Reservoir Rock Types Research Articles

Tectonic–Hydrothermal Processes and their Relationship with the Petroleum Potential of the Bazhenov-Abalak Complex of Western Siberia

Abstract —The main types of reservoir rocks have been identified within the Bazhenov-Abalak complex. To assess the geological resources of hydrocarbons, it is proposed to formally divide the entire set of lithological rock types comprising the Bazhenov-Abalak complex into two main varieties: fluid seals and reservoirs. We argue that it is possibile to distinguish between potentially productive and productive rocks, represented by siliceous and carbonate varieties, according to logging data. A possible mechanism for the formation of reservoir rocks within the Bazhenov-Abalak complex as a result of the tectonic-hydrothermal impact on these deposits has been reconstructed. An original methodology for identifying perspective zones of various categories of hydrocarbon accumulation in the Bazhenov-Abalak complex through integrating seismic data and tectonophysical modeling carried out on their basis has been proposed. For example, an assessment of the predicted geological resources of hydrocarbons contained in the Bazhenov-Abalak complex within the limits of 3D seismic exploration of the Yem-Yegovskaya area has been carried out. The necessity of assessing the prospects for oil content and calculating the predicted geological resources of hydrocarbons in the whole Bazhenov-Abalak complex, and not only in the Bazhenov Formation, is substantiated, based on a single mechanism of formation of reservoir rocks of tectonic-hydrothermal origin in them.

Advanced Technique of Rock Typing Characterization of Mishrif Formation, Amara Oil Field in Southern Iraq

Reservoir rock typing integrates geological, petrophysical, seismic, and reservoir data to identify zones with similar storage and flow capacities. Therefore, three different methods to determine the type of reservoir rocks in the Mushrif Formation of the Amara oil field. The first method represents cluster analysis, a statistical method that classifies data points based on effective porosity, clay volume, and sonic transient time from well logs or core samples. The second method is the electrical rock type, which classifies reservoir rocks based on electrical resistivity. The permeability of rock types varies due to differences in pore geometry, mineral composition, and fluid saturation. Resistivity data are usually obtained from well logs, and resistivity logs are available. The third method is the storage capacity of rocks. The focus is on the ability of rocks to store liquids, especially hydrocarbons. This method analyzes porosity, permeability, and pore size distribution data. After that, we compared the previous three methods to identify the types of rocks and determine the best method. In the first method (Cluster Analysis), three types of rocks were identified (Bad, Moderate, and Good). In the second method, electrical rock type (ERT), four types of rocks were identified (Bad, Moderate, Good, and Very good). Then, the third method (Storage Capacity) came and enhanced the results of the second method, so the second method is considered the best and most accurate method determining the types of rocks.

Linking petrophysical heterogeneity and reservoir rock-typing of the post-rift shallow marine siliciclastics to their depositional setting: The Upper Cretaceous Bahariya reservoirs, north Western Desert, Egypt

The upper Cretaceous clastic facies of the Bahariya Formation host the main reservoir intervals in the north Western Desert (NWD) of Egypt. These clastics were deposited in a parallic depositional environment characterized by different scales of reservoir heterogeneities. However, the link between these heterogeneities and the depositional setting of the reservoir facies is still blurred. In this study, we investigate a highly-heterogeneous reservoir facies of Bahariya Formation in Yasser Field wells, NWD in order to construct a predictive framework for the distribution of the best reservoir rock types (RRTs) and flow zones. Seismic stratigraphy was integrated with core sedimentology to understand the depositional architecture of the reservoir facies. Moreover, conventional core analysis and wireline logs were interpreted to evaluate the scales of lithological and petrophysical heterogeneities in the different RRTs.In the studied Yasser Field, the Bahariya Formation consists of tidal facies deposited in a wide range of tidally-influenced conditions. The seismic facies varies greatly with variation in the tidal regime. Best reservoir rock types RRTs are associated with tidal channels and amalgamated tidal bars (RRTI). RRTI rocks represent the main fluid flow conduits in the studied Bahariya reservoir. Lithological and petrophysical heterogeneities are more prominent in the tidal mud flat and mixed tidal flat facies which form RRTIII rocks. Linking seismic with sedimentary facies enabled us to predict the distribution of the best reservoir flow zones in the study region. The present results establish a framework for predicting the optimum reservoir quality facies based on integrating seismic, sedimentary and petrofacies. This framework could be applied in analogous tidally-influenced reservoir facies with high depositionally-controlled pore system heterogeneity.

Development of a new hydraulic electric index for rock typing in carbonate reservoirs

Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R2) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.

Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks

The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.

Correlating Capillary Pressure and Resistivity Index for Carbonate Reservoir

Capillary pressure is a significant parameter in characterizing and modeling petroleum reservoirs. However, costly laboratory measurements may not be sufficiently available in some cases. The problem amplifies for carbonate reservoirs because relatively enormous capillary pressure curves are required for reservoir study due to heterogeneity. In this work, the laboratory measurements of capillary pressure and formation resistivity index were correlated as both parameters are functions of saturation. Forty-one core samples from an Iraqi carbonate reservoir were used to develop the correlation according to the hydraulic flow units concept. Flow zone indicator (FZI) and Pore Geometry and Structure (PGS) approaches were used to identify the reservoir hydraulic flow units. The experimentally derived correlations can be used to predict capillary pressure from resistivity, which is widely available from well-logs. FZI and PGS rock typing methods were applied to characterize the reservoir rock types. For both methods, the log-log plot of Leverett J-function and capillary pressure versus resistivity index for each rock type represent a power-law model relationship between these parameters. Despite the good permeability-porosity prediction results, the FZI approach did not yield a good correlation between J and I. PGS resulted in a better performance in terms of both permeability-porosity prediction and Pc with I correlation because PGS honors the pore geometry and structure relationship with the mean hydraulic radius more than FZI. This work introduces a new correlating approach that aims to assist in reservoir characterization and simulation.

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs.

To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. K-means cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.

Carbonate reservoir characterization and permeability modeling using Machine Learning ـــ a study from Ras Fanar field, Gulf of Suez, Egypt

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.

Integrating μCT imaging of core plugs and transfer learning for automated reservoir rock characterization and tomofacies identification

Traditional methods of core characterization are time-consuming and require the considerable effort of a large number of experts. At the same time, it is quite difficult to readily detect small but important elements in reservoir rocks, especially inside the core volume, that can have a strong impact on assessing reservoir potential and hydrodynamic properties. This research explores the integration of high-resolution micro-computed tomography (μCT) imaging and transfer learning for the automated characterization of reservoir rocks and tomofacies identification. The study involved collecting and marking a dataset containing 66,560 μCT images of 130 standard core plugs containing all major reservoir rock types. Using this dataset of 2D μCT images of core plugs, we applied convolutional neural networks (CNNs) pre-trained on ImageNet and fine-tuned them for rock classification tasks. Applying a transfer learning approach, we trained seven ResNet-50 models, which were able to identify and classify reservoir rock features in μCT images. These features included basic characteristics such as rock type, general texture, the presence of fractures and sulfidization that are common for all reservoir rocks. Further, depending on a specific rock type, individual features such as grain size, individual sedimentological texture and the presence of organic matter zones in the sample could be determined by the specialized model. The results show an average classification accuracy of over 94%, indicating the effectiveness of transfer learning in enhancing rock characterization. The obtained models demonstrated high validation rates and were organized into a workflow for automated rock feature characterization. A case study conducted on a carbonate core from a real oilfield provided a practical demonstration of the automated system's work, whose predictions were validated by traditional analyses such as X-ray diffraction and thin-section petrography. The produced models demonstrated the ability for rapid detection of subtle details that might otherwise have been missed during manual inspection. In addition, the concept of tomofacies resulting from the integration of μCT images and machine learning predictions has proven useful in highlighting different reservoir zones, allowing us to understand their heterogeneity and the ability to separate rock sections despite their apparent monotony. These findings suggest that the integration of μCT imaging and transfer learning is a promising approach for improving the automation and accuracy of reservoir rock characterization. The developed technique represents a significant advance in reservoir characterization, offering rapid and accurate identification of rock properties that can improve exploration and development strategies.

Dynamic Change Characteristics and Main Controlling Factors of Pore Gas and Water in Tight Reservoir of Yan’an Gas Field in Ordos Basin

Tight sandstone gas has become an important field of natural gas development in China. The tight sandstone gas resources of Yan’an gas field in Ordos Basin have made great progress. However, due to the complex gas–water relationship, its exploration and development have been seriously restricted. The occurrence state of water molecules in tight reservoirs, the dynamic change characteristics of gas–water two-phase seepage and its main controlling factors are still unclear. In this paper, the water-occurrence state, gas–water two-phase fluid distribution and dynamic change characteristics of different types of tight reservoir rock samples in Yan’an gas field were studied by means of water vapor isothermal adsorption experiment and nuclear magnetic resonance methane flooding experiment, and the main controlling factors were discussed. The results show that water molecules in different types of tight reservoirs mainly occur in clay minerals and their main participation is in the formation of fractured and parallel plate pores. The adsorption characteristics of water molecules conform to the Dent model; that is, the adsorption is divided into single-layer adsorption, multi-layer adsorption and capillary condensation. In mudstone, limestone and fine sandstone, water mainly occurs in small-sized pores with a diameter of 0.001 μm–0.1 μm. The dynamic change characteristics of gas and water are not obvious and no longer change under 7 MPa displacement pressure, and the gas saturation is low. The gas–water dynamic change characteristics of conglomerate and medium-coarse sandstone are obvious and no longer change under 9 MPa displacement pressure. The gas saturation is high, and the water molecules mainly exist in large-sized pores with a diameter of 0.1 μm–10 μm. The development of organic matter in tight reservoir mudstone is not conducive to the occurrence of water molecules. Clay minerals are the main reason for the high water saturation of different types of tight reservoir rocks. Tight rock reservoirs with large pore size and low clay mineral content are more conducive to natural gas migration and occurrence, which is conducive to tight sandstone gas accumulation.

Classification of rock types of porous limestone reservoirs: case study of the A oilfield

Rock types with similar lithological components and pore structures form the basic units of porous limestone reservoirs; this influences the reservoir evaluation efficiency and water injection development. As the main oil and gas pay zone in central Iraq, the Cretaceous Khasib Formation reservoirs are influenced by deposition, dissolution, and cementation. There is strong vertical heterogeneity in the most important zone of the Kh2 layer, with diverse rock types and complex pore structures. Based on core observation and casting thin-section identification, the Kh2 layer in the study area was divided into eight lithofacies types as argillaceous bioclastic wackestone, planktic foraminiferium wackestone, lamellar bioclastic wackestone, intraclastic–bioclastic packstone, patchy green algae packstone, green algae and pelletoid packstone, benthic foraminiferium–bioclastic packstone, and intraclastic grainstone. Along with the reservoir void space types of the lithofacies, capillary pressure curves are used to quantitatively analyze the throat and pore features of the different lithofacies. From the porosity–permeability cross-plot characteristics and distribution of pore types, 14 petrophysical facies are obtained. Finally, based on the differences between the lithofacies and petrophysical facies, the Kh2 member is divided into 13 rock types with different geological origins and petrophysical characteristics. Among these, the rock type RT1-8-14 has the best and rock type RT1-1-1 has the worst physical properties among the reservoir rock types. This study provides an optimization method for carbonate reservoir evaluation and is expected to be beneficial for efficient development of similar carbonate reservoirs.

Selection of suitable geothermometers for predicting the subsurface temperatures with higher accuracy: A study based on globally distributed geothermal field data

Subsurface reservoir temperature of a geothermal province is popularly estimated using various geothermometers but it is necessary to examine the suitability of them. Present work studies the performances of five popular geothermometers Na-K, Na-K-Ca, Quartz, Chalcedony and K-Mg to forecast the reservoir temperature of 148 geothermal springs located in different parts of the world. The predicted temperature of the geothermometers have been compared with the bottom whole temperature (BHT) of the wells drilled in those geothermal fields to check the accuracy of the predictions.The results show that performance of a particular geothermometer is significantly affected by the type of rocks in a geothermal reservoir. Na-K geothermometer provides highest accuracy for the basaltic and sandstone reservoir rocks with average errors 15 % and 11 % respectively. The quartz geothermometer also provides almost equally good prediction for sandstone. The best result for a granitic reservoir rock is obtained by Na-K-Ca with an average error about 15 %. The carbonate reservoir rocks are the most difficult reservoir rock to use any geothermometer as lowest error of prediction using any geothermometer is about 26 %. The chalcedony performed the poorest among all geothermometers and is not recommended for any reservoir rock types under considerations. Thus, the study on global geothermal fields shows that the reservoir rock type must be considered to select a geothermometer else it may cause very high error in prediction of reservoir temperature. However, none of the geothermometer could produce error less than 10 %. It is worth noting that the present study though establishes the suitability of a geothermometer for a particular lithology, it does not explore the rationale for such a suitability.

Rock Typing and Flow Unit Identification in the Upper Cretaceous Carbonate Reservoirs in Jambur Oil Field

The effectiveness of dynamic models in field development and predicting future reservoir performance depends on the accuracy and reliability of geological models. These models are constructed based on an accurate categorization of reservoir rock types and the identification of flow units. understanding the geological structure, lithological characteristics, and depositional processes is crucial in differentiating rock types and determining flow units within strata. This study focuses on identifying rock types and flow units within the upper Cretaceous reservoir/Qamchuqa formation of the Jambur oilfield. By employing four petrophysical techniques across six wells, it was determined that the Rock Fabric Number technique and the Winland porosity-permeability relation were insufficient for accurately estimating permeability, primarily due to constraints in regression analysis. The classification of flow zone indicators resulted in the identification of five distinct rock classes that offer a more reliable means of assessing permeability in intervals lacking core data. Subsequently, the cluster analysis process sorted the reservoir rock into cohesive groups based on the raw log data and calibrated it with the Flow Zone Indicator (FZI) method. In conclusion, permeability can be determined through equations derived from the FZI method, and the establishment of petrophysical characteristics within a geological model can be achieved by utilizing rock facies derived from cluster analysis.

Reservoir Rock Discrimination Based on Integrated Image Logs and Petrographic Analysis: A Case Study from the Early Miocene Nukhul Carbonate, Southern Gulf of Suez, Egypt

AbstractThe discrimination of rock types within the limestones and dolostones of the Nukhul Formation in the West Younis Field (Gulf of Suez Basin, Egypt) presents significant challenges due to their multi-scale compositional and diagenetic heterogeneity, diverse pore types, complex microstructures, and limited core data. This study aims to characterize the carbonate reservoir of the Early Miocene sediments and establish distinct reservoir rock types by employing textural analysis, geological interpretations (i.e., structural interpretation, fracture analysis, reservoir characteristics) using advanced imaging tools, and petrophysical measurements to model porosity/permeability profiles across the reservoir. A new dataset was obtained from the latest exploratory well in the West Younis Field, incorporating microresistivity and acoustic image logs, well logs, nuclear magnetic resonance (NMR) tools, and drill cutting petrographic analysis. The integration of these datasets provided a comprehensive understanding of the properties of the Early Miocene carbonate reservoir. Based on image logs, the carbonate facies were divided into four reservoir units. Petrographic evaluation further classified two facies (A and B) based on diagenetic factors controlling reservoir quality. The results revealed the occurrence of multiple phases of dolomitization, which influenced the reservoir quality. Early-stage dolomitization enhanced reservoir quality, while late-stage idiotopic dolomite crystal growth diminished it. The study also provided comprehensive information on the original rock fabric/texture, diagenetic processes, porosity types and origins, as well as the spatial distribution of pores (permeability index) within this complex carbonate reservoir. By employing an integrated technique, this study successfully differentiated the carbonate reservoir into distinct rock types, leading to improved reservoir characterization and field development. Additionally, the findings contribute valuable insights for the development and exploration of the Early Miocene carbonate section in the southern Gulf of Suez.

A comprehensive study on optimizing reservoir potential: Advanced geophysical log analysis of zamzama gas field, southern indus basin, Pakistan

This study investigates the hydrocarbon and gas potential of the Pab Formation's Late Cretaceous reservoir in the Zamzama Gas Field (ZGF) situated in Pakistan's Southern Indus Basin (SIB). Vital insights to support Pakistan's escalating energy requirements. Petrophysical characteristics of the Pab Formation (PFm) zones of interest in the ZGF were determined by evaluating geophysical logs from four wells. Various techniques were employed to analyze the reservoir rock type (RRT), lithofacies, and spatial distribution of petrophysical parameters. The petrophysical assessment reveals that gas-bearing zones primarily consist of sandstone with intermittent shale layers. These reservoirs exhibit distinguishing attributes, including clean sand composition, high resistivity, total porosity (∅T), good permeability (K), minimal shale content (Vsh), and low water saturation (Sw). The total porosity ranges from 12% to 16%, permeability from 14 mD to 19 mD, water saturation levels between 34% and 44%, and hydrocarbon saturation (Sh) from 56% to 66%. The findings suggest significant gas reserves within the studied sand units of the Zamzama Block. Overall, this research provides a detailed and comprehensive understanding of reservoir characteristics, thereby increasing the potential for further hydrocarbon exploration and production in the study area.

Strontium isotopes and rare earth elements in terrestrial hot-spring deposits: Characterization and geothermal implications

Identifying geothermal reservoir rock types is fundamental in geothermal exploration, but the absence of active surface geothermal manifestations (especially hot springs) in blind geothermal systems makes this identification difficult. Nevertheless, blind geothermal systems may develop early-formed hot-spring deposits. As (bio-)chemical precipitate, these deposits may retain geochemical signatures of spring waters and thus provide insights into reservoir rock types. To assess their geothermal implications, the 87Sr/86Sr and rare earth elements + yttrium (REE + Y) characteristics of hot-spring deposits in two geothermal systems were investigated and compared with those of their reservoir rocks. Results showed relatively uniform 87Sr/86Sr values in hot-spring deposits within each system, but occasional 87Sr/86Sr contamination induced by exogenous detritus input was also observed. The amount of detritus input relates to the potential for interaction with surrounding soils/rocks and is thus environmentally controlled. Hot-spring deposits with high Sr concentrations showed greater 87Sr/86Sr contamination resistance than those with low Sr concentrations, revealing the influence of Sr concentration in hot-spring deposits on detrital 87Sr/86Sr contamination. The detritus input also influenced the REE + Y signatures of the hot-spring deposits, underscoring the necessity of contamination assessment before geochemical interpretation. Excluding samples with significant 87Sr/86Sr and/or REE + Y contamination, the remaining samples closely mirrored their respective reservoir rocks in terms of 87Sr/86Sr, with partial overlap in REE + Y signatures. This suggests that the 87Sr/86Sr and REE + Y characteristics of hot-spring deposits provide valuable insights into reservoir rock types. However, variations in 87Sr/86Sr or REE + Y compositions between some hot-spring deposits and corresponding reservoir rocks indicate additional influencing factors beyond reservoir rock types. Therefore, a comprehensive understanding of reservoir rock types requires integrated geochemical characterization, probably including 87Sr/86Sr, REE + Y, and other parameters. These findings underscore the potential of geochemical characterization of hot-spring deposits for identifying geothermal reservoir rock types, and this geochemical approach can complement geological and geophysical data to improve exploration efficiency, especially in blind geothermal systems.

Reservoir rock typing for optimum permeability prediction of Nubia formation in October Field, Gulf of Suez, Egypt

Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R2 correlation coefficient. The R2 correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability.

Determination of Reservoir Rock Type in Sarvak Reservoir of an Iranian Oilfield

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.

Reservoir and Rock Type Characterization: Case Study for Khasib Formation, Southern Iraq

Characterizing the reservoir accurately and understanding its rock’s composition is essential in predicting performance and determining reservoir designs. In this study, the carbonate Khasib formation from the late Cretaceous period for x oil field- southern Iraq has been examined characterizing. To achieve this, different characterization techniques were utilized. Firstly, using the flow zone indicator method revealed five hydraulic flow units (HFUs) of the Khasib formation. Every HFU represents a particular quality of reservoir rock. HFU1 is the one that refers to poor quality, while bad-quality reservoir rock is displayed as HFU2. HFU3 and HFU4 signify the intermediate and good reservoir rock quality respectively. The last hydraulic flow unit was of the highest quality reservoir rock which is denoted as HFU5. Additionally, we utilized cluster analysis to identify five distinct rock types within the Khasib formation. These rock types were labeled as RT-1 (the best reservoir rock type), RT-2 (good reservoir rock type), RT-3 (intermediate reservoir rock type), RT-4 (poor rock type), and RT-5 (very poor rock type). In addition, the recognition of five different HFUs that reflected the physical characteristics unique to each reservoir rock was achieved using Winland’s approach. Rock properties inside the reservoir are classified to HFU1 for best rocks, then HFU2 denotes good rock qualities through a medium one labeled as HFU3 while later HFU4 indicates poor quality, and the poorest quality is marked as HFU5. Finally, Lucia's classification for carbonate rock was employed as another analyzing rock quality method. Utilizing this technique reveals three distinct rock types within the Khasib formation. RC1 is the microfacies of grain stone, RC2 is the representative of pack-stone microfabrics and RC3 denotes muddy materials. The final rock types (facies) for Khasib formation can be identified according to the incorporation of the different characterization methods which can be utilized to create a realistic three-dimensional rock type model and distribute the properties based on the rock type.

Implications of the depositional and diagenetic attributes on the reservoir properties of the siliciclastic mangahewa formation, Taranaki Basin, New Zealand: Integrated petrographical and petrophysical studies

The siliciclastic Mangahewa reservoir in the Pohokura Gas Field presents a formidable challenge for gas exploration due to its diverse petrophysical properties. To address this challenge, our study employs a comprehensive approach integrating petrography and petrophysical analyses for core and well logging data. In the present study, we uncover an association of sandstone microfacies, in the key wells like Pohokura South-1, Pohokura-1, and Pohokura-2 wells, which change from fine to medium-grained subfeldsarenites to lithic feldsarenites microfacies. The petrographic investigation pinpoints the critical factors influencing the reservoir quality, including facies types, grain size, pore types, sedimentary structure, detrital clay content, and mineral composition. Both the primary intergranular and secondary dissolution pore spaces are responsible for the influential large intergranular pore volumes causing a superior reservoir quality. Mineral composition, influenced by the source and lithofacies, significantly impacts the reservoir properties. Diagenetic processes further control the reservoir quality, where the abundant silica cement, the illitic clays, and the rare pervasive carbonate cementation render the low reservoir quality. Compaction significantly reduces porosity during diagenesis. High-quality reservoir sandstones are characterized by coarse grain size, minimal detrital clay presence, predominantly quartz composition, limited carbonate and moderate silica cementation, low authigenic illite levels, and minor compaction impact. Based on the petrophysical data, the Mangahewa reservoir is composed of five RRTs (reservoir rock types) with the RRT1 samples having the best reservoir characteristics due to their fair porosity, very good permeability, and macro pore sizes. On the other side, the RRT5 samples are characterized by the lowest reservoir quality (poor porosity and permeability and micropore sizes) due to cementation and compaction. Our study provides valuable insights into the intricate petrophysical properties of the Mangahewa reservoir, enhancing the predictability of the reservoir behavior and optimizing hydrocarbon production in this challenging geological setting.