High-resolution Reservoir Models Research Articles

Informed reduction of the geological data on rock material interfaces in subsurface CO2 storage reservoirs

Recent developments in subsurface data capturing technologies have presented the opportunity of representing high-resolution information in field scale reservoir models of CO2 geo-sequestration sites. While the wealth of geological data is critical for the accurate prediction of CO2 flow and trapping in the subsurface, it often presents computational challenges. This is especially true for the representation of rock, or lithological, interfaces which are small-scale features in reservoirs and significantly control CO2 migration and trapping. It is important to reduce this information to ensure that numerical simulations are completed within a reasonable time. However, care must be taken to ensure that none of the key lithological interface associations are lost during data reduction. This study presents a machine learning based approach for expressing the amount of rock interfaces characteristic of sedimentary CO2 storage reservoirs in terms of a reduced number of principal components which capture the necessary geological information. The workflow is applied to a high-resolution reservoir model of the Paaratte Formation, Otway Basin, Australia, which is coastal to shallow marine siliciclastic reservoir. The information in the reservoir model was captured using 922 k rock interfaces. However, the algorithm predicts that the same information could be represented using only 7 principal lithological interface associations. The outcomes are validated using multiphase flow simulations which show the uniqueness of these principal components in terms of their impact on CO2 flow and trapping.

Viscosity prediction using image processing and supervised learning

The paper implements image processing followed by supervised learning to predict the viscosity of hydrocarbon present at a specific reservoir depth in a heavy oil reservoir located in California, USA. Viscosity is predicted from white-light and UV-light images of side-wall core samples extracted from specific depths along the wellbore. The fluid viscosity primarily ranges from 5 to 600 cP at a temperature of 180 degF. The white-light and UV-light images are available for 680 side-wall core samples. Sobel, Sato, Hessian, LBP, and Multi-Otsu filters extract meaningful features from red, green, and blue pixel intensities of the white-light and UV-light images. The distribution of pixel-wise values for each filtered/raw image is further processed to derive histogram-based features. As a result, 600 features are extracted for each side-wall core sample. Thresholds based on the variance, MI score, F score, and Person’s r are imposed to select the most informative features for the viscosity prediction. Compared to filtered white-light images, raw RGB white-light images have stronger viscosity association. On the contrary, filtered UV-light images have stronger viscosity association as compared to raw RGB UV-light images. Supervised learning is deployed using the resulting features in both regression and classification of target viscosity. For the prediction of continuous-valued viscosity (regression), random forest is the best performer with a mean absolute error of 27 ∓ 3 cP. For the viscosity classification, high-viscosity and low-viscosity samples can be very well detected at a F1 Weighted Score higher than 0.95 and Matthew’s Correlation Score higher than 0.9. Overall, for viscosity prediction, classification is much superior to the regression. The novel image-based viscosity prediction workflow will help lower the cost and improve the precision of laboratory-based viscosity measurements and simultaneously contribute to a high-resolution reservoir model development.

High resolution pre-stack seismic inversion using few-shot learning

We propose to use a Few-Shot Learning (FSL) method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data. Recently, artificial neural network (ANN) demonstrates great advantages for seismic inversion because of its powerful feature extraction and parameter learning ability. Hence, ANN method could provide a high resolution inversion result that are critical for reservoir characterization. However, the ANN approach requires plenty of labeled samples for training in order to obtain a satisfactory result. For the common problem of scarce samples in the ANN seismic inversion, we create a novel pre-stack seismic inversion method that takes advantage of the FSL. The results of conventional inversion are used as the auxiliary dataset for ANN based on FSL, while the well log is regarded the scarce training dataset. According to the characteristics of seismic inversion (large amount and high dimensional), we construct an arch network (A-Net) architecture to implement this method. An example shows that this method can improve the accuracy and resolution of inversion results.

Lithology prediction method of coal-bearing reservoir based on stochastic seismic inversion and Bayesian classification: a case study on Ordos Basin

Abstract The Upper Palaeozoic coal measures in the Ordos Basin are rich in unconventional natural gas resources; however, the reservoir heterogeneity is relatively strong, which majorly restricts the accuracy of lithology prediction. Stochastic seismic inversion can synthesize the geological–geophysical information and establish high-resolution reservoir models based on geostatistical theory to obtain good inversion results; moreover, it can characterize reservoir lithology and fluid-bearing properties. The present study aimed to propose a high-precision lithology classification method for coal-bearing strata in the Ordos Basin, using Bayesian classification and stochastic seismic inversion. Initially, the reservoir geological model was established on the basis of the sequential Gaussian simulation algorithm, and stochastic seismic inversion was performed in combination with rock physics analysis, thereby obtaining a high-resolution elastic parameter data volume. Thereafter, the probability density function (PDF) and the probability density confusion matrix (PDF confusion matrix) were introduced to quantitatively analyse the ability of sensitive elastic parameters to distinguish lithology. Eventually, a logging lithology-fluid classification template was established based on the Bayesian classification technology; furthermore, a 3D lithology-fluid prediction was completed in combination with the inversion results. The prediction results are in good accordance with the logging data, which verifies the feasibility and effectiveness of the method.

Integration of sub- meter scale lithological heterogeneity in reservoir models for an improved quantification of CO2 trapping capacities

Sub-meter scale lithological heterogeneity in the form of sedimentary structures such as cross, planar and massive beddings is known to enhance carbon mineral trapping. However, this understanding is largely qualitative. Quantitative estimates of trapping capacities at this scale are limited by the availability of high-resolution field data. Additionally, such heterogeneity is typically not accounted for in conventional reservoir models due to computational restriction posed by the grid size resolution. As a result, the impact of sub-meter scale lithological heterogeneity on carbon trapping capacities is typically not accounted for in dynamic flow and reactive transport simulations at the reservoir scale. The present study aims to quantify the impact of intraformational baffles on carbon mineralization for a range of rock types. A series of 2-D multiphase reactive transport simulations were set-up to model the geochemical processes occurring at the interface of a baffle and a reservoir rock for a period of 1000 years. The study shows that the presence of baffles with certain rock properties within the reservoir rock can enhance or reduce carbon mineral trapping capacity relative to homogeneous reservoir rocks depending on clay and carbonate mineral content in baffles. Further, a new workflow is developed to build a 2D high-resolution reservoir model of the Paaratte Formation, Otway Basin while overcoming the technical challenges associated with sub-meter scale discretization of geological models.

A Comparative Study of Geometrical Upscaling Methods for Inclusion of Fault Zone Structure into Upscaled Reservoir Simulation Models

Most petroleum reservoirs contain faults, and a key methodological challenge in full-field reservoir modeling is to include the effects of 3D sub-seismic fault zone structure within the 2D fault surfaces represented in commercially available simulators. To address this shortcoming, connectivity-based geometrical upscaling (GU) is introduced to include these fault zones implicitly in the upscaled reservoir model, and in this study, a comparison is made of the accuracy and flexibility of two different GU methods. The existing template-based geometrical upscaling (TBGU) method is compared to a newly developed flow-based geometrical upscaling (FBGU) method. In both methods, the faults and their associated single-phase fault rock properties are represented in the upscaled reservoir simulation model implicitly as neighbor and non-neighbor cell-to-cell connection transmissibilities, which are determined from 3D fault zone structures, but these transmissibilities are calculated in very different ways. Both approaches require a high-resolution reservoir model (i.e., truth model), containing 3D fault zone structure explicitly within the model geometry, which is then upscaled using the two methods to take into account the influences of the fault zone structure. The accuracy of both the upscaling methods is examined by comparing the flow behavior of the truth model with that of model versions upscaled using two different ways. The results of the two versions of upscaled models revealed significant differences and indicate that the FBGU technique is a much more accurate and flexible means of including structurally complex fault zone into low-resolution upscaled reservoir simulation models.

Two-level uncertainty assessment in stochastic seismic inversion based on the gradual deformation method

Geostatistical seismic inversion can combine seismic data, well data, and spatial continuity of the property of interest to obtain high-resolution reservoir models and evaluate uncertainties. Some workflows estimate global geostatistical parameters, such as correlation length, and keep them fixed in all simulations and inversions. This can introduce biases due to the sparsity of available well data and underestimate the uncertainty of inversion. A better approach is to incorporate the uncertainty in these global parameters. Lateral correlation length is one of the most difficult parameters to estimate. We have developed a seismic inversion method based on local gradual deformation method, which incorporates the uncertainty of lateral correlation length and provides a two-level uncertainty evaluation. We first estimate a uniform prior distribution of lateral correlation length from well data and additional geologic expert knowledge. After using fast Fourier transform (FFT) moving average simulations and local gradual deformation optimization, we obtain multiple realizations from which we could extract the lateral correlation lengths and calculate their posterior distribution. The FFT moving average method generates reservoir models by a convolution between a filter operator and a random noise field. The filter operator does not change during inversion, and the correlation structure of the random noise field could be changed by the local gradual deformation method to match the seismic data. A synthetic model test shows that the correlation lengths and the global probability distribution of the inverted results tend to the true geostatistical characteristics. The posterior distribution of the lateral correlation length narrows after inversion. Compared with conventional geostatistical seismic inversion techniques, uncertainties in the results increase because we incorporate the uncertainty in the global parameters. A real case also demonstrated that by modifying the random noise field locally, thin layers in a thick formation are well restored, even if they are not interpreted in advance.

Seismic chronostratigraphy at reservoir scale: Lessons from a realistic seismic modeling of mixed clastic-carbonate strata in the Permian Basin, West Texas and New Mexico, USA

We tested the validity of tracking seismic events as representations of chronostratigraphic surfaces at the subseismic, high-frequency-cycle level. A high-resolution geocellular model was generated from approximately 400 m of mixed clastic-carbonate sequences in the San Andres and Grayburg Formations in the Permian Basin, with 0.3–0.6 m layering and minimal upscaling. Realistic stratigraphic framework, facies, and velocity volumes were created by geostatistically mapping data from outcrop and subsurface sources while honoring state-of-the-art principles of stratigraphic and sedimentary analyses. Using the synthetic seismic data of different frequencies, the potential and pitfalls of using autotracked seismic horizons in building high-resolution reservoir models were tested. At the reservoir (meter) scale, the seismic reflections from flatter and thicker sediments with less facies and velocity heterogeneities tend to follow geologic-time surfaces; on the contrary, reflections from where thin sediments dip against flat strata with more facies and velocity heterogeneities tend to follow lithostratigraphy. For the latter seismic-guided reservoir modeling is not very precise, even with data as high as 140 Hz frequency. Therefore, for seismic-assisted reservoir prediction and modeling, the interpretation of seismic events is useful, but well calibration is critical.

Enhancement of SPSA algorithm performance using reservoir quality maps: Application to coupled well placement and control optimization problems

Determination of optimal well location is a vital step in the design of an efficient field development plan which aims to maximize the hydrocarbon recovery and profits from the project. This step has become further complicated as the well control parameters are also introduced into the problem which is known as coupled well placement and control optimization problem. As the number of optimization variable increases, computational cost also increases and it becomes a serious challenge, especially in high resolution reservoir models since the optimization process becomes very time demanding. Simultaneous Perturbation Stochastic Approximation (SPSA) has been introduced as an efficient algorithm in finding the optimum solution for problems with a large number of variables. In this work, SPSA algorithm has been modified using reservoir quality maps to enhance and accelerate finding the good solutions with a minimum number of function evaluation which is desirable for high-resolution reservoir models and high dimensional optimization problems. In this modification, reservoir quality maps are employed to navigate production wells toward high potential areas instead of random movement in standard SPSA. The modified algorithm is tested for joint well placement and control optimization problem using both simultaneous and sequential optimization approach in a synthetic and two benchmark models. The results show that modified SPSA outperforms standard SPSA both in terms of final result and convergence trend. Also, in most cases, simultaneous well placement and control optimization approach gives better results compared to sequential approaches for both algorithms.

A three dimensional high-resolution reservoir model of Napo Formation in Oriente Basin, Ecuador, integrating sediment dynamic simulation and geostatistics

Abstract With the rapid development of computer hardware and software technology, 3D modeling technology of reservoirs has been widely applied in the exploration of oilfields. However, when modeling the reservoir with few wells, due to the absence of efficient constraints in interwell areas, the accuracy of the resulting 3D model is usually lacking. In this paper, we study the issue of model accuracy under the conditions of oil reservoir with few wells and propose a new 3D geological modeling method that is constrained by the multiple parameters based on sedimentary dynamics. We carry out a series of sediment dynamic simulation, by considering a number of key processes and parameters affecting the deposition of M1 member of Napo Formation in T-Oilfield, Oriente Basin, from 79.8 Ma to 76 Ma, to constrain the lithofacies and properties models. The proposed modeling method comprises two steps. First, the lithology distribution is obtained from a petrologic 3D model through the forward modeling of sedimentary processes. Second, geostatistical method is applied to build the reservoir geological model with the constraints of sedimentary dynamics, and logging information from work areas, the distribution of sedimentary characteristics by using a coordination Kriging modeling method. As a result, a high-degree fit is obtained between the actual characteristics of the strata and the reservoir model, with relative error of 4.6%, indicating future application of this method for geological modeling of reservoirs with few wells.

High-resolution geostatistical modeling of an intensively drilled heavy oil reservoir, the BQ 10 block, Biyang Sag, Nanxiang Basin, China

Abstract Establishing a high-resolution subsurface model is the ultimate goal of reservoir characterization. The “BQ 10 Block”, in Biyang Sag of Nanxiang Basin in China, is a data-rich mature field with heavy oil deposits penetrated by over two hundred wells with an average well spacing of 75m. The aim of this study is to establish a three-dimensional high-resolution model of the BQ 10 reservoir and to accurately and efficiently derive its geological and geometrical properties. A geostatistical modeling study was carried out in combination with stratigraphic correlation, core observations, log interpretation, lithology estimation, and sedimentary facies analysis. Based on core and wireline log data, lithology and conventional well logs (i.e. resistivity, gamma ray, and spontaneous potential) were correlated first. Lithofacies data at cored well locations were grouped into petrofacies units based on reservoir quality data. Using a neural network method, petrofacies at uncored wells was predicted based on correlation with conventional well logs. For different petrofacies, statistical analyses revealed correlation between laboratory porosity and permeability with acoustic well logs. A petrofacies model was built alternatively with indicator kriging, truncated Gaussian simulation (TGS), and sequential indicator simulation (SIS). SIS with a 3D trend model was identified to give the most reasonable petrofacies distribution. Using this model, facies-controlled petrophysical property modeling was performed using sequential Gaussian simulation (SGS). A validation procedure based on “minimum acceptance criteria” was employed to ensure that the SIS/SGS realizations are consistent. For this data-rich reservoir, this study demonstrates an effective application of the standard geostatistical modeling techniques on constructing high-resolution reservoir models of a heavy oil reservoir. The final suite of models reveal a likely range of reservoir facies and petrophysical property distributions for BQ 10, which can be useful for designing heavy oil recovery operations.

K-Values-Based Upscaling of Compositional Simulation

Summary Enhanced-oil-recovery (EOR) processes involve complex flow, transport, and thermodynamic interactions; as a result, compositional simulation is necessary for accurate representation of the physics. Flow simulation of compositional systems with high-resolution reservoir models is computationally intensive because of the large number of unknowns and the strong nonlinear interactions. Thus, there is a great need for upscaling methods of compositional processes. The complex multiscale interactions between the phase behavior and the heterogeneities lie at the core of the difficulty in constructing consistent upscaling procedures. We use a mass-conservative formulation and introduce upscaled phase-molar-mobility functions for coarse-scale modeling of multiphase flow. These upscaled flow functions account for the subgrid effects caused by the absolute permeability and relative permeability variations, as well as the effects of compressibility. Upscaling of the phase behavior is performed as follows. We assume that instantaneous thermodynamic equilibrium is valid on the fine scale, and we derive coarse-scale equations in which the phase behavior may not necessarily be at equilibrium. The upscaled thermodynamic functions, which represent differences in the component fugacities, are used to account for the nonequilibrium effects on the coarse scale. We demonstrate that the upscaled phase-behavior functions transform the equilibrium phase space on the fine scale to a region of similar shape, but with tilted tie-lines on the coarse space. The numerical framework uses K-values that depend on the orientation of the tie-lines in the new nonequilibrium phase space and the sign of upscaled thermodynamic functions. The proposed methodology is applied to challenging gas-injection problems with large numbers of components and highly heterogeneous permeability fields. The K-value-based coarse-scale operator produces results that are in good agreement with the fine-scale solutions for the quantities of interest, including the component overall compositions and saturation distributions.

Reservoir characteristics and three-dimensional architectural structure of a complex fault-block reservoir, beach area, China

The objective area in this paper is a complex fault-block reservoir, which is provided by China Petroleum Engineering Design Competition. Reservoir characteristics which comprise of stratigraphic features, vertical changes, and section properties (thickness, percentage sand, and percentage amalgamation) are documented. Through comprehensive analysis on structural architecture and reservoir characteristics, a three-dimensional quantitative reservoir modeling is taken at a regional scale of 69 × 97 × 37 m3 with the application of geostatistics as theoretical guidance. Then, a high-resolution hierarchical reservoir model of this field has been generated with a combination of hierarchical, structural, physical, and well trajectory data. The established three-dimensional geological model integrates all well and structural information, which can provide a basic model for subsequent sedimentary microfacies modeling and property modeling as well. Finally, a series of profiles are built successively, they are three-dimensional fence diagrams, connecting well sections and well group profiles, which can be a valuable tool for reflecting geologic body with great reality and thus achieve reservoir characterization of the complex fault-block reservoir.

Reservoir Characteristics and Three-dimensional Architectural Structure of a Complex Fault-block Reservoir, Beach Area, China

The objective area is a complex fault-block reservoir, and is provided by China Petroleum Engineering Design Competition(CPEDC). Reservoir characteristics, including stratigraphic features, vertical changes and section properties (thickness, percentage sand and percentage amalgamation) are documented. Based on comprehensive analysis on structural architecture and reservoir characteristics, a three-dimensional quantitative reservoir modeling is taken at a regional-scale of 69×97×37 m3 with the application of geostatistics as theoretical guidance, and thus high-resolution hierarchical reservoir model is generated with a combination of hierarchical data, structural data, physical data and well trajectory data. Therefore, the established three-dimensional geological model integrates all well-point information and structural information, and provides a basic model for subsequent sedimentary microfacies modeling and property modeling as well. Finally, three-dimensional fence diagrams, connecting-well sections and well group profiles are built successively.

Proxy-Based Metamodeling Optimization of Gas-Assisted-Gravity-Drainage Process

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 185701, “Proxy-Based Metamodeling Optimization of the Gas-Assisted-Gravity-Drainage Process in Heterogeneous Sandstone Reservoirs,” by Watheq J. Al-Mudhafar and Dandina N. Rao, SPE, Louisiana State University, prepared for the 2017 SPE Western Regional Meeting, Bakersfield, California, USA, 23–27 April. The paper has not been peer reviewed. Unlike continuous gas injection and water-alternating-gas injection, gas-assisted gravity drainage (GAGD) takes advantage of the natural segregation of reservoir fluids to provide gravity-stable oil displacement. The feasibility of carbon dioxide (CO2) GAGD was investigated for immiscible injection through equation-of-state compositional reservoir simulation with design of experiments (DOE) and proxy modeling to obtain the optimal future-performance scenario. After history matching, Latin-hypercube sampling (LHS) was used as a low-discrepancy and more-uniform DOE approach to create hundreds of simulation runs to construct a proxy-based optimization approach. Introduction Many enhanced-oil-recovery studies have been conducted for CO2-flooding optimization in real oil fields; however, to the best of the authors’ knowledge, no study has been made for GAGD implementation and optimization in a real oil field. To implement the optimization process, a full compositional reservoir simulation was constructed to evaluate the reservoir performance through CO2-GAGD flooding for 10 years of future reservoir prediction. Then, proxy-model optimization was conducted through manipulating the operational decision factors that influence the CO2 flooding through GAGD by means of DOE. More specifically, DOE and proxy modeling were combined to create a simplified alternative (metamodel) to the compositional reservoir simulation to optimize the operational decision factors affecting GAGD. Four proxy models were adopted and validated as metamodels for the compositional reservoir simulator: polynomial proxy model, multivariate additive regression splines (MARS), fuzzy logic/genetic algorithm, and generalized boosted modeling (GBM). GAGD The GAGD concept involves placing horizontal producers at the bottom of a pay zone. Then, gas is injected in a gravity-stable mode, either immiscible or miscible, through vertical wells from the top of the formation. Because of gravity segregation resulting from the distinct fluid densities at reservoir conditions, the injected gas accumulates at the top of the pay zone to form a gas cap, providing gravity-stable oil displacement that drains down toward horizontal producers and leading to better sweep efficiency and higher oil recovery. Fig. 1 illustrates the basic concepts of GAGD. GAGD Simulation The main-pay reservoir in the South Rumaila oil field was selected for a full, detailed compositional reservoir simulation to enhance oil recovery through GAGD. The main pay has only three lithology types—sand, shaly sand, and shale—with distinct areal permeability distributions. A high-resolution geostatistical reservoir model was reconstructed for lithofacies and petrophysical properties considering multiple-point geostatistics.

A three-dimensional high-resolution reservoir model of the Eocene Shahejie Formation in Bohai Bay Basin, integrating stratigraphic forward modeling and geostatistics

Reservoir characterization based on geostatistics method requires well constraints (e.g. seismic data with high quality) to predict inter-well reservoir quality that is conformed to geological laws. Nevertheless, the resolution of seismic data in multiple basins or reservoirs is not high enough to recognize the distribution of different types of sand bodies. In this paper, we propose a new method to improve the precision of reservoir characterization: reservoir modeling with the constrains of sedimentary process model and sedimentary microfacies. We employed stratigraphic forward modeling, a process-based method, to constrain the reservoir modeling in one oil-bearing interval of the third member of Eocene Shahejie Formation in J-Oilfield of Liaoxi Sag, Bohai Bay Basin.We divide reservoir modeling into two orders using different types of constrains. In the first order, we use the simulated shale model from stratigraphic forward model that is corrected by wells data as a 3D trend volume to constrain the reservoir sand-shale modeling. In the second order, different types of sedimentary microfacies in the sandy part of the model are further recognized and simulated within the constrains of sedimentary microfacies maps. Consequently, the porosity, permeability and oil saturation are modeled under the control of precise sedimentary microfacies model. The high-resolution reservoir model shows that the porosity, permeability and oil saturation of distributary channel is generally above 20%, 10md and 50%, respectively, which are much higher than that of other types of sedimentary microfacies. It can be concluded that comparing to other types of sedimentary facies, distributary channel has better physical properties and more oil accumulation in the fan-delta front and therefore is the most favorable zones for petroleum development in the research area.

Streamline-Based Transport Tomography With Distributed Water Arrival Times

Summary Traditional history matching involves calibration of reservoir models by use of well response such as production or tracer data aggregated during multiple producing intervals. With the advent of novel tracer technologies, we now can obtain distributed water or tracer arrival-time information along the length of horizontal or vertical wellbores. This provides significantly improved flow resolution for detailed reservoir characterization through inversion of distributed water or tracer arrival times in a manner analogous to travel tomography in geophysics. In this paper, we present an efficient approach to incorporate novel tracer-surveillance data and distributed water arrival-time information during history matching of high-resolution reservoir models. Our approach relies on a streamline-based work flow that analytically computes the sensitivity of water-arrival times with respect to reservoir heterogeneity, specifically porosity and permeability variations. The sensitivities relate the changes in arrival time to small perturbations in reservoir properties and can be obtained efficiently with the streamline-based approach with a single flow simulation. This makes the approach particularly well-suited for high-resolution reservoir characterization. Finally, the sensitivities are used in conjunction with an iterative inversion algorithm to update the reservoir models with existing and proven techniques from seismic tomography. The power and utility of our proposed approach are demonstrated with both synthetic and field examples. These include the SPE benchmark Brugge field case and an offshore field in North America. Compared with traditional history-matching techniques, the proposed tomographic approach is shown to result in improved resolution of heterogeneity through matching of water-arrival time at individual completions in addition to the aggregated well-production response. This results in improved performance predictions and better identification of bypassed oil for infill targeting and enhanced-oil-recovery (EOR) applications.

CO2 Injection in a Saline Formation: Pre-Injection Reservoir Modeling and Uncertainty Analysis for Illinois Basin – Decatur Project

The Illinois Basin – Decatur Project (IBDP), funded by the United States Department of Energy National Energy Technology Laboratory, aims to inject one million tonnes of carbon dioxide (CO2) into the Mount Simon Sandstone. The injection of CO2 started in November 2011 and will extend over three years. Pre-injection reservoir modeling and uncertainty analysis has been carried out to obtain probabilistic predictions of storage performance indicators.Key characteristics of the storage site such as capacity, injectivity and containment are often evaluated based on the modeling results. Simulation studies for IBDP started in 2007 using general regional knowledge. Over time, simulations have increased in complexity and have become more representative of the Mount Simon Sandstone as more data have been acquired. In this study, a high resolution reservoir model consistent with all available data was developed. Water injection/fall-off tests were used to history match and calibrate the petrophysical properties of the injection zone. Using a high-resolution grid around the wellbore in the injection zone allowed us to quantify uncertainties in plume shape and size. Lateral and vertical extent of the model was sufficient to account for the injection induced differential pressure front defined by regulatory requirements. Using two hundred realizations, a detailed uncertainty analysis was performed to provide probabilistic evaluation of the storage site performance. Irreducible water (H2O) saturation, residual CO2 saturation, CO2 relative permeability at the residual H2O saturation, and horizontal-vertical permeability ratio were among the parameters considered uncertain. These parameters were studied to quantify the uncertainties of performance factors including containment of CO2, injectivity, and dynamic mass partitioning (mobile, residually trapped, and dissolved). On the reservoir scale, we analyze uncertainty in CO2 saturation and pressure profiles, and its potential implications for regulatory requirements for Area of Review (AoR) evolution during the life of the project.

Results of the Brugge Benchmark Study for Flooding Optimization and History Matching

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 119094," Results of the Brugge Benchmark Study for Flooding Optimization and History Matching," by E. Peters, TNO; R.J. Arts, TNO and Delft University of Technology; and G.K. Brouwer and C.R. Geel, SPE, TNO, prepared for the 2009 SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2-4 February. The paper has not been peer reviewed. In preparation for the SPE Closed-Loop Reservoir Management Workshop held in Bruges, Belgium, in June 2008, a unique benchmark project to test the use of flooding-optimization and history-matching methods was organized in the form of an interactive competition during the months preceding the workshop. In total, nine groups participated and presented their results during the workshop. Introduction Before the Bruges workshop, participants were allowed to conduct a closed-loop waterflood-optimization case study. TNO prepared a high-resolution reservoir model, dubbed the Brugge field, which served as the truth, and a data set was made available in January 2008. The data set consisted of the first 10 years of the production history of the field (including measurement errors), inverted time-lapse seismic data in terms of (uncertain) pressures and saturations, 104 scaled-up realizations of a 3D geological model, all the input for creating a reservoir model, and economic parameters (price and discount rate) for oil and water to calculate net present value (NPV). The goal for the participants was to optimize the field NPV for the last 20 years. To do this, they first had to create a history-matched model or set of models from the data provided. Next, they had to optimize the waterflood strategy for the 30 wells for the next 10 years. All 10 injectors and 20 producers could be operated with three separately operable completions. The optimization strategy for years 11 through 20 then could be sent to TNO for testing on the Brugge field. The response could be used to improve the history match and the optimization for the last 10 years. The control strategy for the last 10 years could again be sent to TNO for testing on the Brugge field. The full-length paper presents a summary of the results presented at Session 2 of the workshop. Because several participants continued working on the problem after the workshop, a summary of those results also is provided in the paper. To make the exercise more representative of a true closed-loop optimization, the exercise was repeated with one participant with a higher frequency of feedback. These results also are presented in the paper.

Stochastic surface modeling of deepwater depositional systems for improved reservoir models

High resolution reservoir models are often required for resource assessment and reservoir management. The reservoir models must reproduce geologically realistic heterogeneity that may affect predictions. An important heterogeneity in many deepwater depositional systems consists of facies variations controlled by geological events that lead to lobe geometries. A surface-based geostatistical scheme is presented to model high resolution lobes and their associated stacking patterns in a geometrically realistic manner. The lobes are constrained to match well data. Facies, porosity and permeability are then modeled within the lobe framework. Application to a reservoir with 36 wells demonstrates the practical applicability of the approach. This surface-based method may be readily extended to model other geometries in various reservoir settings.