Reservoir Quality Evaluation Research Articles

Development of GIS-based framework for reservoir data management and quality evaluation

Abstract Accurately measuring and understanding reservoirs is critical for ensuring successful reservoir development, reservoir development evaluation have become an increasingly important issue in reservoir development engineering and present many challenges, particularly when attempting to integrate and visualize multiple types of data. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of A quantitative oil and gas reservoir data management and evaluation system. The system is an integrated application based on petroleum geology and GIS technology for managing, displaying, administering and analyzing petroleum geological data. In addition, the system is capable of evaluating reservoir quality and input data quality which provides effective recommendations for the design and preparation of reservoir development programs. In this study, a GIS-based quantitative evaluation module was developed considering both reservoir and input data quality. In this case, the reservoir quality index is determined by five major elements, and the input data quality is determined by the quality of six data sources. The weights of each element are determined by principal component analysis (PCA). Developed a raw data output processing software in C# using ArcGIS engine functions, which automatically generates visual maps and reservoir quality assessment information. The feasibility of the platform is verified by using the geological data of Linfen gas field Formation in the eastern slope of Shanxi. The integrated framework system proposed in this study enables decision-makers and the publics to quickly response to oil reservoir development events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and evaluating.

Reservoir characterization of GABO field in the niger delta basin using facies and petrophysical analyses

This study investigates the GABO field in the onshore Niger Delta Basin using well logs and core samples, with a focus on rock type distribution, reservoir continuity, and petrophysical properties to enhance hydrocarbon exploration accuracy. The research involved detailed analysis of core descriptions and images from GABO-20, along with petrophysical data from wells GABO-4, GABO-12, GABO-13, GABO-20, and GABO-51, aiming to identify rock types, evaluate reservoir quality, and determine the spatial distribution of petrophysical properties. The methodology included rigorous well log quality checks, data correlation between wells, petrophysical property evaluation, and lithofacies description. The analysis revealed thirty-five hydrocarbon-bearing zones, with GABO-20 showing average shale fraction values between 0.111 and 0.335, total porosity ranging from 0.242 to 0.329, and hydrocarbon saturation from 0.667 to 0.923, indicating substantial porosity and hydrocarbon saturation conducive to extraction. Well correlations demonstrated excellent sand-shale continuity within the reservoir intervals. Lithofacies analysis of the GABO-20 core identified eight distinct facies, including shale poles, liquid-rich shales, and sandstone poles, revealing a complex distribution of reservoir qualities with significant variations in potential. Cores 1 and 2 exhibited favourable conditions for hydrocarbon production, while Core 3 showed lower reservoir quality due to increased clay content. The depositional settings of the GABO field, particularly in the lower delta plain and delta front zones, showed well-developed reservoir bodies with moderate potential in the tidal zone and little potential in the shaly prodelta environment. This distribution highlights the complexity of the deltaic system and its implications for reservoir modeling and utilization. Reservoir zones associated with High-Quality Reservoir Facies exhibited strong petrophysical properties, suggesting excellent hydrocarbon production potential. The study underscores the importance of detailed reservoir characterization to optimize recovery strategies, offering valuable insights into the distribution and quality of reservoir sands, reducing subsurface uncertainty, and emphasizing the integration of facies and petrophysical approaches for a better understanding of reservoir characteristics in the GABO field.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

Understanding the distribution of shear wave velocity (VS) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available VS data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the VS data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of VS in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of VS in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of VS throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated VS. Also, the calculated VS section has a high correlation with the actual logs at the location of the wells.

Pore structure characterization and reservoir quality prediction in deep and ultra-deep tight sandstones by integrating image and NMR logs

Pore structure and fracture determine reservoir quality in deep and ultra-deep tight sandstones, however, the limited availability of core data makes it challenging to describe the complexity of pore-throat structure and characterize the wide range of fractures. To fill this gap, a comprehensive analysis was conducted to evaluate pore structure and fractures from multiple perspectives. Integration of core, thin sections, scanning electron microscopy (SEM), high pressure mercury injection (HPMI) and nuclear magnetic resonance (NMR) tests are used to evaluate pore structure and characterize fracture of deep and ultra-deep tight sandstones in the Cretaceous Bashijiqike Formation in the Kuqa Depression. Pore structure and reservoir quality types can be divided into dominantly three types according to characteristics of NMR transverse relaxation time (T2) distribution and HPMI curves. Correlation analysis among HPMI and NMR parameters with petrophysical parameters indicate that maximum pore throat size (rmax), logarithmic mean of T2 (T2gm), irreducible water saturation (Swi), and reservoir quality index (RQI) are the most sensitive parameters for pore structure characterization. The Type Ⅰ pore structure is composed of intergranular and intragranular pores, and NMR T2 spectrum shows the highest magnitude, with the calculated mobile fluid saturation content also being the highest. The Type II and III pore structures are composed of intergranular pores, intragranular dissolved pores and intercrystalline micropores in clay minerals. T2gm of Type II and III are smaller, with the calculated mobile fluid saturation content being the lower.The fracture features and parameters including fracture density, porosity, aperture, and length are analyzed using image logs and core. The pore structure characteristics are quantificationally and qualitatively analyzed by NMR logs. Integration of pore structure and fractures by image logs and NMR logs are used for prediction of high quality reservoir and hydrocarbon productivity. High hydrocarbon productivity is obtained in well intervals with abundant natural fractures, and favorable pore structure will also contribute to high matrix porosity. Therefore, NMR logs and image logs are crucial for evaluating reservoir quality and predicting productivity when calibrated with core and analysis data. The comprehensive analysis integrating core data with geophysical well logs provide important insights for reservoir quality prediction of deep and ultra-deep tight sandstones.

Evaluating methods for logging the pore structure of tight sandstone reservoir in Chang 6 member of the Yanchang Formation, Ordos Basin

Pore structure is a key parameter used to evaluate reservoir quality. At present, experimental method is the most important method to analyze reservoir pore structure. However, coring data may be limited, and it is not possible to perform experimental analyses on all cores. Therefore, the researchers explored the use of logging techniques to study the pore structure of reservoirs. The relationship between pore geometry index (PGI), pore permeability and mercury injection parameters was analyzed based on mercury injection experiment, thin slice analysis, production test data and well logging data. These results can then determine the response characteristics of the logging parameters that correspond to different pore structures and establish a method of modeling the PGI through a multiple parameter regression and neural network method. This research shows that: (1) PGI can quantitatively characterize pore structure, and the maximum pore throat radius, displacement pressure and flow unit index have the highest correlation with PGI, which can be accurately characterized by practical formulas; (2) Natural gamma ray, natural potential amplitude difference, acoustic transit time, density, compensated neutron, deep and shallow resistivity logging data can reflect the quality of the reservoir pore structure. However, there are limitations in evaluating reservoir pore structure with a single logging parameter. Multi-parameter regression method and neural network method realize the quantitative calculation of pore structure from the perspective of multi-parameter and nonlinear. (3) The neural network method and multiple parameter regression method are used to study the pore structure of reservoir and realize the continuous quantitative calculation of pore structure index in a single well. It can be used in uncored analysis Wells and as one of the parameters to evaluate reservoir pore structure.

Pore Structures and Evolution Model of Reservoirs in Different Secondary Structural Zones in the Eocene Shahejie Formation, Chezhen Sag, Bohai Bay Basin.

Although abundant unconventional oil resources have been discovered in conglomerate and sandstone reservoirs in rift basins, the mechanism of differential pore evolution in conglomerates and sandstone reservoirs within different secondary structural zones of rift basins is not yet clear. The pore structures of conglomerate and sandstone reservoirs in the distinct secondary structural zones in the Chezhen Sag were quantified in three dimensions using high-resolution microcomputed tomography (micro-CT). Thin section and scanning electron microscopy observations were used to investigate the differential evolution mechanisms of conglomerate and sandstone reservoirs. Micro-CT analysis of the pore structures of conglomerate and sandstone reservoirs revealed that sandstone reservoirs are superior to conglomerate reservoirs with regard to the pore number and pore connectivity and that sandstone reservoirs are more heterogeneous than conglomerate reservoirs. Triangles dominate the pore and pore throat geometries of sandstone and conglomerate reservoirs, while the sandstone reservoir pores are more regular than conglomerate reservoir pores. The depositional environment, mineral composition, and diagenetic intensity jointly control the quality of the reservoirs. Because of the lengthy transportation distance of their parent rocks, the compositional maturity and sorting behavior of sandstone reservoirs in depression and gentle slope zones are better than those of conglomerate reservoirs in steep slope zones, and thus sandstone reservoirs have a higher initial porosity than conglomerate reservoirs. The rapid compaction experienced by the conglomerate reservoirs in steep slope zones in their early stages creates a closed diagenetic environment, making it difficult to effectively improve reservoir porosity through dissolution. However, the widely developed microfractures in the reservoirs provide channels for fluid migration, promote the development of dissolution pores, and form a tight reservoir dominated by secondary pores. With weak compaction and an open diagenetic environment, the primary pores in sandstone reservoirs in the gentle slope zone are preserved in large quantities. Meanwhile, dissolution expands the secondary pores of the reservoir, resulting in a high-quality reservoir having both primary and secondary pores. In addition, an approach based on primary, secondary, and total porosity was proposed in the study to efficiently evaluate reservoir quality and identify reservoir evolution mechanisms.

Characteristics and Controlling Factors of Natural Fractures in Deep Carbonates of the Puguang Area, Sichuan Basin, China.

Understanding the types, characteristics, and controlling factors of natural fractures in deep and ultradeep carbonates is crucial for evaluating reservoir quality, optimizing well deployment, and comprehending their impact on hydrocarbon exploitation. Multiple types of natural fractures are widespread in the deep carbonates of the Feixianguan Formation in the Puguang area, northeastern Sichuan Basin, China, and the main controlling factors are complex. Based on geological, geophysical, and experimental data, this study defined fracture types and analyzed the fracture development characteristics in the deep Feixianguan carbonates. On this basis, the main geological factors that control the development and distribution of tectonic fractures were discussed by combining statistical and experimental analyses. Results indicate that natural fractures in the Feixianguan Formation can be genetically classified into tectonic and diagenetic fractures. Specifically, tectonic fractures include shear fractures and tensile fractures, in which the former are predominant. In the Feixianguan carbonates, tectonic shear fractures are mainly developed in the NE-SW and near E-W strikes, with dip angles mostly ranging from 30 to 70°. The majority of shear fractures appear on a small scale and have good effectiveness. The fracture heights and apertures are commonly less than 50 cm and 25 μm, respectively, and unfilled fractures account for more than half. The distribution and development of tectonic fractures are mainly controlled by lithology, mechanical stratigraphy, reservoir physical properties, diagenetic cementation, and faults. In the Feixianguan carbonates, tectonic fractures are more developed in crystalline dolomite. With the increasing thickness of mechanical stratigraphy, the fracture density decreases and the scale increases. The presence of early dissolution pores can prevent the formation of later tectonic fractures. Tectonic fractures in the NNW-SSE and near N-S strikes generally possess poor effectiveness due to multiple cementations after their formation. In the vicinity of faults, tectonic fractures generally stretch subparallel to the extension direction of the fault, and the development degree of fractures close to major faults is higher.

Laboratory Study of Stress Sensitivity Characterization and Reservoir Quality Evaluation of Yingxiongling Shale in Qaidam Basin

Laboratory Study of Stress Sensitivity Characterization and Reservoir Quality Evaluation of Yingxiongling Shale in Qaidam Basin

The Classification and Evaluation of an Interlayer Shale Oil Reservoir Based on the Fractal Characteristics of Pore Systems: A Case Study in the HSN Area, China

The evaluation of shale reservoir quality is of great significance for the exploration and development of shale oil. To more effectively study the distribution characteristics of shale reservoir quality, thin-section observation, scanning electron microscopy and pressure-controlled porosimetry were used to obtain the pore structure characteristics of shale in Chang 7, including pore types, pore size distribution, etc. In addition, the fractal dimensions of the shale samples were calculated based on pressure-controlled porosimetry data. The results show that residual interparticle pores, dissolution pores and clay-dominated pores were the main pore types. The overall pore size was mainly distributed between 3 nm and 50 μm. The pore system was divided into four types using fractal features, and the shale reservoir was divided into four types based on the proportion of different types of pore system. In different types of reservoirs, the production capacity of exploration wells varies significantly, as does the production capacity of horizontal wells. The classification of shale reservoirs using mercury intrusion fractal analysis proved to be suited for the efficient development of Chang 7 shale oil reservoirs.

A Comprehensive Evaluation of Shale Oil Reservoir Quality

To enhance the accuracy of the comprehensive evaluation of reservoir quality in shale oil fractured horizontal wells, the Pearson correlation analysis method was employed to study the correlations between geological parameters and their relationship with production. Through principal component analysis, the original factors were linearly combined into principal components with clear and specific physical meanings, aiming to eliminate correlations among factors. Furthermore, Gaussian membership functions were applied to delineate fuzzy levels, and the entropy weight method was used to determine the weights of principal components, establishing a fuzzy comprehensive evaluation model for reservoir quality. Without using principal component analysis, the correlation coefficient between production and evaluation results for the 40 wells in the Cangdong shale oil field was only 0.7609. However, after applying principal component analysis, the correlation coefficient increased to 0.9132. Field application demonstrated that the average prediction accuracy for the cumulative oil production per kilometer of fractured length over 12 months for the 10 applied wells was 91.8%. The proposed comprehensive evaluation method for reservoir quality can guide the assessment of reservoir quality in shale oil horizontal wells.

A new insight to access carbonate reservoir quality using quality factor and velocity deviation log

Estimating wave damping in carbonate rocks is complex due to their heterogeneous structure. For this reason, further research in this area is still necessary. Since the identification and evaluation of reservoir quality play an essential role in the optimal use of hydrocarbon resources, efforts are made to provide new solutions to achieve this goal by managing knowledge and accessing information from new tools such as the Vertical Seismic Profile (VSP). Seismic waves are deformed in frequency content and amplitude as they pass through the earth's layers. Part of the reduction in wavelength is related to the nature of the wave propagation and part to the geological properties, including porosity and fracture. Anisotropy and velocity model analysis, rather than the direct connection between reservoir parameters and seismic absorption coefficient, have received the majority of attention in earlier studies on the impact of reservoir parameters and fractures on changes in the quality factor. In this study, the correlation of the quality factor with parameters such as velocity deviation, fracture density, and permeability has been investigated, and an attempt has been made to define the quality factor as a tool to assess the quality of the reservoir. The statistical study using the multiple linear regression method found that fracture density is the most important parameter that follows the trend of the quality factor value. In the analysis, the quality factor showed a relatively good correlation with the permeability of the core data, so in the periods with maximum permeability, the quality factor had the lowest values. According to K-Means Clustering Analysis, 18% of the studied reservoir interval was evaluated as good quality, 33% as medium, 36% as poor, and 12% as hydrocarbon-free. This work provides insight into accessing reservoir quality using quality factor and velocity deviation logs and would be valuable for the development of reservoir quality prediction methods. Based on the study's results, it is recommended to apply this technique for modeling reservoir heterogeneity and assessing 2D and 3D seismic data to predict the reservoir quality of gas fields prior to drilling operations and reduce exploration risks.

Identification, characterization, and up-scaling of pore structure facies in the low permeability reservoirs: Insight into reservoir quality evaluation and sweet-spots analysis

The traditional pore structure classification scheme separates the qualitative pore origin classification from the quantitative pore structure parameter classification. This separation prevents a comprehensive understanding and evaluation of the pore structure and characteristics of multi-scale reservoirs. To overcome this limitation, we propose a new pore classification scheme that combines the “geological origin and structure parameters” of pore systems, introducing the term “pore structure facies” (PSF). By considering the genetic types of pores, the pore structure can be qualitatively divided into several categories, each representing different pore genesis. Within each category, the pore structure is further subdivided into subcategories based on pore structure parameters. To validate the effectiveness of the PSF scheme, we focus on the low permeability reservoirs of the Weixinan Depression in the Beibuwan Basin of the South China Sea. We identify and characterize PSF types using a multi-scale approach, including thin section, porosity-permeability, digital image analysis, mercury intrusion test, micro-CT imaging, and simulation. The results reveal that the study area can be classified into three main categories and six subcategories: intergranular homogeneous type (macroporous, mesoporous, and microporous), heterogeneous dissolution-pore type (moderate and strong dissolution), and cementation-induced few-pore type. Significant differences are observed in the two-dimensional and three-dimensional pore structure, petrophysical properties, and pore-scale flow of PSF, leading to significant variations in logging responses. Moreover, we successfully predict and verify the PSF profile using artificial neural network algorithms and field test data. The predicted PSF profile exhibits substantial differences in productivity, with the combination of PSF types serving as an indicator of productivity. We compare the quality of multi-scale reservoirs in the study area using the “point (single well) line (inter-well profile) plane (reservoir)" method based on PSF. Finally, to quantitatively characterize reservoir quality, we propose the Reservoir Sweet-spot Index (RSI), considering the reservoir thickness. By using the boundary of sedimentary facies, we generate a map of sweet-spots reservoirs that provides guidance for future energy exploration and development. The analytical scale of PSF and RSI can be flexibly adjusted, from the heterogeneity within the sand body to the distribution characteristics of a reservoir plane, offering valuable insights into reservoir quality estimation at different scales.

The intelligent optimization of perforation cluster locations incorporating the fiber optics monitoring results

During horizontal well multi-stage fracturing (HWMF), superfractures are often identified. To promote the uniform propagation of multiple fractures, it is necessary to finely optimize the perforation cluster locations based on the geological and engineering parameters. This work proposes an efficient method to design the perforation cluster locations in consideration of the geoengineering sweet spots with similar mechanical properties. Well log data and the precise fiber optics (FO) monitoring results are combined to find the main influencing factors. The principal component is conducted by introducing correlation analysis and Random Forest. Moreover, the K-means++ clustering method is used to evaluate reservoir quality. The fracturing sweet index (FSI) is proposed to measure the fracturing performance of each category quantitatively. The proposed workflow is effectively validated by two production scenarios. Moreover, the workflow can automatically evaluate reservoir quality based on intelligent clustering methods. Compared with the original design, the updated design lowers the gap among multiple fractures within one stage and increases the well production by 20%–50%. This work is beneficial for the on-site treatment of its feasibility and generalizability.

Single-Factor Comprehensive Reservoir Quality Classification Evaluation-Taking the Hala'alat Mountains at the Northwestern Margin of the Junggar Basin as an Example.

In the process of petroleum geology exploration and development, reservoir quality evaluation is an essential component. However, conventional reservoir quality evaluation methods are no longer able to provide accurate and comprehensive assessments for all types of reservoirs. Therefore, the comprehensive evaluation of reservoir quality using multiple single factors is of significant importance in improving the level of reservoir quality assessment and enhancing the effectiveness of oil and gas exploration techniques. Conventional reservoir quality evaluation methods can assess only the quality of individual reservoir properties, resulting in limited classification outcomes. Taking the Cretaceous formations in the southern margin of the Hala'alat Mountain in the Junggar Basin as the research object, preliminary classification criteria were established based on the principles of formation coefficient, storage coefficient, and flow unit index. Combining experimental data such as core observation, thin-section identification, pore permeability analysis, and scanning electron microscopy, a comprehensive set of reservoir quality classification and evaluation criteria were developed. Furthermore, the corresponding reservoir classification evaluation maps were generated to illustrate the spatial distribution of reservoir quality. The study reveals that the area can be classified into four types of reservoirs, namely, Class I, Class II, Class III, and Class IV, corresponding to the best reservoir, relatively good reservoir, relatively poor reservoir, and poor reservoir, respectively. Among them, the second (K1q2) and third (K1q3) members of the Cretaceous Qingshuihe Formation, as well as the first (K1h1) and third (K1h3) members of the Cretaceous Hutubi Formation, exhibit the best reservoir quality as Class II. On the other hand, the second member of the Cretaceous Hutubi Formation (K1h2) exhibits the best reservoir quality as Class III, with relatively poorer reservoir quality overall. The research findings of this study can provide an important theoretical basis for oil and gas exploration and development in the region.

Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin

Abstract Quantitative studies of the pore-throat structure (PTS) characteristics of tight sandstone reservoirs and their effects on fluid mobility were proposed to accurately evaluate reservoir quality and predict sweet spots for tight oil exploration. This study conducted high-pressure mercury injection (HPMI) and nuclear magnetic resonance (NMR) experiments on 14 tight sandstone samples from the Chang 7 member of the Yanchang Formation in the Jiyuan area of the Ordos Basin. The HPMI was combined with the piecewise fitting method to transform the NMR movable fluid transverse relaxation time (T 2) spectrum and quantitatively characterize the PTS characteristics and the full pore-throat size distribution (PSD). Then, movable fluid effective porosity (MFEP) was proposed to quantitatively evaluate the fluid mobility of tight sandstone reservoirs and systematically elucidate its main controlling factors. The results showed that the PTS could be divided into four types (I, II, III, and IV), which showed gradual decreases in average pore-throat radius (R a), continuous increases in the total fractal dimension (D t), and successive deterioration of reservoir fluid mobility and percolation capacity. Moreover, the full PSD (0.001–10 μm) showed unimodal and multi-fractal characteristics. According to the Swanson parameter (r apex), the reservoir space types can be divided into small and large pore-throat and the corresponding fractal dimension has a relationship where D 1 < D 2. Large pore-throat had higher permeability contribution and pore-throat heterogeneity but a lower development degree and MFEP than small pore-throat, which had a relatively uniform and regular PSD and represented the primary location of movable fluids. Moreover, the development degree and heterogeneity of small pore throat controlled the flowability of reservoir fluids. MFEP can overcome the constraints of tiny throats and clay minerals on movable fluid, quantify the movable fluid content occupying the effective reservoir space, and accurately evaluate the reservoir fluid mobility. The combination and development of various pore-throat sizes and types in tight sandstone reservoirs results in different PTS characteristics, whereas differences in the mineral composition and content of reservoirs aggravate PTS heterogeneity, which is the main factor controlling the fluid mobility.

Logging evaluation of pore structure and reservoir quality in shale oil reservoir: The Fengcheng Formation in Mahu Sag, Junggar Basin, China

The Permian Fengcheng Formation in the Mahu Sag of the Junggar Basin is characterized by frequent changes of mineral compositions and lamina structure. The alkali lacustrine shale reservoir shows strong heterogeneity and anisotropy, it is particularly important to clarify the relationship between pore structure and reservoir quality. The pore characteristics were investigated by using thin sections, Scanning Electron Microscope (SEM), and Nuclear Magnetic Resonance (NMR) analyses. The pore types are mainly composed of inorganic pores, organic pores, and microfractures. The storage space is dominated by inorganic pores and microfractures. The pore structure was divided into four types by NMR parameters including T2 spectral morphology, T2gm, total porosity, and movable fluid porosity. Combined with core NMR experiments and two-dimensional (2D) NMR logs, 21 ms was adopted as the T2cutoff value to represent the demarcation between movable and bound fluids. From Type I to Type IV, the energy clusters reflect the movable fluids including movable oil and movable water gradually decreasing in the upper right corner. The energy clusters reflect the bound fluids including clay-bound water and bitumen gradually increasing in the lower left corner in the T1-T2 map. The results show that the heterogeneity of mineral compositions and lamina structures are the key factors directly leading to the complexity and distribution of pore structure. Quartz with strong compaction resistance helps keep a large number of primary interparticle pores, and feldspar dissolution pores caused by organic acids are present. Dolomite intercrystalline pores and dissolution pores provide a large amount of storage space for the shale, while the cementation of calcite reduces pore spaces. The increase of felsic mineral content is conducive to improving the pore structure and oil-bearing potential of oil shale reservoir, while clay minerals and calcite are contrary. There are abundant primary interparticle pores and dissolution pores in the layered siliceous (felsic) shales, and they consist of the upper sweet spot in the Fengcheng Formation. The longitudinal time (T1) and transverse time (T2) spectra are bimodal with high total porosity and movable oil content, reflecting high reservoir quality and oil-bearing property. Laminated dolomite shales contain dolomite intercrystalline pores and dissolution pores, and they consist of the lower sweet spot in the Fengcheng Formation. The results above provide a new perspective on the evaluation of pore structure and reservoir quality of shales in the context of an alkaline lake.

Reservoir quality evaluation of the Narimba Formation in Bass Basin, Australia: Implications from petrophysical analysis, sedimentological features, capillary pressure and wetting fluid saturation

The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin, Australia. This study involved combination methods such as petrophysical analysis, petrography and sedimentological studies, reservoir quality and fluid flow units from derivative parameters, and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality. Cementation, compaction, and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it. It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability. Porosity ranges from 3.1% to 25.4% with a mean of 15.84%, while permeability ranges between 0.01 mD and 510 mD, with a mean of 31.05 mD. Based on the heterogenous lithology, the formation has been categorized into five groups based on permeability variations. Group I showed an excellent to good quality reservoir with coarse grains. The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index (RQI) and flow zone indicators (FZI) as well as mostly mega pores. The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero. Group II showed a fair quality reservoir with lower petrophysical properties in macro pores. The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality. Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure. It may indicate smaller grain size and increasing amount of cement and clay minerals. Group IV, and V are interpreted as a poor-quality reservoir that has lower RQI and FZI. The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii (r35) values to be within the range of macro, meso-, micro-, and nano pores. The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V. Agreement with the classification of permeability, r35 values, and pore type can be used in identifying the quality of reservoir.

Depositional and diagenetic controls on reservoir properties along the shallow-marine carbonates of the Sarvak Formation, Zagros Basin: Petrographic, petrophysical, and geochemical evidence

As the second important oil reservoir of Iran, shallow-marine carbonates of the Sarvak Formation were deposited in a tectonically active setting, under a warm and humid paleoclimatic condition. This study focuses on sedimentological, geochemical, and petrophysical analysis of this formation in the Abadan Plain, Zagros Basin. Petrographic studies have resulted in the recognition of five microfacies deposited in lagoon, patch-reefs and their taluses, shoal, and open marine environments. They represent a ramp-like depositional model for the Sarvak Formation. Two third-order depositional sequences are differentiated and dated as Cenomanian–Turonian, according to the results of biostratigraphic analysis. Effects of intensive meteoric diagenesis are recorded beneath the two paleoexposure surfaces (Cenomanian–Turonian boundary, and middle Turonian). They include pervasive dissolution (karstification), collapsed brecciation, silicification, recrystallization, and development of paleosols. Additionally, decreased values of δ18Ocarb and δ13Ccarb and a sharp inverted-J pattern in the δ18Ocarb vs. δ13Ccarb cross plot indicate major meteoric phases. The most decreased values of δ18Ocarb (−7 ‰) and δ13Ccarb (−8.6 ‰) are measured from the meteoric cements and paleosols. Reservoir quality evaluation revealed that the best reservoir zones of the Sarvak Formation are developed within the rudist-dominated facies with intensive meteoric dissolution. They are concentrated within the regressive systems tract of the Cenomanian sequence. Fractured and dolomitized mud-dominated facies provided the speed/baffle zones in transgressive systems tracts, and highly compacted/cemented facies formed the barrier units. It could be concluded that the facies characteristics and meteoric diagenetic processes had major controls on reservoir quality of the Sarvak Formation, in the Abadan Plain. These controlling factors are predictable in a high-resolution sequence stratigraphic framework.

Movable hydrocarbon index as a tool for evaluating reservoir quality: a case study of the Nubia sandstone in West Hurghada province (southern Gulf of Suez, Egypt)

Movable hydrocarbon index as a tool for evaluating reservoir quality: a case study of the Nubia sandstone in West Hurghada province (southern Gulf of Suez, Egypt)