Carbonate Reservoir Characterization Research Articles

Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method

Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development.

Characterization of Carbonate Reservoirs and Main Controlling Factors Research

Characterization of Carbonate Reservoirs and Main Controlling Factors Research

Quantitative characterization and application of deep learning carbonate reservoir

Abstract Quantitative reservoir characterization is an important research task in carbonate reservoir development. At present, the comprehensive evaluation of drilling, logging, seismic, and productivity testing data is usually used, which requires a large number of test data and tedious manual data collation. Deep learning technology can independently learn complex reservoir characteristics, has a strong ability for high-dimensional data structure mining, and has a strong advantage in solving complex nonlinear reservoir description problems closely related to various geological characteristics. Therefore, this paper applies deep learning technology to reservoir characterization. First, a deep neural network model is constructed, and then seven characteristic parameters such as fracture density, fracture opening, fracture porosity, matrix porosity, total porosity, effective thickness, and specific oil production index are taken as input variables. After model training and verification, the effective permeability distribution map of the reservoir in the target area can be automatically generated. The results show that the deep learning method has strong adaptability and practicability for the quantitative characterization of complex reservoirs, and provides a new way of thinking for the quantitative characterization of reservoirs.

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.

Automated Reservoir Characterization of Carbonate Rocks using Deep Learning Image Segmentation Approach

Summary The objective of this study is to develop a systematic and novel workflow for the automated and objective characterization of carbonate reservoirs with the help of deep learning architectures. An image database of more than 6,000 carbonate thin-section images was generated using the optical microscope and image augmentation techniques. Five features, namely clay/silt/mineral, calcite, pores, fossils, and opaque minerals, were identified with the help of manual petrography of the thin sections under the microscope. A total of four deep learning models were developed, which included U-Net, U-Net with ResNet34 backbone, U-Net with Mobilenetv2 backbone, and LinkNet with ResNet34 backbone. The Ensemble model of U-Net + ResNet34 and U-Net + MobileNetv2 yielded the highest intersection over union (IoU) score of 75%, followed by the U-Net + ResNet34 model with an IoU score of 61%. The models struggled with class imbalance, which was very prominent in the image database, with classes such as fossils and opaques considered to be rare. The statistical analysis of the relative errors revealed that the major classes play a more important role in increasing the final IoU score as opposed to the common understanding that the rare classes affect the model performance. The novel workflow developed in this paper can be extended to real carbonate reservoirs for time efficient, objective, and accurate characterization.

Advanced machine learning for missing petrophysical property imputation applied to improve the characterization of carbonate reservoirs

Missing data is a common cause of uncertainty in reservoir characterization, especially in core analysis of porosity and permeability. This is due to the high cost and time required to gather core samples while drilling through the entire reservoir thickness. Therefore, the ability to collect core samples and directly assess petrophysical parameters is restricted by the small proportion of the reservoir thickness that can be measured by the retrieved cores. As a result, the absence of data has the potential to lead to inconsistent and imprecise classification of reservoirs and geomodeling, thereby increasing reservoir uncertainty. Therefore, imputation algorithms are needed to estimate petrophysical properties for incomplete intervals. This study comprehensively compares seven imputation techniques for predicting missing horizontal and vertical core permeability and core porosity data in a well drilled through a carbonate reservoir in a southern Iraqi gas field. The performance of each method was assessed using relative bias (RB) and reasonable descriptive statistical distributions (histogram, log view, and box plots of data ratios to compare the imputed and original data), and robustness to outliers (RtO). The results revealed that the random imputation of missing data (RIMD) technique is the most effective algorithm in addressing the 30.5% missing data (vertical permeability), resulting in the attainment of the RB value closest to zero (0.04076191) compared to other algorithms. Principal component analysis (PCA) and random forest (RF) yielded the most favorable results in terms of handling missing values and outliers for the 15% missing ratio dataset (core porosity and horizontal permeability). The advantages of these approaches are related to their capacity to independently and accurately impute the missing data without requiring extra information, such as well logging records. Additionally, the multiple imputation and machine learning algorithms fill missing values with adequate numbers based on dataset variable distributions and correlations. Multiple plausible values quantify the uncertainty in estimating missing data, avoiding false precision (as with single imputation). Also, the novel workflow involving multiple imputation methods accurately take into account the correlations and variances between the variables. The novel workflow developed for missing data imputation has the potential to be applied to other clastic and carbonate reservoirs in oil and gas fields. The entire data imputation workflow was conducted, validated, visualized by codes in R, a robust open-source statistical computing software.

Development of a New Correlation for Predicting Initial Water Saturation in Carbonate Reservoirs

The Middle East, rich in oil and gas within carbonate rocks, accounts for a significant portion of global reserves, drawing extensive exploration by major oil firms. Unlike Southeast Asia's fracture and cavity-dominated carbonate reservoirs, the Middle East features thick-bedded, pore-structured reservoirs with vast reserves. These complex and varied pore structures cause reservoir inhomogeneity, challenging the technical evaluation of these unconventional reservoirs. Characterization of carbonate reservoirs differs in terms of their mineralogical compositions and heterogenous pore systems from that of clastic reservoirs. Reservoir characterization seeks to build geological and petrophysical models for reservoir simulation. Rock types represent the most crucial characteristics of reservoirs for specialized facies modelling within specific ranges of porosity and permeability. Rock typing is an essential method routinely used by petroleum engineers for characterizing and predicting the reservoir quality of carbonate reservoirs by classifying reservoir rocks into distinct units based on similar petrophysical properties. It is imperative to predict these reservoir properties accurately and precisely. The J-function technique is considered the most effective rock typing procedure. In this study, a new correlation for predicting initial water saturation (Swi) for a reservoir producing from a Permian carbonate formation, located in the Arabian Peninsula, has been developed. The new empirical equation is an augmented Lucia model that utilizes capillary pressure (P�), porosity (), and permeability (k), as independent variables. The coefficient of multiple R2 , the student’s t and F-tests p-value were used in the model evaluation. R2 for the new model was about 0.92, t-test and F-test p-values were much lower than 0.05, indicating that the independent variables are significant. The model was also tested against an independent data set and yielded an R2 of 0.88. Likewise, the new correlation was compared to Lucia’s model and showed better results. The goal of the study is to use the developed correlation in the geostatistical modeling of connate water saturation for analogous formations in the region.

Improving permeability prediction via Machine Learning in a heterogeneous carbonate reservoir: application to Middle Miocene Nullipore, Ras Fanar field, Gulf of Suez, Egypt

Predicting and interpolating the permeability between wells to obtain the 3D distribution is a challenging mission in reservoir simulation. The high degree of heterogeneity and diagenesis in the Nullipore carbonate reservoir provide a significant obstacle to accurate prediction. Moreover, intricate relationships between core and well logging data exist in the reservoir. This study presents a novel approach based on Machine Learning (ML) to overcome such difficulties and build a robust permeability predictive model. The main objective of this study is to develop an ML-based permeability prediction approach to predict permeability logs and populate the predicted logs to obtain the 3D permeability distribution of the reservoir. The methodology involves grouping the reservoir cored intervals into flow units (FUs), each of which has distinct petrophysical characteristics. The probability density function is used to investigate the relationships between the well logs and FUs to select high-weighted input features for reliable model prediction. Five ML algorithms, including Linear Regression (LR), Polynomial Regression (PR), Support Vector Regression (SVR), Decision Trees (DeT), and Random Forests (RF), have been implemented to integrate the core permeability with the influential well logs to predict permeability. The dataset is randomly split into training and testing sets to evaluate the performance of the developed models. The models’ hyperparameters were tuned to improve the model’s prediction performance. To predict permeability logs, two key wells containing the whole reservoir FUs are used to train the most accurate ML model, and other wells to test the performance. Results indicate that the RF model outperforms all other ML models and offers the most accurate results, where the adjusted coefficient of determination (R2adj) between the predicted permeability and core permeability is 0.87 for the training set and 0.82 for the testing set, mean absolute error and mean squared error (MSE) are 0.32 and 0.19, respectively, for both sets. It was observed that the RF model exhibits high prediction performance when it is trained on wells containing the whole reservoir FUs. This approach aids in detecting patterns between the well logs and permeability along the profile of wells and capturing the wide permeability distribution of the reservoir. Ultimately, the predicted permeability logs were populated via the Gaussian Random Function Simulation geostatistical method to build a 3D permeability distribution for the reservoir. The study outcomes will aid users of ML to make informed choices on the appropriate ML algorithms to use in carbonate reservoir characterization for more accurate permeability predictions and better decision-making with limited available data.

Comprehensive pore structure characterization and permeability prediction of carbonate reservoirs using high-pressure mercury intrusion and X-ray CT

Comprehensive pore structure characterization and permeability prediction of carbonate reservoirs using high-pressure mercury intrusion and X-ray CT

Petrographical and petrophysical characterization of pre-salt Aptian carbonate reservoirs from the Santos Basin, Brazil

Reservoir quality in carbonates is influenced by various factors such as the depositional environment, burial history and diagenesis processes. Understanding these geological heterogeneities is essential for successful petroleum exploration. This study characterizes Brazilian pre-salt reservoirs and aims to understand how their heterogeneity impacts reservoir quality. We analysed carbonate samples from the Barra Velha Formation (Santos Basin) through an integration of petrographical and core plug descriptions, petrographical facies characterization, porosity and permeability measurements, and image analysis to identify the principal controls on porosity and permeability, pore-size distribution, and groups with similar petrophysical properties using the hydraulic flow unit (HFU) concept. Five facies groups were recognized: Spherulitestone (F1); Shrubstone (F2); Intraclastic Grainstone (F3); Intraclastic Packstone, Spherulitestone with mud and Shrubstone with mud (F4); and Shrub–Spherulite Intercalations and Bioclastic Grainstone (F5). The analysis of porosity and permeability showed that their variations are associated with pore type and cementation rate. Greater contributions of inter-aggregate, interparticle and vugular porosity, combined with a reduced amount of cement, results in higher porosity and permeability but an increase in cement tends to reduce the porosity and permeability. Among the facies groups, F2 and F3 exhibited the best porosities and permeabilities, followed by F1, F4 and F5. From image analysis, small pores (1.5 × 10 −5 –0.01 mm 2 ) are the most common in all rocks. However, these small pores contributed significantly to total porosity only in F4 and some samples of F3. For F2 and F3, the large pores (from 0.01 mm 2 to a maximum of 19.62 mm 2 ) are the main contributors, while F5 has a homogeneous contribution. Finally, the data were grouped into five HFUs: HFU1 and HFU2 represent the zones with the best reservoir quality, primarily composed of F2 and F3.

Fracture-vuggy carbonate reservoir characterization based on multiple geological information fusion

The complexity and strong heterogeneity of carbonate reservoirs with fracture-vuggy structures present significant challenges in reservoir characterization. To address these challenges, we propose a novel multi-element information fusion modeling approach. This approach is designed to integrate multiple methods and incorporate multi-probability fusion at various facies and scales, thereby bridging the gap between geological information and reservoir modeling. Our methodology involves four key steps. First, the statistics between frequency of karst and geological information are acquired, and we quantify the statistics to regression equations. Second, these regression equations are transferred to probability bodies. The probability bodies can be applied in modeling as a soft control. But just one single body can be input in modeling process. Third, multiple probability bodies are fused into a fusion probability body by a probability fusion algorithm, which can keep the potential information of probability bodies. Finally, we apply the probability body in modeling workflow. By this way, the fusion method bridges the gap between geological information and modeling. The model established through our proposed method showed a significant level of consistency with reservoir re-evaluation, achieving an impressive 90% degree of alignment. Furthermore, the history match analysis revealed a high correlation, indicating the model's reliability. The method effectively integrates various scales and types of geological information, offering an accurate approach to complex carbonate reservoir modeling.

Deep carbonate reservoir characterization using multiseismic attributes: A comparison of unsupervised machine-learning approaches

Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geologic model building. The sparsity of the labeled samples often limits the application of supervised machine learning (ML) for seismic reservoir characterization. Unsupervised learning methods, in contrast, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method of principal component analysis (PCA), the manifold learning methods of t-distributed stochastic neighbor embedding and uniform manifold approximation and projection (UMAP), and the convolutional autoencoder (CAE), on the 3D synthetic and field seismic data of a deep carbonate reservoir in southwest China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geologic features and indicates the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that joint use of several types of seismic attributes, instead of using a single type of seismic attributes, can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize the sedimentary facies distribution, which is consistent with the geologic understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.

Pore to Core Scale Characterization of Hydraulic Flow Units Using Petrophysical and Digital Rock Analyses

This article illustrates an integrated method for characterizing hydraulic flow units in subsurface reservoirs using multiscale images and petrophysical data analysis from well cores. This method is applied to 35 meters of cores drilled from a Late Jurassic Upper Jubayla Formation outcrop in Saudi Arabia, which is analogous to the lower section of the Arab‐D reservoir. Scanning electron microscope (SEM) imagery, thin‐section petrography, and X‐ray computed tomography (CT) images of whole cores and core plugs are used to visualize and characterize these carbonate rocks across multiple length scales. Core plug porosity and permeability data are used for petrophysical characterization and deriving hydraulic flow units (HFU). The multimodal pore types associated with the HFUs and their connectivity using thin section and SEM image analysis, combined with mercury injection porosimetry are used. Whole‐core CT textures and heterogeneity logs are correlated with HFU and digital image analysis of core plugs. Whole‐core CT data prove to be a highly valuable tool for bridging the scale gap between well data and reservoir models. This methodology can be applied to datasets from a large number of wells, especially when combined with machine learning tools, allowing improved characterization of complex carbonate reservoirs.

Integrated geophysical and petrophysical characterization of Upper Jurassic carbonate reservoirs from Penobscot field, Nova Scotia: a case study

Integrated geophysical and petrophysical characterization of Upper Jurassic carbonate reservoirs from Penobscot field, Nova Scotia: a case study

Rock Typing Approaches for Effective Complex Carbonate Reservoir Characterization

For highly heterogeneous complex carbonate reef reservoirs, rock typing with respect to depositional conditions, secondary processes, and permeability and porosity relationships is a useful tool to improve reservoir characterization, modeling, prediction of reservoir volume properties, and estimation of reserves. A review of various rock typing methods has been carried out. The basic methods of rock typing were applied to a carbonate reservoir as an example. The advantages and disadvantages of the presented methods are described. A rock typing method based on a combination of hydraulic flow units and the R35 method is proposed. Clustering methods for rock typing are used. The optimum clustering method is identified, and for each rock type, the permeability–porosity relationships are built and proposed for use in the geomodelling stage.

Advanced seismic characterization of a geothermal carbonate reservoir – insight into the structure and diagenesis of a reservoir in the German Molasse Basin

Abstract. The quality of geothermal carbonate reservoirs is controlled by, for instance, depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution difficult to predict. Using a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate how a comprehensive seismic attribute analysis can significantly improve the understanding of a complex carbonate reservoir. We deliver an improved reservoir model concept and identify possible exploitation targets within the Upper Jurassic carbonates. We use seismic attributes and different carbonate lithologies from well logs to identify parameter correlations. From this, we obtain a supervised neural-network-based 3D lithology model of the geothermal reservoir. Furthermore, we compare fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis) to address scalability. Our results show that, for example, acoustic impedance is suitable to identify reefs and karst-related dolines, and sweetness proves useful to analyse the internal reef architecture, whereas frequency- and phase-related attributes allow the detection of karst. In addition, reef edges, dolines, and fractures, associated with high permeabilities, are characterized by strong phase changes. Fractures are also identified using variance and ant tracking. Morphological characteristics, like dolines, are captured using the shape index. Regarding the diagenetic evolution of the reservoir and the corresponding lithology distribution, we show that the Upper Jurassic carbonate reservoir experienced a complex evolution, consisting of at least three dolomitization phases, two karstification phases, and a phase of tectonic deformation. We observe spatial trends in the degree of dolomitization and show that it is mainly facies-controlled and that karstification is facies- and fault-controlled. Karstification improves porosity and permeability, whereas dolomitization can either increase or decrease porosity. Therefore, reservoir zones should be exploited that experienced only weak diagenetic alteration, i.e. the dolomitic limestone in the upper part of the Upper Jurassic carbonates. Regarding the fracture scalability across seismic and well scales, we note that a general scalability is, due to a combination of methodological limitations and geological reasons, not possible. Nevertheless, both methods provide an improved understanding of the fracture system and possible fluid pathways. By integrating all the results, we are able to improve and adapt recent reservoir concepts, to outline the different phases of the reservoir's structural and diagenetic evolution, and to identify high-quality reservoir zones in the Munich area. These are located southeast at the Ottobrunn Fault and north of the Munich Fault close to the Nymphenburg Fault.

Geo-Body and Geostatistical Modelling of Carbonate Reservoir Facies Architecture and Characterization

Carbonate reservoirs present significant challenges in characterizing and extracting hydrocarbons due to their low permeability, matrix heterogeneities, fractures, and dissolution patterns. Accurately predicting the facies architecture and reservoir properties in such complex formations has been a persistent challenge for geoscientists. This paper proposes an integrated approach that combines geo-body extraction and geostatistical modeling to accurately predict the facies architecture and reservoir properties in carbonate reservoirs. The methodology begins by generating 3D seismic root mean square amplitude (RMS) attributes, which are then used to extract geo-bodies along the pay sequences. The extracted geo-bodies are then subjected to geostatistical modeling to analyze reservoir properties to facilitate the optimization of drilling and production strategies. To validate the effectiveness of the proposed approach, a small field in the Mumbai offshore basin is chosen as a case study. This field is located on the Mumbai High-Deep Continental Shelf and exhibits westerly dipping structures. Structural mapping confirms the presence of an antiformal structure, with one particular well (D-8) at the crest showing the absence of hydrocarbons. The proposed approach mapped two seismic reflectors within the reservoir zones and generated window-based 3D seismic RMS attributes to extract three geo-bodies within the reservoir. Facies and property modeling revealed the presence of distinct non-reservoir facies with poor reservoir properties near dry wells (D-8, D-4, and D-7), which is in line with the production performance observed in the drilled wells. The proposed integrated approach of geo-body extraction and geostatistical modeling is effective in delineating the facies architecture and reservoir heterogeneity of carbonate reservoirs. It enables the identification of favorable reservoir facies and facilitates a comprehensive assessment of the remaining potential.

Karst architecture characterization of deep carbonate reservoir using image logs in Tarim Basin, West China

This paper has conducted a high‐resolution characterization of a deeply buried carbonate karst in the Tarim Basin of China by utilizing the FMI (Full‐bore Formation Micro Image) method integrated with seismic data, drilling cores and thin sections. It was found that the type and scale of wellbore karst architecture are mainly controlled by karst paleogeomorphology and vertical faults. The karst highlands and slopes, respectively, develop vadose karst and phreatic karst, with different compositions of karst elements. In terms of the karst thickness in the wellbore, the vertical fault development zone is almost twice that of the non‐development zone, both for the vadose karst and phreatic karst. Furthermore, the vertical fault is also able to promote the development of mixing karst. The mixing zone is located 55 m below the water table, with a strata thickness of approximately 25 m, a net reservoir thickness of 5–15 m, and an average porosity of 4%–8%, which serves as an effective hydrocarbon reservoir in the Yingshan Formation. The results will provide insights into the vertical division of karst reservoir and help predict reservoirs using well logs.

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

The purpose of this review paper is to show the possibilities of carbonate reservoir characterization using well logging and laboratory measurements. Attention was focused on standard and new methods of well logging acquisition and interpretation including laboratory experiments to show a part of the history of carbonate rock investigations as hydrocarbon or water reservoirs. Brief information on the geology, mineralogy and petrography of carbonate rocks was delivered. Reservoir properties, i.e., porosity (including fracturing), permeability, and saturation, were defined to emphasize the specific features of carbonates, such as fractures, and vugs. Examples of methodologies were selected from the commonly used laboratory techniques (thin sections examination, mercury and helium porosimetry, X-ray diffraction—XRD) combined with the standard well logs (bulk density—RHOB, neutron porosity—NPHI, sonic slowness—DT, and deep resistivity—Rd) to show the methods that have been used since the very beginning of the scientific and engineering studies of carbonates. Novelty in well logging, i.e., resistivity and acoustic imaging, nuclear magnetic resonance–NMR, dipole shear sonic imaging–DSI, and a spectral neutron-gamma log-geochemical device–GLT combined with modern laboratory investigations (NMR laboratory experiments, scanning electron microscopy SEM), showed how continuous information on mineral composition, porosity and saturation could be obtained and juxtaposed with very detailed laboratory data. Computed X-ray tomography (CT) enabling the 2D and 3D analyses of pores and fractures was presented as a quantitative methodology, effective in pore space characterization, revealing rock filtration abilities. Deep learning and artificial intelligence were used for joining various types of data. It was shown that thanks to new computational technologies original data from very small samples (micro scale), extensively describing the flow ability of the reservoir, could be extended to mezzo scale (core samples) and macro scale (well log images). Selected examples from the published papers illustrated the review. References cited in the text, together with the issues included in them, were the rich source of the practical knowledge processed These were checked by the authors and could be used in other projects.

Multi-scale geophysical characterization of microporous carbonate reservoirs utilizing machine learning techniques: An analog case study from an upper Jubaila formation outcrop, Saudi Arabia

Microporosity hosts a significant portion of the total hydrocarbon volume in the Middle Eastern carbonate reservoirs. An improved understanding of microcrystals' morphology that hosts micropores, their impact on reservoir properties, and their spatial distributions will contribute to a more accurate reservoir quality prediction. This study proposes a methodology that utilizes machine learning approaches for microporosity characterization and integrates a multi-scale dataset ranging from micrometer-scale SEM images to meter-scale seismic attributes to build reservoir porosity models. We tested the methodology on an outcrop in Riyadh, Saudi Arabia, which exposes the upper part of the Jubaila Formation equivalent to the lower part of the Arab-D reservoir. We acquired a 35 m-long core and a 600 m-long 2D seismic line behind the outcrop. Laboratory-scale petrophysical measurements, including porosity, permeability, acoustic velocity, bulk and grain density, and x-ray diffraction, were performed over 106 horizontal core plugs drilled from the core. We quantitatively characterized the morphology and microtextures of micrite crystals using SEM images by performing Random Forest classifications trained on SEM image features. We performed unsupervised classification using Self-Organizing Map (SOM) to all lab-measured properties for data clustering. We investigated potential correlations between SOM clusters with micrite morphology, which resulted in a predictable relationship following the granularity and sphericity of microcrystals with porosity, permeability, and acoustic velocity. It was possible to represent multiple lithofacies with a single log-linear porosity-permeability relationship and a single value of equivalent differential effective medium (DEM) aspect ratio in velocity-porosity space. The inter-relationship between micrite morphology and microporosity in the well was then propagated to reservoir-grid scale using inverse differential effective medium (DEM) of acoustic impedance from inverted seismic data. The methodology developed in this study thus provides a practical way to integrate key sub-grid scale micro-and macro-heterogeneities into reservoir scale property models.