Reservoir Characterization Process Research Articles

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

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.

A theoretical approach for water saturation estimation in shaly sandstones

The process of quantitative reservoir characterization is a crucial component spanning from the initial estimation of in-place reserves to the optimization of production design. Water saturation is a critical parameter in quantitative reservoir characterization that directly impacts the in-place reserve estimates. In the case of shale-free sandstones, the computation of water saturation using Archie's equation yields fairly accurate results. However, in shaly sandstone reservoirs, the presence of multiple conductive entities in the rock poses additional complexities. Currently, various empirical, semi-empirical and theoretical equations are available in literature to estimate water saturation; nevertheless, none of these models is such that it is both theoretically sound and practically applicable.In this study, we present a novel approach that is both theoretically sound and practically applicable in computing water saturation in shaly sandstones. The fluid-saturated rocks are treated as a system consisting of two interlocked lattice-like structures, namely grains and fluid in the interconnected pores. The composite fluid comprising water and hydrocarbon is modeled using the general mixture rule, while the Hanai-Bruggeman equation is employed to model the electrical conductivity of this system. The resulting electrical conductivity equation is then utilized to compute water saturation in shaly sandstones.The proposed equation was effectively validated using the published core data. Furthermore, we compare the new model with other grain conductivity-based models such as Mixing model and Berg's model. Our findings show that while describing core data the proposed equation gives a lower average Relative Root Mean Squared Error (RRMSE) of 2.05% as compared to 7.56% and 5.58% for the Mixing and Berg's model available in literature, respectively. Moreover, the range of porosity and water conductivity for which the proposed model is applicable, surpasses that of the current models. We also suggest that this equation can be extended to other systems with a similar two interlocked lattice-like structure, such as carbonates.

Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques.

The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When such activity is conducted in the field as part of an outcrop study, the sedimentologist is likely to be exposed to challenging conditions such as the weather and their accessibility to the outcrops. This study uses high-resolution photographs which are acquired from a sedimentological study to test an alternative basic multi-rock identification through machine learning. While existing studies have effectively applied deep learning techniques to classify the rock types in field rock images, their approaches only handle a single rock-type classification per image. One study applied deep learning techniques to classify multi-rock types in each image; however, the test was performed on artificially overlaid images of different rock types in a test sample and not of naturally occurring rock surfaces of multiple rock types. To the best of our knowledge, no study has applied semantic segmentation to solve the multi-rock classification problem using digital photographs of multiple rock types. This paper presents the application of two state-of-the-art segmentation models, namely U-Net and LinkNet, to identify multiple rock types in digital photographs by segmenting the sandstone, mudstone, and background classes in a self-collected dataset of 102 images from a field in Brunei Darussalam. Four pre-trained networks, including Resnet34, Inceptionv3, VGG16, and Efficientnetb7 were used as a backbone for both models, and the performances of the individual models and their ensembles were compared. We also investigated the impact of image enhancement and different color representations on the performances of these segmentation models. The experiment results of this study show that among the individual models, LinkNet with Efficientnetb7 as a backbone had the best performance with a mean over intersection (MIoU) value of 0.8135 for all of the classes. While the ensemble of U-Net models (with all four backbones) performed slightly better than the LinkNet with Efficientnetb7 did with an MIoU of 0.8201. When different color representations and image enhancements were explored, the best performance (MIoU = 0.8178) was noticed for the L*a*b* color representation with Efficientnetb7 using U-Net segmentation. For the individual classes of interest (sandstone and mudstone), U-Net with Efficientnetb7 was found to be the best model for the segmentation. Thus, this study presents the potential of semantic segmentation in automating the reservoir characterization process whereby we can extract the patches of interest from the rocks for much deeper study and modeling to be conducted.

A Review of Historical Studies for Water Saturation Determination Techniques

Water saturation is the most significant characteristic for reservoir characterization in order to assess oil reserves; this paper reviewed the concepts and applications of both classic and new approaches to determine water saturation. so, this work guides the reader to realize and distinguish between various strategies to obtain an appropriate water saturation value from electrical logging in both resistivity and dielectric has been studied, and the most well-known models in clean and shaly formation have been demonstrated. The Nuclear Magnetic Resonance in conventional and nonconventional reservoirs has been reviewed and understood as the major feature of this approach to estimate Water Saturation based on T2 distribution. Artificial intelligence has recently been used to predict water saturation and other parameters in the reservoir characterization process using seismic data, so the main idea of this technique and a list of the author's researchers have been reviewed. In this review article, the reference approach using core analysis by distillation-extraction and retorting techniques have been explained, as well as the saturation-height method, which is based on the capillary pressure concept and wettability. Finally, alternative experimental approaches based on scanning are expressed in this manner.

Geostatistical Inversion of Spectrally Broadened Seismic Data for Re-Evaluation of Oil Reservoir Continuity in Inas Field, Offshore Malay Basin

The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions.

Mixed carbonate-siliciclastic reservoir characterization and hydrocarbon accumulation process of the Ganchaigou area in the western Qaidam Basin, Tibet Plateau

Mixed carbonate-siliciclastic reservoir characterization and hydrocarbon accumulation process of the Ganchaigou area in the western Qaidam Basin, Tibet Plateau

Comprehensive workflow to quantify diagenetic cement from wireline logs of deep water east coast of India wells and rock physics modeling

Presence of diagenetic cement is common in deep-water siliciclastic reservoirs and largely affects reservoir quality. Hence, the quantification of cement volume is an integral part of the reservoir characterization workflow. Lab analysis of core samples provides the cement volume information used as input for rock physics modeling. In the absence of core sample analysis, use of analogous offset well information provides an approximate estimate of cement. In reality, most deep-water wells do not have core samples for lab analysis. Consequently, the availability of offset well information is sparse and sometimes unavailable. Hence, getting an accurate cement volume estimate for reservoir characterization is challenging. For a detailed analysis, it is desirable to have a continuous measurement of cement volume across the reservoir sand. Presence of a calibrated cement volume log for the entire reservoir improves rock physics modeling. In this work, petrophysical M-N lithology cross plot, which is widely used to identify lithologies, has been used to quantify the diagenetic cement present within the reservoir, where M is the density normalized sonic values and N is the density normalized neutron values. Here, the M and N has been used as M-Lithology (MLITH) and N-Lithology (NLITH). In estimating MLITH and NLITH values, compressional sonic, bulk density and neutron logs are used. Porosity estimated from neutron/density is useful to identify the type of cement. Whereas, sonic slowness values are more sensitive towards the amount of grain contact cements. Hence, the estimated values of MLITH and NLITH varies differently in the presence of diagenetic materials. These variations of MLITH and NLITH values are captured and converted to cement volume fractions. In the presence of core data, the lab analysis measurements are used to calibrate the cement volume log. Further, rock physics modeling results helps to validate the output. The results of the study add value by quantifying diagenetic cementation in the reservoir characterization process by mitigating the associated limitations due to absence of sufficient core data.

Elastic Properties of Pannonian Basin Limestone under Different Saturation Conditions

Understanding elastic properties of reservoir rocks is essential for seismic modeling under different saturation conditions as well as lithology discrimination. Experiments on elastic properties of limestones are significantly less published compared to siliciclastic sedimentary rocks. The current study presents the results of laboratory measurements on Pannonian Basin limestone cores. The research was carried out for the first time for a hydrocarbon reservoir in the Bjelovar Depression, located in the southern part of the Pannonian Basin. Ultrasonic velocity measurements and determination of dynamic elastic properties were performed on limestone plugs, in dry and saturated condition under different confining pressure steps. Based on the results obtained in laboratory conditions, an empirical relationship between shear wave velocity (Vs) and compressional wave velocity (Vp) has been defined. The saturated samples show an effect of shear modulus weakening, while three samples have a shear modulus strengthening effect. Two models were used in the interpretation of the measured data, the Kuster and Toksöz and the Xu-Payne model. The results show that the Xu-Payne model describes the data well and the dominant pore type system in the limestone samples can been identified. The interpretation revealed an interparticle pore system with a fraction of microcracks from 20% to 35%. The results have helped to understand the elastic properties of limestones from the southern part of the Pannonian Basin, which are necessary for any process of reservoir characterization, such as porosity distribution and permeability variation.

Selection of Optimum Permeability Estimation Approach in a Heterogeneous Carbonate Reservoir

Determination of permeability is an essential component of reservoir characterization process which is among the key input parameters into a flow simulation models. Permeability modeling in carbonate reservoirs is still a challenge in the world. Permeability is directly determined in the laboratory from core analysis. Alternatively, it can be determined by analyzing well test or well logs. Due to high cost associated with coring and some technical problems, few wells in any given field are cored whereas most wells have wire-line logs.
 In this study detailed core analysis data including core porosity and core permeability supplemented by well logs and well test data to predict a continuous log derived permeability in un-cored wells in a heterogeneous carbonate reservoir in south west of Iran. The Mishrif reservoir in the studied field consists of limestone and interbedded shale. The field has 3 wells that have recovered cores. Permeability prediction was applied by several methods including: fuzzy logic, neural networks, clustering, empirical methods and regression analysis. These different methods were used to determine the optimal approach for utilizing in the field under study. To test the permeability prediction, the techniques were calibrated in 2 cored wells and blind tested in remaining cored well to see how well estimated permeability fitted the actual core permeability. Among all permeability modeling methods applied in the field, it turned out that electrofacies method and after that artificial neural network have the highest degree of association. Fuzzy logic and regression techniques are average in modeling permeability and empirical methods are not capable for predicting permeability in studied heterogeneous carbonate reservoir. The core analysis from 3 cored-wells was applied to determine permeability in 51 un-cored wells.

Deciphering the Reservoir Rocks Lithology by Mineralogical Investigations Techniques for an Oilfield in South-West Romania

An important element of the geological modeling of oil reservoirs is represented by determining of the mineralogical composition and rock types as part of the reservoir characterization process. In the paper we provide a comprehensive mineralogo-petrographic study based on petrographic observations and X-rays diffraction investigations made on several Miocene rock samples collected in the wells spudded in an oil field belonging to the Getic Basin. Getic Basin is a prolific petroleum province in Romania and belongs to petroleum systems of the Carpathian Foredeep. The oil exploration in the Getic Basin started more than 100 years ago and resulted in thousands of wells drilled and tens of fields discovered. The oil field is located in the Gorj County, geologically belongs to the internal zone of the Getic Basin, and is a faulted anticline with hydrocarbon accumulations in Burdigalian and Sarmatian deposits. The petrographic study led to the interpreting of the rock samples analyzed as epiclastic sedimentary rocks represented by conglomerates, breccias, sands, sandstones, claystones and marlstones, and carbonate rocks (limestones). X-rays diffraction investigations indicated the phyllosilicates (smectite and illite) as main minerals in the Sarmatian samples, while in the Burdigalian samples were found as main minerals: quartz, feldspars and carbonate minerals. The paper provides detailed information (like petrographic types, composition and microtexture) on the Miocene reservoir rocks belonging to the Getic Basin. Also the data obtained may be used as basis for future reservoir modeling studies in the region.

Laser Particle Size Analysis (LPSA) approachment for Depositional Environment and Hydraulic Flow Unit (HFU) determination in Keutapang Formation, Rantau Field (North Sumatera Basin) and Air Benakat Formation, Tempino Field (South Sumatera Basin)

Keutapang Formation deposited in North Sumatra Basin at the mid to late miocen on regression phase. Air Benakat Formation deposited in South Sumatra Basin, has similar characteristic with Keutapang Formation which deposited in the regression & compressional phase near the same age. The purpose of this study is to know the relationship between Laser Particle Size Analysis (LPSA) and Depositional Environment to include Hydraulic Flow Unit (HFU) in each Formation to help reservoir characterization process and rock type determination. Laser Particle Size Analysis results show that both formations have grain size from 11 to 160 μm in other words is very fine sand (wentworth scale). Sorting ranges from 1.5 to 2.4Ø which is classified as poorly sorted to very poorly sorted. Skewness ranges from -0.091 to 0.658 Ø grouped into near symmetrical, fine, and very fine skewness. Kurtosis ranges from 0.686 to 1.102 Ø grouped into very platykurtic, platykurtic, and mesokurtic. Based on the Linear Discriminate Function (LDF) method, by evaluating the values of Y1, Y2, Y3 and Y4, it describes that both formations are deposited on the Deltaic Environment with Shallow Agitated Water and dominated by traction currents. Difficulties in geomodelling process comes when there is no relationship between log analysis properties (which will be distributed) with HFU (from core data analysis). With LPSA data, we can obtain a linear (indirect) relationship between grain size and the shale volume on the same rock geometry. This relationship can be used to distribute HFU values by using a plot of grain size with HFU or Flow Zone Index.

Integration of Electrofacies and Hydraulic Flow Units to Delineate Reservoir Quality in Uncored Reservoirs: A Case Study, Nubia Sandstone Reservoir, Gulf of Suez, Egypt

Recognition of reservoir quality is an important objective in reservoir characterization process. By definition, the quality of a reservoir is described by its hydrocarbon storage capacity and deliverability. The storage capacity is a function of porosity, whereas deliverability is a function of permeability. Thus, both porosity and permeability are the main reservoir quality controlling factors. Five wells were selected to study the reservoir quality of the Nubia sandstone in the Gulf of Suez, Egypt. The Nubia interval, deposited in continental to shallow marine conditions, consists mainly of sandstone intercalated by thin layers of shale. Three wells were partially cored and the other two wells are completely uncored. Based on several complementary techniques, it is concluded that the Nubia sandstone is of moderate to extremely heterogeneous quality. The porosity–permeability cross-plot showed fair-to-medium relationship, indicating the significant role of diagenetic agents. The Nubia reservoir quality has been enhanced principally by fracturing, dissolution and leaching. However, the reservoir quality decreased by cementation, compaction and filling of pore spaces by kaolinite. Based on principal component and cluster analyses, six electrofacies are recognized within the Nubia interval. Three electrofacies are dominant, whereas the other three are subsidiary. Flow zone indicator (FZI) was determined based on mean hydraulic radius and normalized porosity. Correlation between electrofacies and FZI permits discriminating the subject formation into reservoir quality ranks. The relative complexity of this reference formation notwithstanding, because of the robustness of the resulting electrofacies–FZI correlations, it was relatively straightforward to recognize and reasonably predict the reservoir quality of the uncored intervals.

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an activation function recently proposed in the literature is implemented and tested. The computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.

Machine learning approaches for petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

The definition of lithology in oil wells by means of multiple geophysical analysis profiles an important role in the reservoir characterization process. From this one can generate lithologic models which in turn will be filled in with the petrophysical properties and can then be used in flow simulators to understand and study the behavior of an oil field. The identification of lithology can be accomplished by direct and indirect methods, but these procedures are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the process of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. In this paper balancing strategies are implemented in order to tackle the lack of data due to the characteristics of the distinct diagenetic processes. Six machine learning methods combined with data balancing techniques and a model selection approach to classify geologic data from the South Provence Basin. The results show the balancing strategies improve the overall performance of all classifiers and the model selection allows or obtaining the best parameters from a user-defined set. The computational tool developed in this paper arises as an alternative to assist geologists ans specialists in the task of reservoir heterogeneities identification.

A New Method of Porous Space Reconstruction Using Multipoint Histogram Technology

Pore-scale modeling is becoming a hot topic in overall reservoir characterization process. It is an important approach for revealing the flow behaviors in porous media and exploring unknown flow patterns at pore scale. Over the past few decades, many reconstruction methods have been proposed, and among them the simulated annealing method (SAM) is extensively tested and easier to program. However, SAM is usually based on the two-point probability function or linear-path function, which fails to capture much more information on the multipoint connectivity of various shapes. For this reason, a new reconstruction method is proposed to reproduce the characteristics of a two-dimensional (2D) thin section based on the multipoint histogram. First, the two-point correlation coefficient matrix will be introduced to determine an optimal unit configuration of a multipoint histogram. Second, five different types of seven-point unit configurations will be used to test the unit configuration selection algorithm. Third, the multipoint histogram technology is used for generating the porous space reconstruction based on the prior unit configuration with a different calculation of the objective function. Finally, the spatial connectivity, patterns reproduction, the local percolation theory (LPT), and hydraulic connectivity are used to compare with those of the reference models. The results show that the multipoint histogram technology can produce better multipoint connectivity information than SAM. The reconstructed system matches the training image very well, which reveals that the reconstruction captures the geometry and topology information of the training image, for instance, the shape and distribution of pore space. The seven-point unit configuration is enough to get the spatial characters of the training image. The quality of pattern reproduction of the reconstruction is assessed by computing the multipoint histogram, and the similarity is around 97.3%. Based on the LPT analysis, the multipoint histogram can describe the anticipated patterns of geological heterogeneities and reproduce the connectivity of pore media with a high degree of accuracy. The two-point correlation coefficient matrix and a new construction theory are proposed. The new construction theory provides a stable theory and technology guidance for the study of pore space development and multiphase fluid flow rule in the digital rock.

Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization

The successful applications of the conventional Computational Intelligence (CI) techniques and Hybrid Intelligent Systems (HIS) in petroleum reservoir characterization have been reported. However, these techniques are limited in their capability to handle a single hypothesis of a problem at a time. The major objective of the reservoir characterization process is to produce models that are robust enough to help improve the accuracy of the predictions of reservoir properties for use in full-field and large-scale simulation models. Research in CI continues to evolve new techniques and paradigms to meet this noble objective. It has been shown that there are uncertainties in the reservoir characterization process as well as the optimal choice of CI/HIS models parameters. The main challenge is to develop models that are capable of handling multiple hypotheses to reduce the uncertainties thereby ensuring optimal solutions. The ensemble machine learning paradigm has been established to tackle this challenge. This new machine learning technology has not been adequately explored in handling some of the petroleum engineering challenges. This paper rigorously reviews the concept of ensemble learning paradigm, presents successful applications outside petroleum engineering and the geosciences, discusses a few successful attempts in petroleum engineering and the geosciences, and concludes with some recommendations for the much-needed future applications.

Use of Emulator Methodology for Uncertainty-Reduction Quantification

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169405, “Use of Emulator Methodology for Uncertainty-Reduction Quantification,” by C. Ferreira, Universidade Estadual de Campinas; I. Vernon, Durham University; D.J. Schiozer, SPE, Universidade Estadual de Campinas; and M. Goldstein, Durham University, prepared for the 2014 SPE Latin American and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, 21–23 May. The paper has not been peer reviewed. Most simulation models go through a series of iterations before being judged as giving an adequate representation of the physical system. This can be difficult because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time. Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space. Introduction Reservoir simulators are important and widely used in reservoir management. They are used in reservoir-performance prediction and for decision making. These simulators are computer implementations of high-dimensional mathematical models for reservoirs, where the model inputs are physical parameters and the outputs are observable characteristics such as well-pressure measurements and fluid production. Uncertainties are always present in the reservoir-characterization process; thus, input parameters are usually uncertain and so is the simulator output. The procedure to calibrate the reservoir-simulation model is called history matching. On the basis of observed data, a set of possible input choices for the reservoir model is identified. Two different procedures can be used to perform the history matching: deterministic and probabilistic approaches. The deterministic approach involves running the initial simulation model with different input values to obtain one simulation model between many probable matches to the field data. In a probabilistic approach, in which several reservoir-model scenarios are considered, the uncertainty analysis procedure is used. Identifying the input parameters for which the simulation outputs match the observed data can be a difficult task because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time.

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.

A cell-centered multipoint flux approximation method with a diamond stencil coupled with a higher order finite volume method for the simulation of oil–water displacements in heterogeneous and anisotropic petroleum reservoirs

The simulation of fluid flow and transport in heterogeneous and anisotropic oil and gas reservoirs poses a great challenge from the numerical point of view, due to the modeling of complex depositional environments, including inclined laminated layers, channels, fractures and faults and the modeling of deviated wells, making it difficult to build and handle the Reservoir Characterization Process (RCM), particularly by using structured meshes (cartesian or corner point), which is the current pattern in petroleum reservoir simulators. Under certain hypotheses, the mathematical model that describes the fluid flow in petroleum reservoirs includes an elliptic equation with heterogeneous, possibly discontinuous, coefficients for the pressure field and a non-linear hyperbolic equation for the saturation field. In the present paper, these equations are solved via an Implicit Pressure Explicit Saturation (IMPES) procedure. To solve these equations, we use a full cell-centered finite volume formulation. The pressure equation is discretized by a non-orthodox Multipoint Flux Approximation Method with a Diamond type stencil (MPFA-D) and used for the first time for the solution of two-phase flow problems in heterogeneous porous media. It is very robust and capable of reproducing piecewise linear solutions exactly by means of a linear preserving interpolation with explicit weights that avoids the solution of locally defined systems of equations. For the solution of the saturation equation, we use a Monotone Upstream Centered Scheme for Conservation Laws (MUSCL) method based on a gradient reconstruction obtained by a least square technique in which monotonicity is reinforced by an appropriate slope limiter. The method can be used with general polygonal meshes, even though we restrict ourselves to conforming triangular and quadrilateral grids. In order to validate and show the robustness of our formulation, we solve some problems including heterogeneous and anisotropic reservoirs and displacements with high mobility ratios. Our results compare well with others found in literature.