Distribution Of Reservoir Properties Research Articles

Practical results of scattered waves using for prediction of fractures zones and evaluation of faults conductivity concerning oil and gas field

The main task in the modern development of the oil industry in in each area is creating a rational strategy for the exploration and hydrocarbon production. This strategy ensures an optimal balance between the required production level and explored reserves. In this regard, oil and gas geophysics requirements are constantly increasing. Geological exploration is being reoriented to search for complex oil and gas traps. Importance of the issues related to the uneven lateral distribution of reservoir properties — primarily porosity and permeability of rocks — is increasing. Standard methodological techniques used in seismic studies no longer provide reliable solutions of geological tasks.This article focuses on testing technology of fracture zone prediction in geological sections and assessing conductivity for fluids of tectonic disturbances at oil and gas fields. The solution of this task will significantly increase the efficiency of pools development concerning oil and gas fields characterized by complicated geological structure. Typically, evaluation of faults and fractures conductivity is accomplished on the basis of geological and production data. Fault conductivity and fracture zone data in explicit form is not used neither in geological modeling nor in hydrodynamic modeling. This study presents an attempt to apply the diffuse seismic wave field to predict fracture zones in the geological environment and the properties of faults at a real site. Modelling was carried out to assess the potential of this approach, confirming the technology's viability. Further, real data was processed and compared with field data. Real data was processed and compared with field data.

Application of machine learning to predict the time evolution of condensate to gas ratio for planning and management of gas-condensate fields

One of the most important parameters for the evaluation, forecast, and management of gas-condensate fields is the evolution of the condensate to gas ratio (CGR) over time. This parameter tends to decrease as reservoir pressure declines. In the conventional approach, gas and condensate samples are collected at the beginning of production and periodically later to conduct laboratory experiments on composition, CGR, and fluid properties. However, sample collection, transportation, and analysis require a lot of time and effort and could be very expensive. Likewise, dynamic models are also frequently used to predict CGR over time. However, these models could include many uncertainties due to ambiguous input data, including reservoir structures, fluid phase interaction, and reservoir property distribution. Therefore, the application of machine learning to predict the time evolution of CGR in this research could be a new and effective approach to supplement conventional methods.

Application of two classes of inverse problems for optimization and management of the natural hydrocarbon field development process

The article considers two classes of inverse problems in the modeling of hydrocarbon field development: optimization and adaptation of the development history. They jointly form the basis for field development management in a closed loop, based on a constantly updated 3D geological and technological model. The problems of model adaptation (identification) arise due to the point-by-point measurements and the inaccuracy of determining the initial data on porosity and permeability of the host rocks and other parameters of the deposit when building the 3D model. Such problems are regarded as the inverse ones, because the consequence – field measurements – is used to determine their cause. The problems under consideration are related to the identification of both porosity and permeability of the reservoir and other key parameters that determine the dynamics of development. Geologically consistent formulation ensures that the adapted model retains the original geological principles of the distribution of reservoir properties. The second class of problems under consideration is the problems of optimization (regulation) of development. One of the sought-after formulations is related to the optimal redistribution of a given target level of production from the field to all production wells in time. In this case, the technological limitations of surface equipment are taken into account. The maximized criterion reflects the economic effect of the asset development. In this article, without going into details of mathematical realization, the authors consider practical examples of solving these problems using full-scale 3D multiphase filtration models of the real fields. The algorithms based on the current methods of optimal control theory are used, which were developed and implemented by the researchers of Oil and Gas Research Institute of the Russian Academy of Sciences in the SimMatch software package and confirmed their effectiveness on numerous synthetic and real examples. Application and further development of these methods within the framework of a single loop, taking into account continuous information from the entire set of sensors, will significantly increase the efficiency of intelligent wells and field control systems.

Sequence stratigraphic model as a modern tool for seismic data interpretation

The sequence stratigraphic models for the clinoform part of the section in the territory studied by production drilling within the Nadym-Pur oil and gas region of Western Siberia are constructed. The purpose of study was to identify patterns in the distribution of reservoir properties of the Achimov objects and substantiate a systematic approach to the correlation of reflecting horizons. The main task is to identify the characteristic features of systemic tracts based on core data from a well-studied field. To solve the problem, a classification of sediments studied by core in wells was carried out within the framework of a sequence model. To confirm theoretical concepts about the formation of improved reservoir zones in system tracts, an analysis of filtration and capacitance parameters (porosity, permeability) was performed. There is the relationship between the reservoir properties and the type of system tract in which the deposits were formed. The seismic section requires the interpretation according to the principles of sequence stratigraphy, and criteria for tracing reflecting horizons. An algorithm for constructing a model is proposed that provides a forecast of zones with improved reservoir properties. A number of advantages for the unsystematic approach to correlating reflecting horizons are given.

Integrated geological data, 3D post‐stack seismic inversion, depositional modelling and geostatistical modelling towards a better prediction of reservoir property distribution for near‐field exploration: A case study from the eastern Sirt Basin, Libya

De‐risking the hydrocarbon potential in near‐field exploration is one of the most important procedures in the exploration of hydrocarbons, and it requires the integration of various data to predict the reservoir characteristics of the prospect area more accurately. In this work, wells and 3D seismic data from the Libyan producing oil fields were utilized to demonstrate how well this technique worked to improve and describe the hydrocarbon potential of the carbonate geobody that corresponds to the Palaeocene Upper Sabil Formation, which was revealed by new seismic data. This study integrates different types of data, including 3D seismic, seismic acoustic impedance, depositional history and geostatistical analysis, to predict the facies, reservoir porosity and permeability distributions and then visualize them in a 3D reservoir model. The 3D seismic data analysis revealed the presence of a clear seismic anomaly geobody (GB) that has never been penetrated by any well. The sedimentological analysis for the well adjacent to the GB indicated a deep‐water depositional environment as turbidites surrounded by deep‐water mud dominated facies. The Upper Palaeocene interval in the study area was subdivided based on the depositional facies and seismic stratigraphy into eight zones that were used to build the reservoir model framework. According to the porosity permeability relationships, the carbonate facies has been classified into five E‐Facies, that is, soft highly argillaceous limestone, hard argillaceous limestone, porous limestone (<20% porosity, and >30% shale volume), medium quality limestone (10–20% porosity, and >30% shale volume) and tight limestone (<10% porosity, and >30% shale volume). The rock physics and inversion feasibility analysis indicated that the acoustic impedance (AI) can be used to predict the porosity but not the lithology or the fluid content. The Bayesian classification has shown excellent results in predicting and modelling the reservoir facies distribution within the study area, utilizing the integration of gross depositional maps (GDEs), wells and seismic data. The reservoir quality of the GB was predicted by using the post‐stack seismic inversion, which indicated a high porosity interval (25%–30%). Moreover, the statistical analysis integrated with the well and seismic data was used to predict the GB permeability. The predicted permeability was reasonably high (40–60 mD). The final E‐facies show an excellent match with the input well data and an excellent match with the blind wells that were used for result quality control (QC) with higher vertical resolution. The developed model can be used as a guide for de‐risking the studied GB hydrocarbon potential in the studied basin, and it can be applied in other similar geological conditions worldwide for exploring underexplored reservoirs and de‐risking their hydrocarbon potential.

Lithology and Porosity Distribution of High-Porosity Sandstone Reservoir in North Adriatic Using Machine Learning Synthetic Well Catalogue

Reservoir characterization on offshore fields often faces specific challenges due to limited or unevenly distributed well data. The object of this study is the North Adriatic poorly consolidated clastic reservoir characterized by high porosity. The seismic data indicate notable differences in reservoir quality spatially. The only two wells on the field drilled the best reservoir area. Seismic data, seismic reservoir characterization, and accurate integration with scarce well data were crucial. This paper demonstrates how the application of machine learning algorithms, specifically a Deep Forward Neural Network (DFNN), and the incorporation of pseudo-well data into the reservoir characterization process can improve reservoir properties prediction. The methodology involves creating different reservoir porosity and thickness scenarios using pseudo-well data, synthetic pre-stack seismic data generation, seismic inversion, and DFNN utilization to improve porosity prediction. This study also highlights the importance of lithology discrimination in the geological model to better constrain reservoir properties distribution in the entire reservoir volume. Facies probability analysis was utilized to define interdependence between litho–fluid classes established from the well data and acoustic impedance volume. Apart from the field well data, seismic inversion results, and DFNN porosity volume as main inputs, acknowledgments from the neighboring fields also had an important role.

Facies control of the reservoir properties distribution in the carbonate rocks of epicontinental platforms

Background: It is difficult to imagine the modern world without hydrocarbon raw materials. Oil and gas production is not only the basis of a powerful fuel and energy complex but also the main source of foreign exchange earnings and a leading component of the budget for many countries. Carbonate rocks strike the imagination by their diversity, but at the same time they cause many problems: they are extremely variable from the point of view of reservoirs. The main problems of productive carbonate members are heterogeneity; distribution of reservoirs in the form of lenses with different configurations and strikes as well as hydrodynamic connections between the lenses is often chaotic. All of the listed problems of carbonate reservoirs are associated with secondary processes. Aim: The project aims to assess the reservoir properties of carbonate rocks from the point of view of facies zoning on the example of Ovinparm rocks. Materials and methods: In this work, we used: rock material from 8 wells (475 m), 14 wells with hydrodynamic data and a 3D seismic cube. Results: Based on the data obtained, the principles of facies diagnostics of the epicontinental platform deposits of were developed, facies of epicontinental platforms were identified, rock material was linked to logging data, a facies model and a facies map were built, a sequence stratigraphic analysis was carried out, and the regularity of the selective nature of the manifestation of secondary processes depending on the characteristics of the conditions for the accumulation of facies of epicontinental platforms was revealed, promising zones were identified taking into account the facies zoning and the intensity of the passage of secondary processes in the study area. Conclusion: By understanding how the reservoir properties are related to carbonate depositional conditions, we can predict the most interesting zones and determine the main secondary processes leading to such improved or degraded reservoir properties.

Efficient screening of locations with the best pressure dissemination potential in geological CO2 storage reservoirs with lithological heterogeneity

Efficient and safe disposal of CO2 in the subsurface depends on several conditions including a low-pressure build-up to avoid the risk of induced seismicity. CO2 storage reservoirs commonly comprise lithological heterogeneity which may contribute to pressure build-up during CO2 injection. The presence of certain rock types restricting CO2 flow, known as flow barriers, can limit the migration of CO2 leading to reservoir pressure build-up particularly near the injection point. Identifying suitable injection locations away from such rock types could require numerous high-fidelity numerical simulations due to the uncertainty of the distribution of such barriers. This study presents a new computationally efficient reservoir screening approach to minimize the probability of enhanced pressure build-up resulting from lithological heterogeneity near the CO2 injection point. The approach utilizes graph network algorithm to identify the path of the least resistance to CO2 flow between the injection location and the top of the reservoir. This path accounts for lithological heterogeneity and was coupled to the results from 50 reservoir-scale numerical simulations, capturing a range of reservoir property distributions, to derive a classification criterion for predicting grid-cell scale injectivity index. Testing showed that the approach could accurately predict the spatial variability of the injectivity index with a computational boost of up to 10,000 times.

Intelligent Approach for Investigating Reservoir Heterogeneity Effect on Sonic Shear Wave

Heterogeneity refers to a not uniform distribution of reservoir properties. To overcome the problem of heterogeneity, most reservoir studies split the reservoir into different zones. In general, this disparity affects all log tools. Sonic shear wave time (SSW) is a critical metric in geomechanical modeling that is strongly influenced by reservoir heterogeneity and the kind of porous fluid composition. To detect the effect of reservoir heterogeneity on SSW prediction, an artificial neural network (ANN) was applied as an intelligent technique. One Iraqi vertical well that penetrated the Asmari reservoir was selected for this study. It contains 2462 SSW measured points as well as the following seven log parameters: Gamma Ray, Caliper, Density, Neutron, Compressional sonic, and True resistivity log over measured depth. Based on formation assessment and available well data, the Asmari reservoir was classified into six zones (with different lithology and different fluid content): A, B1, B2, B3, B4, and C. To investigate the effect of lithology on SSW, two runs of ANN had been conducted in this study. Initially, we developed a single ANN for all 2462 measured points, while in the second, six ANNs were built, one for each zone. The optimum structure for all the developed ANNs was obtained with one hidden layer of 12 neurons (7-12-1). The statistical parameters used for comparison are average percent error (APE), absolute average percent error (AAPE), standard deviation (SD), mean square error (MSE), and correlation coefficient (R2). It was observed that these parameters are approximately close to each other for the developed seven ANNs. The R2 values of the seven ANNs are 0.98 for all zones, and 0.99, 0.99, 0.99, 0.99, 0.99 and 0.96 for each zone respectively. The insignificant differences of results can be explained by the fact that the log readings (i.e. inputs variables) are already reflected the effect of lithology. Therefore, we recommended using the ANN based on 2462 for predicting SSW to any lithology zone. A mathematical model for representing the suggested ANN to simplify the calculation.

Influence of facies and tectonic structure on the reservoir properties distribution

The lithofacies features influence of productive strata and a system of tectonic faults on the reservoir properties distribution of Jurassic deposits one of the area of Western Siberia was analysed. Main productive strata in this place is Tyumen suite. The facies model of field was updated according reconstruction of sedimentation environment by electrometric facies models (by Muromcev V. S), interpretation results of seismic and core descrition. Dedicated sedimentation zones show main conception of Western Siberia basin evolution in Jurassic. Lithofacies analysis allow to identify main features of sedimentation zones, find out a reasons of uneven reservoir properties distribution. Also influence of tectonic faults system on reservoir properties of productive layers was found. Data summation help update fault model and highlight new low-amplitude faults. Next comparing maps of initial and remaining recoverable reserves of oil with lithofacies and fault model of reservoir rock help identify reason of zones with high density deposits forming and develop most effictient stimulation of oil recovery.

Relaxation characteristics of core samples on the example of parametric well: database of formation properties by NMR-data

The database of relaxation characteristics of core samples from the parametric well was created. It includes their main petrophysical parameters, the NMR signal and the results of its processing and interpretation. A comparative analysis of the obtained data with the results of lithological and stratigraphic analysis was carried out. Using the example of a parametric well, it is shown that NMR data can be used to quickly obtain information on the distribution of reservoir properties of core samples both along the section as a whole and separately for each suite.

Seismic Interpretation and Reservoir Static Model: A Case Study in Block MFK, Riau Province, Indonesia

MFK Block was located between Kampar and Rokan Hulu, Riau Province and 135 from Pekanbaru City, Indonesia. There are 33 wells in Field X, MFK Block with 27 active wells. This field has an area of about 79.65 km2 that located in Central Sumatra Basin. The field was discovered in 1976 and began to be produced in January 1979. Our research is focused on AK reservoir intervals, which is also part of Bekasap formation. The main aims of this study are to interpret field structure model, determine the distribution of reservoir properties, develop static reservoir model for field as a reference for field performance enhancement, estimate oil reserves in the field reservoir, and the prospect of hydrocarbons in the AK reservoir. The available data are 2D seismic data, mudlog descriptions, well log data, and perforation data. The methods used in this study are stratigraphic sequences, electrofacies analysis, geological structure analysis, static reservoir modelling, and estimation of hydrocarbon reserve volumes. Based on mudlog and electrofacies analysis, the study interval was arranged into 2 lithofacies units, namely sand channel and sand bar. The depostion environment of Bekasap Formation is estuarine environments. The interval of the study has an association of fasies moving northeast to southwest. Based on the results of property reservoir analysis, facies that have good reservoir quality is sand channel facies. Based on the static modeling method approach, the estimated stock tank oil initially in place (STOIIP) for AK reservoir interval is 728 MBBL.

An advanced workflow to compress the uncertainties of stochastic distribution of Bahariya reservoir properties using 3D static modeling: An example from Heba Oil Fields, Western Desert, Egypt

The main objective of this paper is to construct a static model that compress the uncertainties of the stochastic distribution of the reservoir properties of the Bahariya Formation in Heba field, at the northeastern portion of the Western Desert. This model has been constructed through the integration of the interpretations of the eighteen 2D seismic sections and the analysis of well logs data for four wells (HEBA 300X, E.BAH-E−1X, E.BAH-D-1X, and HEBA 10X) drilled in the study area. This set of data was implemented in a harmonic workflow. Structural framework was the first step created on the basis of the seismic and well log interpretations. Model zonation was mainly managed by the marine flooding events took place during the Cenomanian period. The trapping faults position uncertainty has been compressed through the tying of the seismic profiles with the identified fault cuts in the well data. Effective porosity spectrum was broke up into three reservoir qualities. The results showed heterogeneous facies qualities for oil production in specific five zones in the topmost part of the Bahariya Formation. The effective porosity model was generated stochastically considering the normal distribution for each reservoir quality. Water saturation was distributed by two methods; 1) Sequential Gaussian Simulation that was co-simulated by porosity model. 2) Log-based saturation height function for each reservoir quality. This methodology provided as accurate as possible estimates for the volume calculation by quantifying the sensitivity of the important parameters such as oil contact. Additionally, the model was prepared to be used as a front end for dynamic simulation.

Distribution of Petrophysical Properties Based on Conceptual Facies Model, Mishrif Reservoir/South of Iraq

A 3D geological model is an essential step to reveal reservoir heterogeneity and reservoir properties distribution. In the present study, a three-dimensional geological model for the Mishrif reservoir was built based on data obtained from seven wells and core data. The methodology includes building a 3D grid and populating it with petrophysical properties such as (facies, porosity, water saturation, and net to gross ratio). The structural model was built based on a base contour map obtained from 2D seismic interpretation along with well tops from seven wells. A simple grid method was used to build the structural framework with 234x278x91 grid cells in the X, Y, and Z directions, respectively, with lengths equal to 150 meters. The total number of grids is (5919732) in the geological model. CPI (computer-processed interpretation) for 7 wells contain (facies, porosity, water saturation, and NTG) was imported to Petrel 2016 software. Facies log was upscaled and distributed along the 3D grid. Truncated Gaussian with trend method was used to distribute the facies taking into account the conceptual facies model of the Mishrif formation. The result shows that the trend of sedimentation suggests a retrogradation pattern from NW to SE. Facies1 (Reservoir), dominated by Limestone brown to light brown, with oil shows has good distribution within the area and thinning towards the NW. The petrophysical properties (porosity, water saturation, NTG, and permeability) were distributed using the Sequential Gaussian Simulation (SIS) method and the facies model as a guide for distribution.
 The results show that petrophysical properties enhanced in the southeast area, representing the reef region compared to the northwest side of the study area. Unit Mishrif B had the highest porosity value and lower water saturation value along the entire field. While the units Mishrif B1, B2, and B3 show a gradual decrease in reservoir properties towards the field's southeast side. The results also show that the conceptual facies model has great benefit in constructing the 3D geological model, reflecting the geological knowledge used to correctly distribute the reservoir properties (porosity and water saturation).

3D facies and reservoir property prediction of deepwater turbidite sands; case study of an offshore Niger delta field

The distribution of lithofacies and key reservoir properties such as porosity, net to gross and water saturation is essential to describing reservoir heterogeneity predicted within a clastic deep water reservoir unit in the study area. A systematic analysis of core and gamma-ray wireline logs was used to identify the reservoir unit in this study. The identified reservoir unit was mapped on 3D seismic data, and four lithological and fluid sensitive seismic attributes extracted for the reservoir were fed as inputs into an unsupervised artificial neural network (UANN) analysis to define the depositional pattern of the reservoir unit. Afterwards, a 3D sand facies trend model was built using the facies depositional pattern map and upscaled properties from well data. The generated 3D sand probability model from trend modelling was subsequently used to constrain geostatistical facies modelling of the reservoir unit. The systematic stratal analysis from core, well data and the integration of seismic attributes using UANN reveal the reservoir unit as a system of turbidite lobe sands and turbidite channel levees sands. The 3D Geostatistical facies distribution reveals spatial and vertical facies heterogeneity. It indicates high sand amalgamations and static hydraulic connectivity of turbidite lobe sands in the distal part of the study area. Further geostatistical distribution of reservoir properties conditioned to facies indicated good to excellent reservoir quality within the turbidite lobe sands, moderate to low reservoir quality within turbidite channel sands and poor reservoir quality at the turbidite channel margins and the deep-water shale facies. The 3D facies and reservoir property models built in this study would ensure optimisation of the reservoir unit, enable better estimation of hydrocarbon volumes and serve as input data for flow simulation.

Geologic Modeling and Ensemble-Based History Matching for Evaluating CO2 Sequestration Potential in Point bar Reservoirs

The target reservoirs in many CO2 projects exhibit point bar geology characterized by the presence of shale drapes that act as barriers preventing the leakage of CO2. However, the extent of the flow barriers can also impede the displacement of CO2 in such reservoirs and restrict the storage volume. Therefore, developing a framework for modeling point bars and their associated heterogeneities is crucial. Yet, for the point bar model to be geologically realistic and reliable for evaluating CO2 sequestration potential, it should be calibrated to reflect historical data (e.g., CO2 injection data). This study is therefore in two parts. The first part focusses on the modeling of point bar heterogeneities (i.e., lateral accretions and inclined heterolithic stratifications). To ensure that the heterogeneities are preserved, we implemented a gridding scheme that generates curvilinear grids representative of the point bar curvilinear geometry. We subsequently incorporated a grid transformation scheme to facilitate geostatistical modeling of reservoir property distributions. The second part of this study is a model calibration step, where the point bar model is updated by assimilating CO2 injection data, in an ensemble framework. Ensemble-Kalman Filter was used first to update ensembles of point bar geometries, to select the geometry that yields the closest match to observed data. Within this geometry, indicator-based ensemble data assimilation was used to perform updates to the ensemble of point bar permeability models. The indicator approach overcomes the Gaussian limitation of the traditional ensemble Kalman filter. The workflow was run on the Cranfield, Mississippi CO2 injection dataset. It was observed, after model calibration, that the final updated ensemble of models yields a reasonable match with the historical data. The updated models were run in a forecast mode to predict the long-term CO2 sequestration potential of the Cranfield point bar reservoir. Results demonstrate that 1) preserving the heterogeneities in the point bar modeling process, and 2) constraining the point bar model to historical data (e.g., CO2 injection data) are essential for accurately evaluating the CO2 sequestration potential in point bar reservoirs.

Deep Learning for Latent Space Data Assimilation in Subsurface Flow Systems

Summary We present a new deep learning architecture for efficient reduced-order implementation of ensemble data assimilation in learned low-dimensional latent spaces. Specifically, deep learning is used to improve two important aspects of data assimilation workflows: (i) low-rank representation of complex reservoir property distributions for geologically consistent feature-based model updating, and (ii) efficient prediction of the statistical information that are required for model updating. The proposed method uses deep convolutional autoencoders (AEs) to nonlinearly map the original complex and high-dimensional parameters onto a low-dimensional parameter latent space that compactly represents the original parameters. In addition, a low-dimensional data latent space is constructed to predict the observable response of each model parameter realization, which can serve as a proxy model in the latent space to compute the statistical information needed for data assimilation. The two mappings are developed as a joint deep learning architecture with two variational AEs (VAEs) that are connected and trained together. The training procedure uses an ensemble of model parameters and their corresponding production response predictions. Simultaneous training of the two mappings leads to a joint data-parameter manifold that captures the most salient information in the two spaces for effective data assimilation, where only relevant data and parameter features are included. Moreover, the parameter-to-data mapping provides a fast forecast model that can be used to significantly increase the ensemble size in data assimilation, without the corresponding computational overhead. We apply the developed approach to a series of numerical experiments, including a 3D example based on the Volve field in the North Sea. For data assimilation methods that involve iterative schemes, such as the ensemble smoother with multiple data assimilation (ESMDA) or iterative forms of the ensemble Kalman filter (EnKF), the proposed approach offers a computationally competitive alternative. Our results suggest that a fully low-dimensional implementation of ensemble data assimilation in effectively constructed latent spaces using deep learning architectures could offer several advantages over the standard algorithms, including joint data-parameter reduction that respects the salient features in each space, geologically consistent feature-based updates, as well as increased ensemble size to improve the accuracy and computational efficiency of calculating the required statistics for the update step.

Reservoir rock typing and electrofacies characterization by integrating petrophysical properties and core data in the Bangestan reservoir of the Gachsaran oilfield, the Zagros basin, Iran

The super-giant Gachsaran oilfield is situated in the Dezful Embayment of the Zagros basin. The main goal of this study is to characterize the reservoir rock types by integrating the available geological and petrophysical studies in the Bangestan reservoir along with adopting a way to figure out the optimal number of electrofacies codes. At the first step, the types of porosity and geological microfacies were studied by using 163 thin sections from the core samples. The microscopic studies revealed that fractures and intraparticle interstices are the most common porosities types. Also, they resulted in the identification of 4 microfacies in the Bangestan reservoir. In a complementary study, the Velocity-Deviation Log (VDL) was employed to determine the dominant pore types. By comparison of the VDL values and thin section studies’ results, it was deduced that high micro-porosities along with hairline fractures are the predominant pore types of the Bangestan reservoir. At the second step, the optimal number of reservoir rock types (RRT) was determined by using the concept of hydraulic flow units. Thus, six flow units were distinguished by utilizing the Flow Zone Indicator (FZI) method in which the reservoir properties were improved from RRT-1 toward RRT-6. In the third step, the data clustering analysis, known as the electrofacies analysis, was employed to identify the accurate petrophysical rock types based on the meaningful segregation of capillary pressure curves within each electrofacies code and then propagated in non-cored intervals and boreholes. In this research, an optimal number of 7 electrofacies were identified by employing the petrophysical curves including effective porosity (PHIE), bulk density (RHOB), sonic (DT), and water saturation (SWE), along with considering the number of reservoir rock types and the defined geological microfacies, as well as the relations of the available capillary pressure data within different electrofacies codes. The validity of the proposed electrofacies was scrutinized through petrophysical results including shale volume, water saturation, and effective porosity. In the end, the electrofacies codes were compared to the defined geological microfacies. Finally, this study shows that the correlation of capillary pressure data and electrofacies codes can be resulted in figuring out the optimal number of electrofacies codes. In addition, by trying to honor the link between geological microfacies, capillary pressure, and petrophysical curves, the constructed electrofacies model, as a suitable practical integrated rock typing method, can be the foundation of property modeling, guide the spatial distribution of reservoir properties in the 3D model, aids to model the dynamics of fluid flow more realistically in reservoir simulation phase, and ultimately be beneficial for further development and production decisions in the Bangestan reservoir of the Gachsaran oilfield.

Microscopic sand production simulation and visual sanding pattern description in weakly consolidated sandstone reservoirs

To visually describe the sanding pattern, this study constructs a new particle-scale microstructure model of weakly consolidated formation, and develop the corresponding methodology to simulate the sanding process and predict sand cavity shape. The microstructure model is a particle-objective model, which focuses on the random sedimentation of every sand grain. In the microstructure, every particle has its own size, sphericity and inclination angle. It is used to simulate the actual structure of cemented granular materials, which considers the heterogeneity and randomness of reservoir properties, provides the initial status for subsequent sanding simulation. With the particle detachment criteria, the microscopic simulation of sanding can be visually implemented to investigate the pattern and cavity shapes caused by sand production. The results indicate that sanding always starts initially from the borehole border, and then extends along the weakly consolidated plane, showing obvious characteristic of randomness. Three typical microscopic sanding patterns, concerning pore liquefaction, pseudo wormhole and continuous collapse, are proposed to illustrate the sanding mechanism in weakly consolidated reservoirs. The nonuniformity of sanding performance depends on the heterogeneous distribution of reservoir properties, such as rock strength and particle size. Finally, the three sanding patterns are verified by visually experimental work. The proposed integrated methodology is capable of predicting and describing the sanding cavity shape of an oil well after long-term sanding production, and providing the focus objective of future sand control measure.

Feature-based ensemble history matching in a fractured carbonate reservoir using time-lapse deep electromagnetic tomography

Carbonate reservoirs typically exhibit very complex geological structures and are characterized by flow dynamics primarily occurring in fractures. The intricate network of fractures as well as their interconnectedness may lead to unexpected flow patterns and uneven sweep efficiency. Determining reservoir properties of both matrix and fracture channels is quintessential for accurately tracking the fluid front movement in the reservoir, optimizing sweep efficiency, and maximizing hydrocarbon production. In this study, we showcase the application of a feature-oriented ensemble-based history matching workflow to a complex fractured carbonate reservoir box model, focusing on the use of formation resistivity tomography data that are usually inferred from deep crosswell electromagnetic (EM) surveys. Compared with the production data that are commonly used in history matching, deep EM measurements provide additional information about the spatial distribution of subsurface reservoir properties in the interwell volumes by exploiting the strong resistivity contrast between water and hydrocarbons. A hybrid parameterization approach is used to represent the multiscale fracture distribution in which the spatial distribution of small-scale fractures is modelled by a truncated Gaussian simulation method. A large number (over one million) of uncertain model parameters including reservoir matrix and fracture properties as well as Archie's parameters are identified and updated by an iterative ensemble smoother. For an efficient integration of the high-dimensional and noisy EM tomography data, the boundary or contour information extracted from the EM resistivity field is instead assimilated through a distance parameterization approach. A modified bootstrap-based localization is proposed to regularize the model updates adaptively during the iteration to reduce sampling errors. Especially, to improve the computational efficiency in dealing with the large dimensions of both data and model parameters, the localization is implemented in a projected low-dimensional data subspace. Experimental results demonstrate the applicability and efficiency of the developed workflow for reservoir history matching in more realistic model settings. The comparative case study also illustrates the significance of jointly incorporating multiple sources of data for better quantification of model uncertainty, and the great potential of deep EM data for enhancing the characterization of complex fractured carbonate reservoirs.