Modeling Of Reservoir Properties Research Articles

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

Multivariate stochastic seismic inversion with adaptive sampling

We have developed a fully coupled categorical-multivariate continuous stochastic inversion with a combined petro-elastic model and convolution. The new multivariate stochastic seismic inversion approach simulates multiple reservoir properties simultaneously and conditions them to the well and seismic data at the same time through the close integration of multivariate geostatistical modeling and stochastic inversion. This approach combines a trace-by-trace (column-wise) adaptive sampling algorithm with multivariate geostatistical techniques to select reservoir properties that match the seismic data. The adaptive sampling method uses an acceptance-rejection approach to condition geostatistical models to the well and seismic data. The adaptive sampling algorithm defines a practical stopping criteria based on the inherent uncertainty due to modeling assumptions and the size of the uncertainty space. This technique samples the realizations inside the space of uncertainty; the number of realizations attempted increases with the size of the space of uncertainty. Characterizing multiple reservoir properties simultaneously through the close integration of seismic inversion and multivariate geostatistical techniques leads to improved high-resolution reservoir property models that reproduce the original seismic data. A case study is considered to compare the proposed stochastic inversion approach with the conventional methods. The case study represents multivariate stochastic inversion provides high-resolution facies and reservoir physical properties simultaneously that reproduce the original seismic data within quality of data better than the other approaches.

Stochastic inversion of facies and reservoir properties based on multi-point geostatistics

A stochastic inversion method that can provide reservoir models of facies and reservoir properties is proposed for seismic reservoir characterization. It is an iterative optimization process achieved by the probability perturbation method on the basis of conditional simulation with multi-point geostatistics, and it also incorporates a quantum annealing algorithm to improve the accuracy of inverted reservoir properties. The usual practice of stochastic inversion based on multi-point geostatistics is time-consuming and only generates the inverted facies. The probability perturbation method can help to reduce the number of simulations and speed up the inversion process. An inverted model of facies is generated, and a volume of pseudo reservoir properties met with a fixed error is also produced in this stage. However, the continuity of this volume is poor because random sampling is used when transforming facies models into reservoir property volumes. Moreover, the characteristics of reservoir properties in the same facies cannot be reflected well. We extract the volume of pseudo reservoir properties as an initial model for optimization to improve the inversion accuracy of reservoir properties. A quantum annealing algorithm is utilized for its fast convergence rate and ability to avoid falling into a local minimum. The presented method can produce fine-scaled reservoir models of facies, and produce reservoir properties in a reasonable amount of computation time. Tests on model data demonstrate the feasibility and reliability of this technique. Facies and porosity inversion results with relatively higher accuracy are acquired, which verifies the advantages and feasibility of the proposed method. Successful application to field data further validates the effectiveness of this method.

Study on facies-controlled model of a reservoir in Xijiang 24-3 oilfield in the Northern Pearl River Mouth Basin

To better understand the internal structure of the Xijiang 24-3 Oilfield in the Northern Pearl River Mouth Basin, a reasonable and effective three-dimensional quantitative geological model is needed to characterize the distribution characteristics of the reservoir. Xijiang 24-3 Oilfield is a braided river delta deposition, which contains many sedimentary microfacies in three subfacies zones. The reservoir structure is complex and heterogeneous. The reservoir modeling research was conducted on the Xijiang 24-3 oilfield H3A reservoir. Analysis of the oilfield core, the well-logging, and seismic data, establish a regional structural model. The regional sedimentary microfacies model is simulated by multi-point geostatistics. The reservoir property model is established by using facies-controlled modeling technology with sedimentary microfacies as a constraint condition. A facies-controlled property model and a non-facies-controlled property model are established respectively. The facies-controlled model corresponds well with the sedimentary microfacies of the reservoir, and the variation trend of the model shows a higher coincidence rate with the distribution characteristics of the well. The facies-controlled model reflects the spatial distribution characteristics of the physical properties of the underground reservoir and is in accord with the actual geological understanding of the reservoir. This model also provides a reliable basis for the next stage of reservoir description and a basis for further exploration and development work. Cited as : Zheng, L., Chen, C., Lu, C., Cheng, M. Study on facies-controlled model of a reservoir in Xijiang 24-3 oilfield in the Northern Pearl River Mouth Basin. Advances in Geo-Energy Research, 2018, 2(3): 282-291, doi: 10.26804/ager.2018.03.06.

Reservoir description and characterization of Eni field Offshore Niger Delta, southern Nigeria

Integration of structural and horizon mapping of 3D seismic volume, petrophysical studies of over sixty (60) wireline logs, stratigraphic analyses, reservoir property modeling and production information have been adopted to study Eni field that has been experiencing production decline with increase in water output. Generated reservoir structural framework and spatial reservoir property distribution have proved useful to guide the optimal placement of proposed wells and also provide information needful for the development of best production plan that would guarantee effective oil drainage from the delineated reservoir compartments.

Probabilistic Reservoir-Property Modeling Jointly Constrained by 3D-Seismic Data and Hydraulic-Unit Analysis

SummaryWe present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock-physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic-unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D-seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.

Sedimentary Microfacies and Porosity Modeling of Deep‐Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data: An Example from Unit I of Gas Field A, South China Sea

AbstractSandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three‐dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square (RMS) amplitude performed the best The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified: debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well‐logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies‐controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single‐well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

Comparação dos Resultados das Inversões Acústicas Determinística e Geoestatística de um Campo de Petróleo

The most effective way to integrate seismic data in the reservoir characterization process is through the generation of impedance models derived from seismic inversion. In this work we compared the deterministic and geostatistical seismic inversion results of an oil field in order to improve the characterization of the field and generate a more accurate model, where the field behavior predictions could be done in a more effective way. The deterministic acoustic inversion is a widely used technique, which yields a single result, inverting the seismic data available for acoustic parameters (P-impedance). Geostatistical acoustic inversion generates multiple models of reservoir properties (elastic, lithology and petrophysical parameters), all of them equally probable, leading to the possibility of quantifying the uncertainty surrounding the reservoir model being created.

Integrating 3D seismic data and hydraulic units to improve reservoir property models

SUMMARY We present a new method for reservoir property modeling based on integration of 3D seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic unit analysis with a set of techniques for seismic reservoir characterization including: rock physics analysis, Bayesian inference, pre-stack seismic inversion and geostatistical simulation of reservoir properties. Hydraulic units characterize regions and properties of fluid flow in porous permeable media, and are defined at well locations. However, usually the number of wells and their lateral coverage is extremely limited. In contrast, the lateral resolution of 3D seismic data is excellent, and this can be used to extend hydraulic unit analysis away from well locations into the 3D reservoir volume. We develop a probabilistic relationship between each of the hydraulic units defined at well locations, and the 3D seismic information. Reservoir models jointly constrained by 3D seismic and hydraulic unit analysis can therefore be useful to improve the production history matching process.

Simultaneous optimization of multiple objective functions for reservoir modeling

We have developed a new method, a synthetic validity test, and a field data demonstration for constructing reservoir models that simultaneously match available seismic, borehole, and geologic data in an optimal way. We combine geostatistical simulation with simultaneous nonlinear stochastic optimization of multiple objective functions, and we do not require the selection of weighting schemes or multiple optimization computational runs. Because each geologic and geophysical data set has its own strengths and weaknesses, realistic models are best obtained by simultaneously matching all of the multiple data constraints. We define a set of objective functions for each of the multiple data sets that are used to constrain the reservoir-modeling optimization. In the examples, we specify an [Formula: see text]-norm misfit to match inverted seismic attribute volumes, an [Formula: see text]-norm to match lithofacies and porosity logs, and the Hausdorff metric to match image patterns in geologic map-based information. We use a geostatistical technique to initialize a population (ensemble) of starting models and run a nonlinear evolutionary algorithm to drive the ensembles of model solutions toward the Pareto optimal front, along which represents the best-compromise set of model solutions that simultaneously satisfy all of the multiple objectives. We tested our method on a 3D synthetic object-oriented reservoir model for which variogram-based simulation techniques typically fail to reproduce realistic models. We applied our method on a producing reservoir located offshore Western Australia. Our results indicate that the new method is capable of producing optimal models of reservoir properties and flow-unit connectivity that are consistent with known reservoir information, including dynamic production data such as pressure interference tests, and 4D seismic monitoring data.

A Best Practice in Static Modeling of a Coalbed-Methane Field: An Example From the Bowen Basin in Australia

Summary The development of a coalbed-methane (CBM) field in its early stage is often plagued by the lack of well control and the scarcity of geological data across a large geographical area. Therefore, constructing a representative static model to estimate the in-place volume presents a formidable challenge. In this paper, we propose a work flow to overcome this challenge and apply it to a CBM field in the northern Bowen basin of Australia. One may consider this work flow as a best practice for the following reasons. First, it makes use of data from various sources including cores, well logs, seismic interpretation, and topography. Second, it performs rigorous quality control on these data, such as depth shift and log normalization. Third, coal-ply division and correlation and subsequent structural modeling are based on three types of correlation: well-to-well, well-to-seismic, and well-seismic-geographic information system. Fourth, it establishes the low, base, and high trends for the most-important reservoir properties. Fifth, it constructs a base-case static model by combining the aforementioned structural and reservoir-property models. Sixth, it uses sensitivity analysis, which varies one reservoir parameter at a time, to rank the impact of reservoir parameters on in-place volume. Seventh, it uses uncertainty analysis that varies all reservoir parameters simultaneously to arrive at the P10, P50, and P90 in-place volumes and their corresponding static models that one can use for reservoir simulations to estimate the recoverable volumes.

DEPOSITIONAL FACIES, PORE TYPES AND ELASTIC PROPERTIES OF DEEP‐WATER GRAVITY FLOW CARBONATES

Quantitative petrographic analyses of deep‐water resedimented carbonates from the Gargano Peninsula (SE Italy) were integrated with petrophysical laboratory measurements (porosity, P‐and S‐wave velocities) to assess the impact of sedimentary fabrics and pore space architecture on velocity‐porosity transforms. Samples of Upper Cretaceous carbonate came from the Monte Sant'Angelo, Nevarra and Caramanica Formations and can be classified into four depositional facies associations: F1, lithoclastic breccias; F2, bioclastic packtones to grainstones; F3, interbedded grainstones‐packstones and wackestones; and F4, (hemi‐) pelagic mudstones. Five pore type classes were distinguished: I and II, dominant intercrystalline microporosity; IIIa, dominant intergranular macroporosity; IIIb, dominant mouldic macroporosity; and IIIc, mixed intergranular and mouldic macroporosity. Pore type was found to strongly control velocity‐porosity transforms, unlike depositional facies associations.The equivalent pore aspect ratio (EPAR), derived from differential effective medium models, is proposed to identify pore types from elastic properties. The EPAR originates from the bulk modulus or shear modulus of the samples (K‐ and μ‐EPAR, respectively). Regardless of porosity values and depositional facies, microporous samples (type I) and samples with dominant intergranular porosity (type IIIa) are characterized by low values of K‐ and μ‐EPAR (<0.22) and by K‐EPAR > μ‐EPAR By contrast, samples with dominant mouldic porosity (type IIIb) display high values of K‐ and μ‐EPAR (>0.25 and 0.4 respectively) and K‐EPAR < μ‐EPAR.High permeability limestones with dominant intergranular porosity cannot be discriminated from low permeability microporous carbonates. The petrophysical classes derived from elastic properties are shown to be distinct from reservoir property‐driven rock types. In the present case, a seismic‐based poro‐elastic model does not match the reservoir property model. Hence, a sedimentary facies model for the studied carbonates cannot accurately represent the petrophysical properties, which are determined by pores types and pore network architecture.

Effect of components composition of condensate reservoirs on well test analysis: An experimental design approach

Gas condensate is a challenging fluid in reservoir due to difficulties arising from condensate dropout around the well. Condensate blockage complicates simulating the behavior of this fluid especially in well testing. The common approach in gas condensate well testing is using gas single-phase pseudo-pressure. In this study, statistical approach is applied to well test analysis in order to investigate the effect of fluid composition on reservoir behavior. Mixture design controls the number of otherwise numerous well testing of fluids to a limited number. The output data of well testings are then used for multivariable regression to create proxy models of reservoir and fluid properties including initial pressure of reservoir, gas permeability in condensate zone and maximum liquid dropout of fluids. Results show that proposed model efficiently simulates mentioned properties with high R-squared values. Results also show that the intensity of condensate formation in the reservoir is a strong function of rich components composition in the reservoir.

储层建模技术在羊二庄油田明馆储层油藏描述中的应用<br>Application of Reservoir Modeling in Description of Minghua and Guantao Reservoir in Yangerzhuang Oil Field

羊二庄油田明馆储层均为河流相沉积，由于曲流河的侧向迁移和辫状河河道的多期叠加造成储层非均质性严重。目前进入高含水、高开发后期，剩余油分布比较复杂，第一次油藏描述中应用传统的确定性建模方法已经不能满足剩余油挖潜描述的需要。为了解决上述问题，采用了RMS相控建模软件，通过构造建模、沉积微相建模、储层物性建模建立羊二庄油田三维可视化相控地质模型，并实现了与数模软件的无缝接口。 The Minghua and Guantao reservoir in Yangerzhuang oil field pertain to fluvial facies, so the lateral migra- tion of Meander River and the multi-period superposition of braided channel lead to the serious reservoir heterogeneity. It is the time of last period of high cut and high development at present, residual oil distribute more complicated. Tradi- tional deterministic modeling adopted in the first description of reservoir can’t meet the excavation of residual oil. In order to solve the problems mentioned above, RMS phase-control modeling software is adopted to establish the three- dimensional visible phase-control geographical model in Yangerzhuang oil field by setting up structural modelling, sedimentary microfacies modeling and reservoir property modeling, which has realized the seamless interface with nu- merical simulation software.

Stochastic inversion of facies from seismic data based on sequential simulations and probability perturbation method

We presented a new methodology for seismic reservoir characterization that combined advanced geostatistical methods with traditional geophysical models to provide fine-scale reservoir models of facies and reservoir properties, such as porosity and net-to-gross. The methodology we proposed was a stochastic inversion where we simultaneously obtained earth models of facies, rock properties, and elastic attributes. It is based on an iterative process where we generated a set of models of reservoir properties by using sequential simulations, calculated the corresponding elastic attributes through rock-physics relations, computed synthetic seismograms and, finally, compared these synthetic results with the real seismic amplitudes. The optimization is a stochastic technique, the probability perturbation method, that perturbs the probability distribution of the initial realization and allows obtaining a facies model consistent with all available data through a relatively small number of iterations. The probability perturbation approach uses the Tau model probabillistic method, which provides an analytical representation to combine single probabilistic information into a joint conditional probability. The advantages of probability perturbation method are that it transforms a 3D multiparameter optimization problem into a set of 1D optimization problems and it allowed us to include several probabilistic information through the Tau model. The method was tested on a synthetic case where we generated a set of pseudologs and the corresponding synthetic seismograms. We then applied the method to a real well profile, and finally extended it to a 2D seismic section. The application to the real reservoir study included data from three wells and partially stacked near and far seismic sections, and provided as a main result the set of optimized models of facies, and of the relevant petrophysical properties, to be the initial static reservoir models for fluid flow reservoir simulations.

Rock physics modeling of static and dynamic reservoir properties—a heuristic approach for cemented sandstone reservoirs

Rock physics models for fluid and stress dependency in reservoir rocks are essential for quantification and interpretation of 4D seismic signatures during reservoir depletion and injection. However, our ability to predict the sensitivity to pressure from first principles is poor. The current state-of-the-art requires that we calibrate the pressure dependence of velocity with core measurements. A major challenge is the fact that consolidated rocks often break up during coring, and hence the stress sensitivity is likely to be overpredicted in the laboratory relative to the in-situ conditions (Furre et al., 2009). For unconsolidated sands, acquisition of core samples is not very feasible due to the friable nature of the sediments. One physical model that has been applied to predict pressure sensitivity in unconsolidated granular media is the Hertz-Mindlin contact theory. Several authors (Vernik and Hamman, 2009, among others) have suggested empirical models with fitting parameters that correlate with microcrack intensity, soft porosity, and aspect ratio of the rock, and feasibility studies can be undertaken based on assumptions about these parameters. These models may not be easy to use for poorly to moderately consolidated sandstones with contact cement, where crack parameters and aspect ratios are difficult to quantify.

Calculation of Permeability Tensors for Unstructured Gridblocks

Summary Geostatistical models of reservoir properties are high resolution with many grid cells; it is impractical to use them directly in flow simulation because of computational costs. Upscaling techniques are applied to average small-scale permeability values to larger flow simulation blocks. In cases where unstructured grids are used or the geological features inside the gridblock are not aligned with the block geometry, symmetric or full-permeability tensors arise instead of a simple diagonal tensor. A method is presented to calculate the permeability tensor for an unstructured gridblock to account for the small-scale heterogeneity information inside the gridblock. Single-phase flow-based upscaling is performed based on random boundary conditions and the effective tensor permeability is found through an optimization technique. Full, symmetric and diagonal permeability tensors are calculated for 2D and 3D blocks and sensitivity analysis is performed.

Challenges and Technologies in Reservoir Modeling

Reservoir modeling is playing an increasingly important role in developing and producing hydrocarbon reserves. In this paper, we provide a brief overview of some main challenges in reservoir modeling, i.e., accurate and efficient modeling of complex reservoir geometry and heterogeneous reservoir properties. We then present modeling techniques we recently developed in addressing these challenges, including a method for generating constrained Voronoi grids and a generic global scale-up method. We focus on the Voronoi gridding method, which is based on a new constrained Delaunay triangulation algorithm and a rigorous method of adapting Voronoi grids to piecewise linear constraints. The global scale-up method based on generic flows is briefly described. Numerical examples are provided to demonstrate the techniques and the advantage of combining them in constructing accurate and efficient reservoir models. AMS subject classifications: 37M05, 76S05, 86A60, 65M50, 65N50

Assessing the resolution‐dependent utility of tomograms for geostatistics

Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co‐located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low‐angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

Construction of High-Resolution Acoustic Impedance Data for Reservoir Modeling

The 3-D models of absolute acoustic impedance values were built integrating well, seismic and geologic data utilizing deterministic and stochastic inversion techniques to bet utilized as soft data in reservoir property models to aid the development of the Rotliegend Gas Field, northwestern Germany.