Modeling Of Reservoir Properties Research Articles

Data-driven multiscale geomechanical modeling of unconventional shale gas reservoirs: a case study of Duvernay Formation, Alberta, West Canadian Basin

The Duvernay Formation in Canada is one of the major oil and gas source formations in the Western Canadian Sedimentary Basin, located at its deepest point. While it demonstrates promising development potential, challenges arise in the urgent need for integration of geology and engineering models, as well as in optimizing sweet spots, particularly as infill wells and pads become central operational objectives for the shale gas field. A lack of the geomechanical understanding of shale gas reservoirs presents a significant obstacle in addressing these challenges. To overcome this, we implemented data acquisition and prepared historical models and profiles, resulting in an extended high-resolution geological and reservoir property model with a fine grid system. Subsequently, a 3D full-field multi-scale geomechanical model was constructed for the main district by integrating seismic data (100 m), geological structures (km), routine logs (m), core data (cm), and borehole imaging (0.25 m), following a well-designed workflow. The predicted fracturability index (brittleness) ranges from 0.6 to 0.78, and a lower horizontal stress difference (STDIFF) is anticipated in the target formation, Upper Duvernay_D, making it a favorable candidate for hydraulic fracturing treatment. Post-analysis of the multi-disciplinary models and various data types provides guidelines for establishing a specific big database, which serves as the foundation for production performance analysis and aggregate sweet spot analysis. Fourteen geological and geomechanical candidate parameters are selected for the subsequent sweet spot analysis. This study highlights the effectiveness of multi-scale geomechanical modeling as a tool for the integration of multi-disciplinary data sources, providing a bridge between geological understanding and future field development decisions. The workflows also offer a data-driven framework for selecting parameters for sweet spot analysis and production dynamic analysis.

Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production

Few-shot learning and modeling of 3D reservoir properties for predicting oil reservoir production

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

ABSTRACTWe present a methodology for seismic inversion that generates high‐resolution models of facies and elastic properties from pre‐stack data. Our inversion algorithm uses a transdimensional approach where, in addition to the layer properties, the number of layers is treated as unknown. In other words, the data itself determine the correct model parameterization, that is, the number of layers. The reversible jump Markov Chain Monte Carlo method is an effective tool to solve such transdimensional problems as it generates models of reservoir properties along with uncertainty estimates. However, current implementations of the reversible jump Markov Chain Monte Carlo algorithms do not account for the non‐Gaussian and multimodal nature of model parameters. The target elastic reservoir properties generally have multimodal and non‐parametric distribution at each location of the model. The number of modes is equal to the number of facies. Taking these factors into account, we extend the reversible jump Markov Chain Monte Carlo algorithm to simultaneously invert for discrete facies and continuous elastic reservoir properties. The proposed extension to the algorithm iteratively samples the facies, by moving from one mode to another, and elastic properties by sampling within the same mode. The integration of facies classification within the inversion reduces non‐uniqueness, improves convergence speed and produces geologically consistent results. The workflow uses machine learning to generate probabilistic priors for the model parameters. We validate our approach by applying it to a synthetic dataset generated from a well log with two facies and then to a complex synthetic two‐dimensional model involving three facies having overlapping elastic property distribution. Finally, we apply our algorithm to a field dataset acquired over an unconventional reservoir. Our algorithm demonstrates the usefulness of incorporating facies information in seismic inversion and also the feasibility of inverting for facies from seismic data.

Integration of geoscience data to delineate quality of the Asmari reservoir, Iranian part of the Persian Gulf basin

The Hendijan Field is an asymmetric structure located in the northwestern Persian Gulf and consists of several oil and gas-bearing intervals. Oligocene to Miocene successions in the field host large volumes of hydrocarbons. The Asmari Formation comprises the mixed siliciclastic and carbonate members formed in a shallow marine carbonate ramp depositional environment. The siliciclastic part displays the best reservoir quality within the Asmari Formation. Through petrophysical, sedimentological, and geophysical analysis, this study divides the Asmari Formation into six, third-order depositional sequences. By doing so, it clarifies the relationships between depositional sequences and reservoir characteristics, particularly porosity and water saturation. Detailed petrographical analysis, core description, well log analysis, and seismic interpretation distinguish eight microfacies, two petrofacies, and six third-order sequences within the Asmari Formation. 3D reservoir-property models are developed that incorporate elements of the sequence stratigraphical framework. Within the six identified sequences, high reservoir quality zones are associated with AS-4 and AS-5, which comprise mostly grain-supported lithologies associated with shoal facies. The supratidal, intertidal, and lagoonal facies of AS-2, which are often associated with the occurrence of anhydrite and mud-supported fabrics, reflect lower reservoir quality. Hydraulic flow units (HFU) are delineated to assist in characterizing reservoir heterogeneity and porosity/permeability prediction. Five HFU were identified by applying the flow-zone-indicator method to evaluate reservoir quality. Both the transgressive system tract (TST) of AS-2 and early TST plus late highstand system tract (HST) of AS-6 contain elements of HFU-A. The most productive units of AS-4 and AS-5 belong to HFU-E.

Petrophysical Evaluation of H-field, Niger Delta Basin for Petroleum Plays and Prospects

Abstract in English As a result of a combination of several methodologies, the H-Field, Niger-Delta, has been delineated. To identify probable reservoirs, seals, and source rocks in the study area, this study integrates sequence stratigraphy with petrophysical concepts through a comprehensive analysis of 3D seismic and well logging datasets. According to the 3D facies model, reservoirs are dominated by sand and are laterally extensive. They were then upscaled and stochastically distributed to create a 3D reservoir property model. On average, the porosity of these clastic reservoirs ranges from 22 to 28%. Reservoir net-to-gross (NTG) ratios range from 0.67 to 0.96. Water saturation ranges from 8% to 30%, while hydrocarbon saturation ranges from 70% to 92%. Four gas-bearing units and six oil-bearing units are present in reservoirs P 0.5 and P 1 compartments. All reservoir intervals’ oil and gas volumes are evaluated based on the hydrocarbon distribution. Geological models of the subsurface, such as the one in this paper, are crucial for future reservoir development programs.

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.

The reservoir property modeling of volcanic reservoir: An application to the Shahejie Formation, Zaoyuan oilfield, eastern China

As an increasing number of volcanic reservoirs has been discovered all over the world, the study of volcanic reservoirs has caught more attention. However, researchers mainly focus on the characterization of the external architecture of volcanic rock mass and the superimposed relationship between the rock mass, while paying little attention to the fine anatomy of the internal structure of the rock mass, making it difficult to effectively guide the development of the volcanic reservoir. Taking Zao35 block in Huanghua depression, Bohai Bay Basin, China, as an example, we find a workflow of establishing a high-resolution volcanic stratigraphic structure model and reservoir properties models by integrating well- and seismic-data analysis. The seismic data indicate that the volcanic activities consist of three stages: Stages I and III have positive seismic amplitude reflections, whereas stage II has a negative seismic reflection. The well data indicate that the lithology column of target formation in our study area consists of eight volcanic strata and seven mudstone strata. According to the lithology interpreted using well data, stages III, II, and I consist of five, one, and two volcanic eruption periods, respectively. We have established a structure and stratigraphy model with eight zones of volcanic rock and seven zones of mudstone barrier (15 zones in total) to effectively distinguish volcanic rocks (products of the eruption period) from sedimentary rocks (products of the volcanic dormant period). Based on the seismic attribute analysis, we have combined the sequential indicator simulation and local anisotropy from the seismic amplitude attribute. The model of volcanism can be reproduced by sequential indication simulation of local variogram with variable range directions. Based on the principle of “facies control” (perform lithology simulation in different regions according to lithofacies), we have used the sequential indication method to simulate volcanic rocks. The simulation results have a very good agreement with the shape of lava flows spreading around the crater. To describe the internal structure of the volcanic rock body, reveal internal lithology distribution characteristics, and reservoir physical properties, we provide an effective research method for the development of volcanic reservoir geologic research.

Numerical modeling and laboratory testing of reservoir properties of coal

Numerical modeling and laboratory testing of reservoir properties of coal

Geostatistics assisted by machine learning for reservoir property modeling: A case study in presalt carbonates of Buzios Field, Brazil

An embedded model estimator (EMBER) petrophysical modeling algorithm has been applied to obtain effective porosity and permeability within the presalt carbonate reservoirs of the Barra Velha Formation in Buzios Field, Santos Basin. This advanced methodology was used due to the heterogeneity and complexity of the reservoirs, which makes modeling by conventional geostatistical methodologies difficult. For effective porosity modeling, we chose one facies model, one stratigraphic seismic attribute (acoustic impedance), and one structural seismic attribute (local flatness) as secondary variables. Permeability was modeled by using the best effective porosity simulation result as a secondary variable. Our results demonstrate that average effective porosity and permeability were 0.10 v/v and 440 md, respectively, indicating good reservoir quality throughout the studied area. A vertical trend of high effective porosities and permeabilities for the basal and uppermost reservoir sections was identified in our results, as well as a trend with lower values for these reservoir properties for the intermediate reservoir section. The lower section of the formation presented more continuity, and we infer it to be the best reservoir interval. We observed two horizontal trends for these reservoir properties at the formation top: one of higher values aligned to the north–south direction at the structural highs and another of lower reservoir properties related to isolated structural lows within structural highs. Correlation between modeled results and the blind test ANP-1 well upscaled properties was high, and upscaled well-log property distributions were preserved in the EMBER simulations, proving the predictive capacity of the algorithm. Finally, conditional distributions analysis indicated that the basal section of the Barra Velha Formation presents higher uncertainty for the estimation of effective porosity. Even though this interval is considered to have the best reservoir characteristics, decision making should be done with caution for this section.

Integrated processing and interpretation to image and derisk a carbonate reservoir clouded by shallow gas — A case study from offshore Vietnam

Carbonate reservoirs are increasingly becoming an important resource for hydrocarbon production because they contain the majority of remaining proven oil and gas reserves. In this context, carbonate reservoirs could represent new opportunities; however, there is still a lack of understanding of their subsurface status and characterization. Carbonate reservoirs are more difficult to evaluate than their siliciclastic counterparts because many aspects of carbonate rocks make their seismic image signature complex and difficult to interpret. Moreover, the presence of complex overburden such as shallow gas accumulation can exacerbate amplitude and phase fidelity at the reservoir, which introduces an additional imaging challenge. This makes field development of carbonate reservoirs extremely difficult because field development requires detailed delineation of characteristic karst features to avoid drilling hazards and sudden water breakthrough. In this paper, we demonstrate that a tight integration of signal processing, depth model building, and imaging, as well as near-real-time seismic interpretation feedback, is the key to success for imaging complex carbonate reservoirs with overburden challenges. Our findings show that such an integrated approach can result in a substantially better image, reduced depth uncertainty, and better delineation of karst and fractures. It can also aid in well placement and improve reservoir property modeling.

Modeling of reservoir properties using seismic attributes and evaluation of well logs: a case study in Bauna and Piracaba Oil Fields, Santos Basin, Brazil

Modeling of reservoir properties using seismic attributes and evaluation of well logs: a case study in Bauna and Piracaba Oil Fields, Santos Basin, Brazil

Static Reservoir Modeling of the Eocene Clastic Reservoirs in the Q-Field, Niger Delta, Nigeria

This work integrates seismic and well log data to establish a 3D reservoir model of the Q-Field, which is a prolific onshore hydrocarbon field situated in the Central Swamp Depobelt of Nigeria. The subsurface modeling focuses on the four principal clastic reservoir intervals of the Agbada Formation (D6200, D7000, D9000 and E2000), which was deposited in a deltaic to fluvio-deltaic system during Eocene. The seismic-based structural modeling inferred an extensional set-up dominated by NW–SE trending normal faults. Reservoirs are sand-dominated and laterally extensive, as interpreted from the 3D facies model. Well log-based petrophysical parameters were up-scaled and distributed stochastically using the Sequential Gaussian Simulation method to generate a 3D reservoir property model. Lateral and vertical heterogeneities of the reservoir properties were inferred from the 3D models. In general, the clastic reservoirs exhibit 18–22% porosity, 62–105 mD permeability, moderate to good net-gross thickness, and 36–74% water saturation. Hydrocarbon accumulation was primarily restricted within the anticlines. Gas-down-to exists in the upper three reservoirs (D6200, D7000 and D9000) at 10,577 ft (1 ft = 0.3048 m), 10,756 ft and 11,389 ft, respectively. Gas–oil and oil–water contacts in the E2000 reservoir were interpreted to be at 11,812 ft and 11,886 ft, respectively. Based on the hydrocarbon distribution, oil and gas-in-place volumes were estimated for all the reservoir intervals. The comprehensive 3D modeling work addressed the spatial distribution of the studied reservoir properties and can be directly useful for planning better the future wells for field development.

Geoscience platform for reservoir property modeling automation using AI: a case study in a pre-salt field in the Santos basin

Geoscience platform for reservoir property modeling automation using AI: a case study in a pre-salt field in the Santos basin

Analysis of the relationship between porosity and permeability in reservoir modeling using the petrophysical rock type approach

The carbonate reservoir is one of the reservoir characters found in hydrocarbon fields in Indonesia. Carbonate reservoirs have complex porosity and permeability relationships. So it is necessary to do a special reservoir character that is different from the siliciclastic reservoir. Efforts that can be made to assist the development of this hydrocarbon field are to analyze the reservoir character in more detail using the petrophysical rock type (PRT) approach. This approach is used by combining geological elements such as the depositional environment, the petrophysical properties of the rock, as well as the fluid flow in it which is reflected by capillary pressure and water saturation. Modeling with this method is expected to be a method that can increase hydrocarbon production optimally in Xena Field. The object of research from Xena Field is Zone A2 which is included in the Parigi Formation. The Parigi Formation is one of the main hydrocarbon-producing reservoirs. The data used in this study are routine core analysis (RCAL) rock data on JLB-07, JLB-08, JLB-02, JLB-23 wells, wire log data (gamma-ray log, resistivity log, density log, neutron log) of 30 wells, and 2D seismic data. The depositional facies are divided into 2 facies, namely the margin reef platform facies and the interior platform facies. Identification of rock type (RT) using the flow zone indicator (FZI) method. The rock type in this field can be divided into 4 rock types, namely RT 1, RT 2, RT 3, RT 4 with RT 1 being able to drain the best fluid and RT 4 to drain the worst fluid. Reservoir property modeling is controlled by facies and rock type (RT) models. The margin reef platform facies are associated with RT 1 and RT 2. The interior platform facies are associated with RT 2 and RT 3.

Multidisciplinary Characterization of Unconventional Reservoirs Based on Correlation of Well and Seismic Data

Combinatorial analysis of key petrophysical parameters can provide valuable information about subsurface hydrocarbon reservoirs. This is particularly important for reservoirs with unconventional rock formations that, due to the low permeability, need to be stimulated by fracturing treatment to provide fluid flow to the exploitation wellbore. In this article, based on data from unconventional shale formations (N Poland), we outline how independent sets of elastic and petrophysical parameters and other reservoir features can be co-analyzed to estimate the fracture susceptibility of shale intervals, which are characterized by a high total organic carbon (TOC) content and high porosity. These features were determined by analysis of each horizon’s elastic and mineralogical brittleness index (BI). These two variants were calculated first in 1D; integrated with the seismic data and finally compared with other parameters such as acoustic impedance, ratio of compressional and shear wave velocities, porosity, and density; and then presented and analyzed using cross plots that highlighted the key relationships between them. The overall BI trends were similar in both horizontal and vertical directions. The highest BI values were observed in the southeast of the analyzed area (Source I) and in the southeast and northwest of the area (Source II). These results can form the basis for predictive modeling of reservoir properties aiding effective reservoir exploration.

Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods

Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent.

Synergistic integration of seismic and geologic data for modeling petrophysical properties

Reservoir characterization and modeling have become increasingly important for optimizing field development. Optimal valuation and exploitation of a field requires a realistic description of the reservoir, which, in turn, requires integrated reservoir characterization and modeling. An integrated approach for reservoir modeling bridges the traditional disciplinary divides and tears down interdisciplinary barriers, leading to better handling of uncertainties and improvement of the reservoir model for field development. This article presents the integration of seismic data using neural networks and the incorporation of a depositional model and seismic data in constructing reservoir models of petrophysical properties. Some challenging issues, including low correlation due to Simpson's paradox and under- or overfitting of neural networks, are mitigated in geostatistical analysis and modeling of reservoir properties by integrating geologic information. This article emphasizes the integration of well logs, seismic prediction, and geologic data in the 3D reservoir-modeling workflow.

Characterization of fluvio-deltaic gas reservoirs through AVA deterministic, stochastic, and wave-equation-based seismic inversion: A case study from the Carnarvon Basin, Western Australia

Multiple state-of-the-art inversion methods have been implemented to integrate 3D seismic amplitude data, well logs, geologic information, and spatial variability to produce models of the subsurface. Amplitude variation with angle (AVA) deterministic, stochastic, and wave-equation-based amplitude variation with offset (WEB-AVO) inversion algorithms are used to describe Intra-Triassic Mungaroo gas reservoirs located in the Carnarvon Basin, Western Australia. The interpretation of inverted elastic properties in terms of lithology- and fluid-sensitive attributes from AVA deterministic inversion provides quantitative information about the geomorphology of fluvio-deltaic sediments as well as the delineation of gas reservoirs. AVA stochastic inversion delivers higher resolution realizations than those obtained from standard deterministic methods and allows for uncertainty analysis. Additionally, the cosimulation of petrophysical parameters from elastic properties provides precise 3D models of reservoir properties, such as volume of shale and water saturation, which can be used as part of the static model building process. Internal multiple scattering, transmission effects, and mode conversion (considered as noise in conventional linear inversion) become useful signals in WEB-AVO inversion. WEB-AVO compressibility shows increased sensitivity to residual/live gas discrimination compared to fluid-sensitive attributes obtained with conventional inversions.

Reservoir properties distribution in the framework of sequence stratigraphic units: A case study from the Kangan Formation, Iranian offshore gas field, the Persian Gulf basin

The Lower Triassic Kangan and Upper Permian Dalan Formations have large volumes of gas and condensate reservoirs in the southern part of Iran and nearby countries. Shallow carbonate and evaporate succession of the gas bearing Kangan Formation in the Salman field was deposited in a shallow water carbonate ramp. The Salman field is a shared structure from which two-thirds is owned by Iran and the remaining portion is under territories of United Arab Emirates (UAE). The Kangan reservoir is traditionally subdivided into K2 and K1 zones. However, it has been divided into two depositional sequences based on the integration of seismic data criteria which are supposed to be a third-order sequence. In this study, the relationship between depositional sequences and reservoir properties have also been investigated. This paper mostly aims at finding and documenting the relation between depositional sequences and reservoir properties as well as constructing the related 3D geological model. Using and comparing three-dimensional (3D) seismic data and five well data particularly petrographic studies allow the recognition of 9 microfacies and two 3rd order sequences which are part of the Permian–Triassic Khuff Formation. In this study, three-dimensional models of sequences framework were constructed based on seismic and well data. Moreover, porosity and water saturation property models have constructed considering the sequence stratigraphic framework. According to the models, the high reservoir quality units mainly consist of grain-dominated rock fabrics, which are associated with shoal facies developed in the Late TST (transgressive system tract) and Early HST (highstand system tract). These results indicated high energy conditions in the transgressive sea-level rise. The supratidal, intertidal and lagoonal facies, presenting anhydrite and mud dominated rock fabrics showing a lower reservoir quality developed in the Late HST and Early TST. Therefore, the reservoir quality of HST is lower than TST. The reservoir quality of K2 is also much better than K1 and it is considered as the most prolific pay zone in the South Pars gas field. Based on the findings, the sequence stratigraphy framework in reservoir characterization plays a significant role in the better understanding of reservoir quality as well as reservoir property modeling.

Reservoir simulation study of enhanced oil recovery by sequential polymer flooding method

As a reservoir reaches depletion stage there will still be a varying amount of residual oil saturation in the reservoir pore volume that is too heavy to move and produced. This undesirable phenomenon occurs due to adverse mobility and/or viscosity ratio between displacing phase (water) and displaced phase (residual oil). A solution to that is polymer injection which has been considered as one of the most effective and successful method to improve oil recovery. Moreover polymer flooding also showed limited success when applied in heterogeneous reservoir. This is because single polymer injection cannot sweep entire porous media efficiently, therefore in this research work the effectiveness of sequential polymer injection is studied and compared with conventional single polymer injection. The reservoir property model was developed from the available well data of an example field using stochastic approach. From the base case two more property models were developed to cover the heterogeneity from slightly to very heterogeneous reservoir rock. The degree of heterogeneity was obtained for each property model using Dykstra-Parson technique. The magnitude of Dykstra-Parson coefficient for Slightly, Moderate and Very heterogeneous were 0.24, 0.59 and 0.69, respectively. Our results indicated that injecting polymers sequentially with varying in concentrations is an effective technique for enhanced oil recovery in heterogeneous reservoir rock. Cited as : Sidiq, H., Abdulsalam, V., Nabaz, Z. Reservoir simulation study of enhanced oil recovery by sequential polymer flooding method. Advances in Geo-Energy Research, 2019, 3(2): 115-121, doi: 10.26804/ager.2019.02.01