost-effectively, and accurately.

In particular, quantitative laboratory measurements are challenging to perform due to their costs and time consumption. So, the need to explore other available data interconnectivity to permeability is of great importance. One of these data is NMR (Nuclear Magnetic Resonance) log data which have been used frequently in recent studies. It is considered to segregate different groups, which can be obtained through cluster analysis. Using reliable parameters in the cluster analysis helps to segregate different rock units which can be used in the permeability models. To select reliable parameters, cross plots of the permeability versus extracted features from the NMR T2 distribution curve were plotted. Results indicate that TCMR, peak reading amplitudes, and T2Lm are the best permeability indicators, respectively. Cluster analysis was performed on T2LM, TCMR and Peak reading amplitudes, which showed the highest consistency with core derived data compared with other parameters. The crucial step is to determine the best estimate of the number of clusters. It is usually taken as a prior in most clustering algorithms. In this research, Davies-Bouldin criterion values versus the number of clusters were used to obtain the optimal number of clusters. The knee method, which finds the “knee” in many clusters vs. clustering evaluation graph, was used. A clustering model with the number of clusters from 2-100 was created. It showed the five is an optimal number of clusters. Subsequently, the Schlumberger-Doll-Research (SDR) coefficients for each cluster were modified using a curve fitting tool in the Matlab software. Results indicated that calculated permeability using cluster analysis showed a higher correlation by core derived permeability than the original SDR permeability model. Since this is the core part of the group attempt to use extracted T2 distribution features in permeability estimation in carbonate reservoirs, more investigation is required to attempt satisfactory results to standardize the value of the coefficient of the permeability models in carbonate rocks with different petrophysical properties.

Permeability is arguably the most critical property for evaluating flow in the reservoir. It is also one of the challenging parameters which must be measured in the field. Nuclear Magnetic Resonance (NMR) logging across the borehole is among the popular techniques, which it is utilized to determine permeability across the reservoir. However, available correlations in literature for estimating permeability from NMR data do not usually provide acceptable accuracy in the carbonate rocks. Therefore, a new model is proposed to estimate permeability by establishing a relationship between core derived permeability and extracted features from the T2 distribution curve of NMR data with the ensemble LSBoost algorithm. The feature extraction process is performed using peak analysis on T2 distribution curves which it leads to 5 relevant parameters, including T2lm, TCMR, prominence, peak amplitude and width. The proposed model is validated by comparing the proposed method’s correlation coefficient against Timur-Coates and SDR equation estimation accuracy. The results show that our model generally provides better prediction accuracies in comparison with the empirical equation-based derived permeabilities.

Permeability is arguably the most important property in evaluating fluid flow in the reservoir. It is also one of the most difficult parameters to measure in field. One of the main techniques for determining permeability is the application of Nuclear Magnetic Resonance (NMR) logging across the borehole. However, available correlations in literature for estimating permeability from NMR data do not usually give acceptable accuracy in carbonate rocks. In this research, two new empirical models are introduced for quantifying NMR extracted permeability in carbonate formations. These models are validated for three carbonate formations, namely, Yamama, Gadvan, and Daryan in one of Iranian offshore reservoirs in the Persian Gulf. The first empirical model applies the pore-related NMR data such as free and bound fluid parameters. The second model, however, is a novel approach that uses the geometric features of the occurring humps in T2 distribution. For assessing the performance of the proposed models, statistical parameters as well as graphical tools are utilized. It is found that the for the examined case studies, geometric approach gives more accurate and reliable estimates compared to the available models in the literature including Timur-Coates and SDR methods.

Abstract The Zakeen Formation is one of the possible gas reservoirs in the Zagros sedimentary basin. In this paper, the cyclostratigraphic study of the upper part of the Zakeen Formation was performed by using evolutionary spectral analysis methods. The Multi-Tapper Method (MTM) periodogram is one of the evolutionary spectral analysis methods used in this study to identify and prove the frequency ratios of Milankovitch cycles in the studied sediment sequences. In this study, using the introduced methodology, the frequency ratios of the Milankovitch cycles in the upper part of the Zakeen Formation was identified for the first time, and their existence was confirmed. Besides, the spectral powers of spectral gamma-ray (SGR) and density (RHOB) series, in evolutionary Fast Fourier Transform (FFT) and Wavelet Transform (WT) Scalograms, in 2SK-1 and 2SKD-1 wells of Salman gas field show strong astronomical signals of the Milankovitch cycles (E, e, O, P). After investigating the cyclostratigraphy of the upper part of the Zakeen Formation in the studied wells, by using new methods of evolutionary correlation coefficient and evolutionary significance levels, comparison of sediment accumulation rate variations of the Zakeen Formation was performed in the studied wells. Keywords: Cyclostratigraphy, Sediment accumulation rate, Milankovitch cycles, Zakeen Formation, Salman gas field Introduction The Zakeen Formation is a clastic succession and in its type section at Faraghan Mountains, north Bandar-Abbas, has 285 m thickness. It is composed of sandstones, shales, and with several dolostones horizons (Ghavidel-Syooki 1999). The Zakeen Formation, based on palynological studies by Ghavidel-Syooki (1999 & 2003), is attributed to the early to late Devonian (Lochkuvian to Frasnian). In this study, only the Frasnian sequence of the upper part of the Zakeen Formation in 2SK-1 and 2SKD-1 wells of the Salman gas field was studied. Cyclostratigraphic studies were also focused primarily on proving the existence of Milankovitch cycles in sedimentary sequences, determining timelines, calculating sediment accumulation rates and stratigraphic classifications (Zheng & Luo 2004; Li et al. 2018a; Chen et al. 2019). To compare the sediment accumulation rates of the upper part of the Zakeen Formation in the studied wells, cyclostratigraphy study was first performed to show the existence of the Milankovitch cycles and the way they affect the upper part of the Zakeen Formation. The spectral analysis methods together with density and spectral gamma-ray logs data were used to study the cyclostratigraphy of the studied formation. Milankovitch (Serbian scientist) has calculated the duration of the three main parameters of the Earth's orbit around the sun that affect global climate change, known as eccentricity, obliquity, and precession, and these are referred to Milankovitch cycles (Milankovitch 1941; Fletcher 2013). Cyclostratigraphy of the Devonian system in the different parts of Earth including southern China (Gong et al. 2001; Chen & Tucker 2003; De Vleeschouwer et al. 2017), France, Morocco (Ellwood et al. 2011), Canada (De Vleeschouwer et al. 2012; De Vleeschouwer et al. 2017), Poland (De Vleeschouwer et al. 2013, 2017), Belgium (De Vleeschouwer et al. 2014, 2017), USA (De Vleeschouwer et al. 2017; Pas et al. 2018) and varying frequencies of Milankovitch cycles from the deposits of these areas have been reported. The purpose of this study is to analyze cyclostratigraphy and compare the sediment accumulation rates of the upper part of the Zakeen Formation in 2SK-1 and 2SKD-1 wells of the Salman gas field. Material & Methods In this paper, by using evolutionary spectral analysis techniques (FFT spectrogram and WT scalogram) and MTM periodogram in a series of digital signal processing methods in Acycle v2.0 software (Li et al. 2018a), time series were extracted from SGR and RHOB logs. Afterward, their spectral powers were used in the cyclostratigraphic analysis of the upper part of the Zakeen Formation. Milankovitch cycles recorded in the upper part of Zakeen Formation were identified using a set of evolutionary spectral analysis methods for the desired logs including MTM and subsequently analyzed in the FFT Spectrogram (Kodama and Hinnov 2015). The influence of such cycles on sedimentation of the upper part of the Zakeen Formation and their abundance in this formation at different depths from the two studied wells were studied. The WT scalogram is then used to compare statistically significant zones between time series (Grinsted et al. 2004). The frequency ratio method (Mayer & Appel 1999) was used to establish the link between the cycles identified in the sedimentary signals and the theoretical astronomical parameters. After identifying the Milankovitch cycles in MTM periodograms, they were then extracted from the SGR logs by filtering the cycles of 405 kyr (E), 100 kyr (e), 34.4 kyr (O) and 21.3 kyr (P). Subsequently, confidence levels with median, 90, 95, 99% levels were plotted for each MTM periodogram and almost all cycles were sampled from the median confidence level and some above 90%. Through performing MTM periodograms, Milankovitch cycles were detected. Also, by using FFT spectrograms (reviewed by Hinnov), their influence on deposition of the upper part of Zakeen Formation and their abundance were labeled. Through the extraction of WT scalograms, high statistical significance zones were compared (correlation against E, e, O, and P cycle filters) to find the main lithological changes, probabilistically. Finally, cyclostratigraphic results in two studied wells were investigated. Moreover, sediment accumulation rates of the upper part of the Zakeen Formation in two studied wells (2SK-1 and 2SKD-1) were estimated. Through applying evolutionary correlation coefficients and evolutionary significance levels of spectral analysis methods for the SGR logs in both wells, estimates of sediment accumulation rates of the upper part of Zakeen Formation was precisely carried out. Discussion of Results & Conclusions Through plotted MTM periodograms for each well logs, the cycles ratio were identified in well 2SK-1 based on SGR MTM periodogram as 18.03, 5.63, 3.6, 2.39, 2.08, 1.58, 1.2, while with refer to RHOB MTM periodogram they are 20.8, 7.11, 4.0, 2.33, 2.0, 1.48, 1.33. This is close to the ratio of 20:5:2:1 cycles (from left to right we have the component ratio of the cycles E, e, O, and P). In contrast, in the MTM periodogram plotted for the SGR log, the ratios are 18.24, 6.08, 3.78, 2.87, 2.38, 1.55, while in MTM periodogram plotted for the RHOB log, ratios of 19.38, 6.45, 4.43, 3.6, 2.62, 2.16, 1.83, 1.49 were obtained. All ratios in two wells, being approximately equal to 20:5:2:1 and thus the relative ratios, each based on their value, are interpreted as one of the Milankovitch cycles (E or e or O or P). In this study, by evolutionary spectral analysis methods including MTM periodograms, FFT spectrograms, and WT scalograms, cyclostratigraphy study of the upper part of the Zakeen Formation were performed in 2SK-1 and 2SKD-1 wells of Salman gas field. Evaluation of sediment accumulation rates of the upper part of Zakeen Formation in these two wells was performed with evolutionary correlation coefficients and evolutionary significance levels. This allows them to be observed in the stratigraphic domain alongside the well log data. Based on FFT spectrograms in 2SK-1 well, three cycles including E, e and O have more effect on the sedimentation rate of the upper part of the Zakeen Formation. In 2SKD-1 well such an effect is due to four cycles comprising of E, e, O and P. Subsequent analyses by the evolutionary correlation coefficient spectrogram plotted for the SGR log in 2SK-1 well revealed that only the E cycle affected the deposition of the upper part of the Zakeen Formation. The evolutionary correlation coefficient plotted for SGR log in 2SKD-1 well also confirmed the effect of E and O cycles on the deposition of Zakeen Formation. Other cycles with low impact on cyclic sedimentation of the upper part of the Zakeen Formation have been involved in these two wells. Sediment accumulation rates of the upper part of Zakeen Formation were estimated by time columns from cyclostratigraphy and thickness of the upper part of the Zakeen Formation for both wells by approximately 0.8 cm per thousand years. Subsequently, through the analysis of evolutionary correlation coefficients plotted for SGR logs, it was determined that sediment accumulation rates of the upper part of the Zakeen Formation in 2SK-1 wells show ascending, constant and descending trends. In contrast, in the 2SKD-1 well, sediment accumulation rates have been almost constant. In general, the sediment accumulation rates of the upper part of Zakeen Formation in 2SK-1 well are estimated more compared to 2SKD-1 well. Also, large variations in sediment accumulation rates of the upper part of the Zakeen Formation were observed in the 2SK-1 well compared to the 2SKD-1 well, indicating the occurrence of hidden effective processes for these changes.

Since more than half of the crude oil is deposited in naturally fractured reservoirs, more research has been focused on characterizing and understanding the fracture impact on their production performance. Naturally open fractures are interpreted from Fullbore Formation Micro-Imaging (FMI) logs. According to the fracture aperture, they are classified as major, medium, minor and hairy fractures in decreasing order of their respective aperture size. Different fracture types were set up in this work as a Discrete Fracture Network (DFN) in synthetic models and a sector model from a highly naturally fractured carbonate reservoir. The field sector model includes four wells containing image logs from two wells and production data from two other wells. Numerous simulations were conducted to capture the contribution of fracture type on production performance. Primary recovery was used for synthetic and field sector models, while waterflooding and gas injection scenarios were considered just for the synthetic models. The results showed that the fracture type and its extent play an essential role in production for all studied models. The reservoir production capabilities might be underestimated by ignoring any fracture types present in the reservoir, especially the major ones. In the secondary recovery, fractures had different impacts. Better displacement and higher recovery were promoted for waterflooding, whereas faster breakthrough times were observed for the gas injection. The performance during gas injection was more dependent on fracture permeability changes than waterflooding. This study’s findings can help in better understanding the impact of the different types of fracture networks on oil recovery at the various production stages. Additionally, the history matching process can be improved by including all types of fractures in the dynamic model. Any simplification of the fracture types might end in overestimating or underestimating the oil recovery.

Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared from core plugs and cuttings. In the second step of rock typing study, electrofacies are determined based on the classification of well log responses using an appropriate clustering algorithm. The well logs used for electrofacies determination include porosity logs (NPHI, DT, and RHOB), lithodensity log (PEF), and gamma ray log. The third step deals with flow unit determination and pore size distribution analysis. To this end, flow zone indicator (FZI) is calculated from available core analysis data. Through the application of appropriate cutoffs to FZI values, reservoir rock types are classified for the studying interval. In the last step, representative capillary pressure and relative permeability curves are assigned to the reservoir rock types (RRT) based upon a detailed analysis of available laboratory data. Through the analysis of drill stem test (DST) and GDT (gas down to) and ODT (oil down to) data, necessary adjustments are made on the generated PC curves so that they are representative of reservoir conditions. Via the estimation of permeability by using a suitable method, RRT log is generated throughout the logged interval. Finally, by making a link between RRT’s and an appropriate set of seismic attributes, a cube of reservoir rock types is generated in time or depth domain. The current paper reviews different reservoir rock typing approaches from geology to seismic and dynamic and proposes an integrated rock typing workflow for worldwide carbonate reservoirs.

The Permo-Triassic Dalan-Kangan and upper Jurassic Arab formations are important carbonate-evaporate reservoirs resulted from depositional and diagenetic heterogeneities. The objectives of the study are comparison precursor mineralogy, identification of dominant pore types and clarify the role of diagenetic processes on petrophysical characteristics and reservoir quality of the Dalan-Kangan and Arab formations in the Persian Gulf. To achieve this goal core description and petrographic study and core poroperm data from two hydrocarbon fields in the Persian Gulf were integrated. Pore system studies show that different pore types contribute to the fluid flow and production within the Dalan-Kangan and Arab formations. These pore types are inter-granular, intercrystalline, touch and non-touch vugs and microspores. The initial mineralogy of the components had a significant effect on pores system distribution and trend of the diagenesis of the two studied intervals. During the Permo-Triassic and upper Jurassic, the composition of ocean waters has been suitable for deposition of non-skeletal components with aragonite and calcite mineralogy, respectively. Thus, moldic pores in the Dalan and Kangan reservoirs and intercrystalline and inter-granular pores in the Arab Formation occur as the dominant pore types. In Dalan and Kangan formations, micropores are more frequent than the Arab Formation, which is attributed to the dominance of mud-dominated facies during the Permo-Triassic periods. Detailed diagenetic studies indicate that the studied intervals were exposed to dissolution, cementation, anhydrite mineralization, dolomitization, physical and chemical compaction and fracturing after deposition. Dissolution, anhydrite mineralization and cementation seem to have exerted the main effect on pore system properties

Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field.

In the this research, the hydrocarbon generation potential of the Gadvan Formation as a probable source rock was investigated in the central part of the Persian Gulf at the borders of Iran. Type and maturity level of kerogen were investigated in six wells using the results of Rock-Eval pyrolysis and compared with results yielded by the modelling software program known as Pars Basin Modeler (PBM). The cross-plot of hydrogen index (HI) versus maximum temperature suggests that the Gadvan Formation reached early to mid-maturity stages in the studied area, which means that it could act as a gas prone source rock. Furthermore, the burial and thermal history of the Gadvan Formation was determined in one well. Two methods, Easy %Ro and time-temperature index (TTI) were used for the reconstruction of thermal modelling and studying the thermal maturity level in all of the drilled wells reaching the Gadvan Formation. The results of the TTI and Easy %Ro methods were in good agreement and both of confirmed the results of Rock Eval analysis. An integrated approach using different techniques showed that the Gadvan Formation can be classified as a poor gas bearing source rock in the studied area, while its maturity increases towards the southern parts of the Persian Gulf.

سرشت­نمایی مخزن و تشخیص فاکتورهای اصلی کنترل­کننده کیفیت مخزنی سهم مهمی در ارائه یک تصویر واقعی از ویژگی‏های مخزن دارد. در این مطالعه با بررسی مجموعه ­ ای از داده ­ های نمودارهای چاه‏پیمایی مخزن و خوشه‏بندی آنها براساس اصول و روش‏های ریاضی و آماری، رخساره‏های الکتریکی مخزن برای ماسه‏های سفت سازند ویلسپی در پنج چاه مربوط به میدان وپچررنج واقع در حوضه پرت در استرالیای غربی شناسایی شدند. این خوشه‏ها یا دسته‏های نمودارهای چاه‏پیمایی که دارای ویژگی‏های درون دسته­ای مشابه و برون دسته­ای متفاوت از بقیه دسته­ها هستند در واقع انعکاسی از ویژگی‏های زمین­شناسی (بافتی و دیاژنزی) و پتروفیزیکی مخزن هستند. در این مطالعه رخساره‏های الکتریکی­ مخزن با استفاده از خوشه‏بندی نمودار‏های چاه‏پیماییی چاه و داده­های تخلخل و تراوایی به دو روش رسوبی و پتروفیزیکی تعیین شدند. در روش رسوبی سه رخساره الکتریکی شناسایی شده منطبق بر ماسه­های تمیز، ماسه­های شیلی و توالی‏های شیلی هستند. این رخساره‏های الکتریکی عمدتاً در ارتباط با ویژگی‏های سنگ‏شناسی و بافت رسوبی بوده و بیانگر نا همگونی بالای مخزن مورد مطالعه هستند. در روش دوم چهار رخساره الکتریکی در ارتباط با ویژگی‏های پتروفیزیکی توالی­های ماسه­ای درون مخزن به دست آمد. به منظور برقراری یک رابطه مناسب بین رخساره‏های الکتریکی و زون‏های تولید درون توالی‏های ماسه­ای پنج واحد جریانی هیدرولیکی مشخص شدند. نتایج مطالعه نشان می­دهد که واحدهای جریانی با کیفیت مخزنی پایین ( A ، B و تا حدی C ) عمدتاً مرتبط با رخساره­های ­دانه­ریز ته نشسن­شده در محیط­های رسوبی کم انرژی هستند. در مقابل واحدهای جریانی با کیفیت مخزنی بالا ( D و E ) در ارتباط با رخساره‏های دانه متوسط تا درشت و خیلی درشت بوده و ویژگی‏های پتروفیزیکی متفاوتی دارند اگرچه تمامی آنها دارای تراوایی پایین بوده و به صورت سفت در نظر گرفته می­شوند. از این رو سه نوع ماسه سنگ مخزن براساس ویژگی‏های پتروفیزیکی و شدت فرآیندهای دیاژنزی در آنها به ترتیب در سه تیپ1 یا ماسه­های خیلی­ سفت، تیپ2 یا ماسه های سفت و تیپ3 یا ماسه­های نیمه­سفت تقسیم­بندی می­شوند. در یک مقایسه، ماسه­های تیپ3 با داشتن تخلخل و تراوایی بالاتر دارای بهترین کیفیت ­مخزنی هستند در صورتیکه ماسه­های با کیفیت مخزنی پایین تیپ1 به شدت تحت تأثیر فرآیندهای دیاژنزی (سیمانی­شدن توسط کانی‏های رسی و سیلیس) قرار گرفته­اند. نتایج این مطالعه بیانگر انطباق خوبی بین گونه­های سنگی مشتق­شده از مغزه و رخساره­های لاگ است. با استفاده از روش مورد استفاده در این مطالعه امکان پیگیری واحدهای جریانی مخزن از روی پاسخ‏های نمودارهای چاه‌پیمایی وجود دارد.

Flow unit characterization plays an important role in heterogeneity analysis and reservoir simulation studies. Usually, a correct description of the lateral variations of reservoir is associated with uncertainties. From this point of view, the well data alone does not cover reservoir properties. Because of large well distances, it is difficult to build the model of a heterogenic reservoir, but 3D seismic data provides regular sampling that can improve reservoir spatial description. In this study, seismic attribute analysis was used to predict flow zone indicator (FZI) values of a carbonate reservoir by using seismic and well log data. First, a 3D acoustic impedance volume was created as an external attribute for seismic data analysis. To improve the ability of FZI prediction, the maximum number of attributes from multiattribute analysis was computed by using a step-wise regression technique. To verify the results of multiattribute technique, the cross plot analysis of multiattribute method was performed. It was found that the R2 value of the correlation between the predicted and actual FZI is as high as 0.859 with an average error value of 2.34 µm. The analysis of the results of multiattribute technique showed that it was an effective technique for FZI prediction in hydrocarbon reservoirs. Such accuracy in building a 3D distribution of FZI provides a good insight into reservoir production zones. The results clearly indicate that the methodology proposed herein can successfully be used to specify the locations of new wells for the purpose of future production or injection plans.

Cementing in high-pressure-high-temperature (HPHT) wells presents challenges due to gas penetration into the cement, leading to low bonding quality and well isolation failures. This study aimed to design a novel multi-target cement formula to deter gas penetration and prevent complications such as formation creep and cement deterioration. A cement slurry was developed for two liner intervals in the first exploratory well in the Minoo field, SW Iran, at a depth of over 6000 m. The thermal gradient was 1.62 °F/100 ft, significantly impacting cement performance; elevated temperatures can accelerate hydration reactions, potentially causing strength retrogression. A blend of additives—including 3 % boric acid as a dual-functioning dispersant and 1 % latex materials—enhanced dispersing properties, compressive strength, and gas migration resistance. The designed slurry achieved an API fluid loss of 4.5 cc and 15 cc, transition times (30BC to 70BC) under 1 minute, with compressive strengths of 25.29 MPa and 35.44 MPa, respectively, compared to ordinary slurries that typically exhibited fluid losses greater than 50 cc and lower strengths. Gas migration resistance was analyzed through fluid migration tests, showing a 50 % reduction in permeability with silica nanoparticles. CBL/VDL logging data confirmed effective bonding and isolation in the wellbore. The innovative formulation enhances the mechanical properties of the cement—improving its durability and ensuring long-term stability in extreme conditions—while also addressing gas migration challenges. This study advances cement technology, enhancing the performance and reliability of cement-based materials in extreme environments through innovative methods, including using boric acid and latex materials in HPHT cement compositions.

This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and hydraulic flow units (HFUs). Neural networks have been used for the last several years to estimate reservoir properties. However, their application for hydraulic flow unit estimation on a cube of seismic data is an interesting topic for research. The methodology for this application is illustrated using 3D seismic attributes and petrophysical and core data from 6 wells from the Kangan and Dalan gas reservoirs in the Persian Gulf basin. The methodology introduced in this study estimates HFUs from a large volume of 3D seismic data. This may increase exploration success rates and reduce costs through the application of more reliable output results in hydrocarbon exploration programs. 4 seismic attributes, including acoustic impedance, dominant fre- quency, amplitude weighted phase and instantaneous phase, are considered as the optimal inputs for pre- dicting HFUs from seismic data. The proposed technique is successfully tested in a carbonate sequence of Permian-Triassic rocks from the studied area. The results of this study demonstrate that there is a good agreement between the core and PNN-derived flow units. The PNN used in this study is successful in modeling flow units from 3D seismic data for which no core data or well log data are available. Resumen Este estudio presenta un modelo inteligente basado en redes neuronales probabilísticas (PNN) para pro- ducir una formulación cuantitativa entre atributos sísmicos y unidades de flujo hidráulico (HFU). Las redes neuronales han sido utilizadas durante los últimos años para estimar las propiedades de reserva. Sin embargo, su aplicación para estimación de unidades de flujo hidráulico en un cubo de datos sísmicos es un tema importante de investigación. La metodología para esta aplicación está ilustrada a partir de datos tridimensionales y datos petrofísicos y de núcleo tomados en 6 pozos de las reservas de Kangan y Dalan, de la cuenca del Golfo Pérsico. La metodología introducida en este estudio estima las HFU de un gran volúmen de datos sísmicos tridimensionales. Esto podría incrementar los índices positivos de explora- ción y reducir los costos a través de una aplicación más confiable en resultados de producción para los programas de exploración en hidrocarbonos. Cuatro atributos sísmicos, obstrucción acústica, frecuencia dominante, fase de amplitud media y fase instantánea, son considerados en este trabajo como aportes claves para predecir los datos sísmicos de las HFU. La técnica propuesta ha sido evaluada exitosamente en una secuencia carbonada de rocas del Pérmicotriásico tomadas del área de estudio. Los resultados de este trabajo demuestran que hay concordancia entre la base de las PNN y las unidades derivadas de flujo. Las PNN utilizadas en este estudio son capaces de modelar unidades de flujo de datos sísmicos tridimen- sionales para los cuales no hay un centro de datos o una secuencia de datos disponible.