A challenging step in reservoir modeling is capturing fluid composition variation. This is a complex task as fluid samples taken from wells in different areas of the reservoir usually have large areal and vertical compositional variation. Modeling representative composition variation with depth in the presence of multiple samples is critical for reservoir simulation and hydrocarbon initially in place assessment, and, on the other hand, a technically challenging task. In this paper, we present an automated workflow integrated in a commercial exploration and production (E&P) software that addresses compositional variation for reservoir simulation model initialization for multiple fluid samples. Composition variation with depth requires a depth window, number of depth points, composition, temperature, pressure and reference depth for all fluid samples. Using a specific equation of state (EoS), the workflow is executed for every fluid sample by performing compositional variation with depth based on Gibbs conditions for thermodynamic equilibrium. The output of this step is a composition variation with depth distribution for every fluid sample. Finally, the best-matching model is chosen by comparing each model results with the data for all existing fluid samples. The proposed workflow was tested using a specific EoS in a reservoir with several fluid samples. One composition variation with depth model was generated for every fluid sample. In the next step, all models were evaluated by calculating the average errors between the model and each fluid sample. Finally, the best-matching models were selected, and the results were evaluated. It was observed that the best-matching models were able to accurately predict the pressure and saturation pressure for large number of fluid samples. The proposed workflow was also integrated into an industry-leading E&P modeling software platform to serve as an automated functionality that outputs the required files to perform initialization with equilibration of the dynamic reservoir model. Capturing fluid composition variation in the reservoir is an important step in reservoir modeling. The proposed work presents an automated workflow that generates the best-matching composition variation with depth model for multiple samples. Using traditional approaches, this is a challenging and time-consuming step as fluid samples taken from wells in different areas of a reservoir can have significant areal compositional variation.

Reservoir simulation is essential for various reservoir engineering processes such as history matching and field development plan optimization but is typically an intensive and time-consuming process. The aim of this study is to compare various deep-learning algorithms for constructing a machine-learning (ML) proxy model, which reproduces the behavior of a reservoir simulator and results in significant speedup compared to running the numerical simulator. Initially, we generate an ensemble of realizations via the reservoir simulator to train the different ML algorithms. The data set consists of a comprehensive set of uncertainty parameters and the corresponding simulation data across all wells. The system utilizes recent advances in deep learning based on deep neural networks, convolutional neural networks, and autoencoders to create machine-learning-based proxy models that predict production and injection profiles as well as the bottomhole pressure of all wells. Thus, the proposed workflows replace the time-consuming simulation process with fast and efficient proxy models. In this work we provide a comparative study of various ML-based algorithms utilizing deep neural networks and convolutional neural networks for constructing a surrogate reservoir model. The trained models can simulate the behavior of the physics-based reservoir simulator by correlating uncertainty parameters to various history-matched reservoir properties. The algorithms were tested on a mature oilfield with a notable number of wells and several decades of production and injection data. We analyze the performance of each ML approach and provide recommendations on the optimal one. The best performing workflow for building the ML proxy model consists of two steps. The first step uses stacked autoencoders to learn a low-dimensional latent space representation of the highly dimensional simulation data. This step allows to reduce the complexity of predicting the simulation data and enhances the prediction quality. The following step constructs an ML model to predict the latent space features from input uncertainty parameters and produces highly accurate results. Reservoir simulation is of paramount importance for various reservoir engineering workflows. Traditional approaches require running physics-based simulators for multiple iterations, which results in time-consuming and labor-intensive processes. We implement and compare several deep-learning-based methods to construct ML proxy models that automate and remarkably reduce the runtime of the reservoir simulation process.

Infill well placement performed as part of field-development planning is traditionally performed by identifying areas of high remaining mobile hydrocarbons and good reservoir rock quality to be targeted. The identification of hotspots was also largely performed on single-model realizations and, therefore, not robust considering the reservoir characterization uncertainties. Increasing efforts were put into incorporating the uncertainties as a key element of the infill well placement workflow by computing probability maps to identify the hotspots with higher chances of success for infill production. The maps were still generated solely based on dynamic reservoir-simulation model results. In this paper we present an intelligent workflow that integrates the opportunity index probability maps concept derived exclusively from dynamic reservoir simulation models, with drilling risk maps derived from drilling data analysis and completions quality maps derived from geomechanical studies, and artificial-intelligence-driven reservoir target classification. The integration provides more depth in the hotspot selection by identifying the most productive and feasible locations for infill drilling. The locations are then used for well placement and trajectory design optimization. The well trajectories optimize factors in the hotspot locations, locations of existing drilling centers, surface topology for new drilling centers to be designed, numbers of available slots on each drilling center, and capital costs such as drilling economics and drilling center cost. Infill injection wells are placed in conjunction with the infill production wells either following a pattern-type of design or peripheral injection. The designed wells are evaluated via an automated pipeline using reservoir simulation where the set of wells will be tested against the ensemble of realizations under uncertainty. A probabilistic approach is taken for the infill well performance and the economics evaluation for candidate screening and selection for the field-development plan optimization. This approach provides higher confidence in the decision making through the early integration of drilling risks and geomechanics data, and provides a more robust assessment of the technical and economic performance of the proposed infill wells under uncertainty. The solution combines various concepts including opportunity index, advanced ML methods for target identification, as well as multidisciplinary integration for well target identification. Well trajectory design evaluation considering both production and injection wells and the evaluation of the performance of the proposed candidates under uncertainty in this context provides more robust results under uncertainty compared to widely used industry practices that lack integration and uncertainty considerations.

Capturing fluid composition variation and distribution in the reservoir is an essential first step for reservoir modeling. Vertical fluid composition variation is commonly considered. However, fluid samples taken from wells in different areas of a reservoir can highlight significant areal variation in the composition to be modeled. Fluid contacts may be tilted, and despite many studies on the subject, the setup is not straightforward. The combination of all three makes the initialization challenging and time consuming. In this paper we describe an automated workflow that integrates fluid sample data and petrophysical data to generate an initial fluid composition distribution that captures vertical and areal composition variations and accurately computes the initial fluid distribution to represent tilted contact configurations and their associated transition zones. Fluid sample data such as composition, pressure, temperature, and sampling depth are provided to an engine that computes composition variation with depth for each sample based on the equation of state (EOS) that characterizes the fluid behavior of the reservoir. The generated composition variation with depth for each sample is spatially distributed at their associated wells and is used to compute the areal composition distribution between the wells in the reservoir. This results in a tridimensional distribution of each fluid component representing the fluid model that captures both vertical and areal composition variations. In parallel, saturation height functions and hysteresis models are used to automatically generate drainage capillary pressure data and associated imbibition and scanning curves used to capture the drainage and imbibition processes responsible for the paleo and current water saturation distribution in the reservoir. The fully automated reservoir initialization process accounts for all available pressure-volume-temperature (PVT) samples. The solution is portable, significantly faster, and accurately captures complex reservoir geology and reservoir history. We present field examples of the proposed approach and illustrate its flexibility and associated comprehensiveness and efficiency. The complete automation of complex initialization methods considering areal and vertical composition variation, combined with tilted contacts modeling, is helping to resolve significant challenges faced across the industry.

Achieving a high-quality history match is critical to understand reservoir uncertainties and perform reliable field-development planning. Classical approaches require large uncertainty studies to be conducted with reservoir-simulation models, and optimization techniques would be applied to reach a configuration where a minimum error is achieved for the history match. Such techniques are computationally heavy, because all reservoir simulations are run in both uncertainty studies and optimization processes. To reduce the computing requirements during the optimization process, we propose to create a robust deep-learning model based on the hidden relationships between the uncertainty parameters and the reservoir-simulation results that can operate as a surrogate model for computationally intensive reservoir-simulation models. In this paper, we present a workflow that combines a deep-learning, machine-learning (ML) model with an optimizer to automate the history-matching process. Initially, the reservoir simulator is run to generate an ensemble of realizations to provide a comprehensive set of data relating the history-matching uncertainty parameters and the associated reservoir-simulation results. This data is used to train a deep-learning model to predict reservoir-simulation results for all wells and relevant properties for history matching from a set of the selected history-matching uncertainty parameters. This deep-learning model is used as a proxy to replace the reservoir-simulation model and to reduce the computational overhead caused by running the reservoir simulator. The optimization solution embeds the trained ML model and aims to deliver a set of uncertainty parameters that minimizes the mismatch between simulation results and historical data. At each optimization iteration, the ML model is used to predict the well-level reservoir-simulation results. At the end of the optimization process, the optimal parameters suggested by the optimizer are then validated by running the reservoir simulator. The proposed work achieves high-quality results by leveraging advanced artificial-intelligence techniques, thus automating and significantly accelerating the history-matching process. The use of uncertainty parameters as input to the deep-learning model, and the model's ability to predict production/injection/pressure profiles for all wells is a unique methodology. Furthermore, the combination of the deep-learning surrogate reservoir model with optimization methods to resolve history-matching problems is advancing the industry's practices on the subject.

This paper presents an integrated subsurface study that focuses on delivering field development planning of two reservoirs via comprehensive reservoir characterization workflows. The upper gas reservoir and lower oil reservoir are in communication across a major fault in the crest area of the structure. Gas from the upper reservoir, which is not under development, is being produced along with some oil producers from the oil reservoir as per acquired surveillance data. Pressure depletion is observed in observer wells of the upper reservoir, which substantiate both reservoirs communication. The oil reservoir is on production since 1994, under miscible hydrocarbon water alternating gas injection (HCWAG) and carbon dioxide (CO2) injection. The currently implemented development plan has been facing several complexities and challenges including, but not limited to, maintaining miscibility conditions, sustainability of production and injection in view of reservoirs communication, reservoir modeling challenges, suitability of monitoring strategy, associated operating costs and expansion of field development in newly appraised areas. In this study, an assessment of multiple alternative field development scenarios was conducted; with an aim to tackle field management and reservoir challenges. It commenced by a comprehensive synthesis of seismic, petrophysical (including extensive core characterizations), geological, production and reservoir engineering data to ensure data adequacy and effectiveness for development planning. The process was followed by evaluation of the historical reservoir management, HCWAG and CO2 injection practices using advanced analytics to identify areas for improvement and accelerate decision making process. The identified areas of improvement were incorporated into a dynamic model via diverse set of field management logics to screen wide range of scenarios. In the final step, the optimal scenarios were selected, in line of having strong economic indicators, honoring operational constraints, corporate business plan and strategic objectives. The comprehensive and flexible field management logic was set up to target different challenges and was used to extensively screen hundreds of different field development scenarios varying several parameters. Examples of such parameters are WAG ratio, injection pressures for both water/gas and CO2, cycle duration, well placement, reservoir production and injection guidelines, different co-development production schemes coupled with static and dynamic uncertainty properties against incremental oil production and discounted cash flow. The simulation results were analyzed using standardized approach where a number of key indicators was cross-referenced to produce optimal field development scenarios with regards to co-development effect of both reservoirs, miscibility conditions, balanced pressure depletion, harmonized sweep as well as robust discounted cash flow. Strong management support, multi-disciplinary data integration, agility of decision making and revisions in a controlled timeframe are considered as the key pillars for success of this study. The adopted workflow covers subsurface modeling aspects from A-Z and following reservoir characterization and modeling best practices. The methodology applied in this study uses an integrated subsurface structured approach to tackle reservoirs challenges and co-development, generate alternative development options leveraging on data analytics techniques and advanced field management strategies.

Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.

Brownfield field development plans (FDP) must be revisited on a regular basis to ensure the generation of production enhancement opportunities and to unlock challenging untapped reserves. However, for decades, the conventional workflows have remained largely unchanged, inefficient, and time-consuming. The aim of this paper is to demonstrate that combination of the cutting-edge cloud computing technology along with artificial intelligence (AI) and machine learning (ML) solutions enable an optimization plan to be delivered in weeks rather than months with higher confidence. During this FDP optimization process, every stage necessitates the use of smart components (AI & ML techniques) starting from reservoir/production data analytics to history match and forecast. A combined cloud computing and AI solutions are introduced. First, several static and dynamic uncertainty parameters are identified, which are inherited from static modelling and the history match. Second, the elastic cloud computing technology is harnessed to perform hundreds to thousands of history match scenarios with the uncertainty parameters in a much shorter period. Then AI techniques are applied to extract the dominant key features and determine the most likely values. During the FDP optimization process, the data liberation paved the way for intelligent well placement which identifies the "sweet spots" using a probabilistic approach, facilitating the identification and quantification of by-passed oil. The use of AI-assisted analytics revealed how the gas-oil ratio behavior of various wells drilled at various locations in the field changed over time. It also explained why this behavior was observed in one region of the reservoir when another nearby reservoir was not suffering from the same phenomenon. The cloud computing technology allowed to screen hundreds of uncertainty cases using high-resolution reservoir simulator within an hour. The results of the screening runs were fed into an AI optimizer, which produced the best possible combination of uncertainty parameters, resulting in an ensemble of history-matched cases with the lowest mismatch objective functions. We used an intuitive history matching analysis solution that can visualize mismatch quality of all wells of various parameters in an automated manner to determine the history matching quality of an ensemble of cases. Finally, the cloud ecosystem's data liberation capability enabled the implementation of an intelligent algorithm for the identification of new infill wells. The approach serves as a benchmark for optimizing FDP of any reservoir by orders of magnitude faster compared to conventional workflows. The methodology is unique in that it uses cloud computing technology and cutting-edge AI methods to create an integrated intelligent framework for FDP that generates rapid insights and reliable results, accelerates decision making, and speeds up the entire process by orders of magnitude.

Artificial lift is generally required to extend field-life beyond the period of natural flow. In the course of the reviews and studies we performed, we noted artificial lift had not been given sufficient consideration at initial design phase. Due to the pressure to maintain the field production, operators often choose a phased artificial lift deployment to cope with the learning curve, risks and uncertainties, which typically includes: ▪A first phase (Phase-1) of artificial lift is screening and concept testing to evaluate artificial lift options;▪A second phase (Phase-2) involves deployment to a group of wells for a selected option;▪A final phase (Phase-3) is the full field implementation. This paper presents an efficient and effective workflow to select the most suitable candidates for Phase-2. Lessons learnt based on the implementation of workflow in real-life case studies will be shared as well: ▪First, a well selection process is performed to review inactive strings and/or low performers that are most impacted by such issues as reservoir pressure declined / high water production. Cased-hole saturation logs (PNC log) and static pressure log measurements are used to build an understanding of areal pressure and water saturation distribution and to further identify localized pressure sinks and watered-out areas. The understanding gained upon reviewing the well-based surveillance data is used to identify candidates for artificial lift application.▪Then, the well candidates which are grouped by categories of issues and different reactivation options are screened for each group of wells. A score-based ranking process is applied to sort candidates on urgency, value or impact of artificial lift and possibly other criteria, using production history and surveillance data.▪Finally, results per well are reconsolidated to reservoir / field level and used to support a field-wide concept, which allows to extract valuable field scale requirements (e.g. the determination of the gas-lift volume requirement for gas-lift planning etc.). This workflow greately helps to understand the root cause of performance issues and to select the most suitable artificial lift concept (including considering well pre-conditioning needs such as water shut-off). It leads to exploring and defining artificial lift concepts and helps to focus on the field development plan and detail engineering study at a very early stage.

Sharjah National Oil Corporation (SNOC) operates three onshore reservoirs in the Emirate of Sharjah. The reservoir simulation models use compositional modelling to capture the fluid dynamics in mature, low porosity highly fractured gas condensate fields. The scope of this project was to improve the reservoir characterization by investigating and overcoming lack of water production in compositional models for effective EOR and gas storage strategies. Water cut of 30%+ comprised of a combination of produced and condensed water in a reservoir with no active aquifer, thus posing a modelling challenge combined with a lack of comprehensive historical PVT data. All existing PVT reports in the database were retrieved and a comprehensive quality check was performed. The best possible PVT results for each field were short-listed and taken as reference datasets for validating the compositional EoS in a depleted field. A new EOS was generated for these fields based on legacy PVT data combined with 38+ years of production data. A shortfall of this new EOS was the inability to produce condensed water as observed in the field with Chloride counts less than 1500 ppm. To rectify this low water production mismatch, a blind test was conducted introducing water as a component in the EoS in the simulation model to see the effect. Moreover, extensive scale problems in any of the wells of 30-year-old mature assets leading to regular interventions never occurred in the asset's operational history. As expected, mobility of the fluids in the system had changed and low salinity condensed water was seen to have a good match. Liberated water was traced at the surface to confirm water production rate of the same order of magnitude as observed in production data. Due to overwhelming water production rates from the trial test, SNOC decided to perform a comprehensive extended PVT study. The naturally fractured carbonates were subjected to geological and material balance study and the data indicated an absence of active aquifers, which made it difficult to match observed water production in simulation models. To effectively plan future EOR projects like gas storage, it was necessary to model the effects of water and its interaction with injected fluids in the reservoir while honouring low water movement in the subsurface. The paper provides a novel workflow for generation of the compositional equation of state with water as a component in retrograde condensate fields. The workflow followed the lumping of hydrocarbon components to minimise runtime and capture maximum possible fluid dynamics in the reservoir without compromising the fluid properties observed in the PVT lab. It was also vital for the simulation model to honour the production history spanning over three decades. It also highlights the ability and importance of including water as an EOS component to effectively capture the condensed water in the reservoirs that many works of literature and simulators are unable to provide insight on.

Abstract Objectives/Scope Oil production optimization under economical and operational constraints is of paramount importance to most E&P companies. Enhancing production from mature oilfields is often achieved through artificial lift operations. Decline in well performance is observed in most aging giant waterflooded reservoirs in Abu Dhabi. The objective of this paper is to propose an efficient method to improve well performance while optimizing long-term field development plans both for minimum investment and maximum recovery. Methods, Procedures, Process This paper presents a dynamic field management strategy to optimize the gas lift allocation with groups of wells performing under different operational constraints. The gas lift allocation optimization is the cornerstone of the field development plan optimization to increase the recovery from existing wells by applying optimal gas lift injection, to optimize the infill drilling planning to achieve the mandated target, and to minimize the requirements for infill drilling. Despite the large number of gas-lift optimization procedures proposed in the literature, this paper describes a very efficient methodology that is suitable for long-term field developments. In addition to optimization of individual well performance, we propose a new logic for gas lift allocation based upon well potential, history, and performance and the long-term investment planned for the field. This required dynamically to change the logic of allocation mechanism in real-time management manner which proved its efficiency when applied to a giant waterflooded reservoir suffering from water override and high water cut. The method’s logics implemented in terms of fields entities within a simulator field management framework are independent of reservoir model and can be easily scaled and applied to any other reservoir. Results, Observations, Conclusions The implementation of the proposed gas-lift based optimization strategy allowed to achieve the mandated field production by increasing the lifetime of existing wells, while delaying and reducing the number of infill wells. This resulted in a much more economic strategy while minimizing cost and risks of drilling activities. Applying this unique workflow, allow to activate the gas lift optimization function while the field still on plateau, which was not possible before following the default logic in all the simulators. The result showed significant improvement on the field scale in terms of gas lift and facility requirements as well as field production performance. In addition to that, realistic gas lift requirements was observed compared with the conventional methods. Economic analysis is still ongoing, however, huge cost saving is expected and will be presented in the technical paper. Novel/Additive Information In current economic situation, making field development more profitable can be achieved by applying the proposed approach which will help establishing solid foundation to develop fields at scale. This is due to the flexibility and intelligence of the logics applied in the methodology which combine both constraints from operations and uncertainty in the long run. As described, the method is generic and can be applied to other fields following the same workflow steps.

Abstract Maturing giant and super giant fields have, typically, an extensive data set ranging from seismic data to time lapse surveillance data. The data set, associated studies and models together with driving values defined by ADNOC form the foundations of long-term plans, field development plans, business plans, reservoir management plans and production optimization plans. Ensuring the adequacy and optimality of the plans and their capability to meet their prescribed objectives is a very challenging task that requires unique assessment workflows. ADNOC is undertaking multiple fast-tracked Integrated Reservoir Performance and Production Sustainability Assurance (IPR) projects with the above objectives in mind. In this paper we will share the experience gained through the execution of such projects and the way this experience helped refining the workflows and the associated value. We designed and applied unique workflows that combine "Bottom up" approaches by technical discipline with the "Top down" focusing on those factors that have the largest impact on the scope of the plans and the ability to deliver the expected outcomes. The identified issues and opportunities are presented in terms of their impact on volumes in place, reserves, facility, drilling plans, surveillance plans, modeling, etc. and are associated urgency indicators to help prioritizing actions. The adopted integrated methodology and workflows helped in identifying and ranking various issues related to the reservoir models (static and dynamic) and many recommendations on how to tackle these issues in the new generation models were provided. Advanced reservoir management workflows were generated towards optimal production and injection balancing as well as to better manage the water flood and identify the most offending injectors. Many scenarios were explored to check the different elements of the full field development plan and ongoing projects, considering all the identified uncertainties. Many recommendations were provided, accordingly, concerning infill drilling and future gas lift program. Specific workflows were generated to optimize the performance of the existing gas lift wells and to identify and rank the future wells that will need gas lift according to their urgency, hence confirm the gas lift compression capacity that was subject of an ongoing project. Key enabler to complete the project in the planed time frame was the use of cutting-edge modeling technology which has a drastic impact on the project and the team's capability to explore a comprehensive set of scenarios with associated sensitivities and uncertainty analysis providing unique insights towards more optimal decisions and clearer way forward.

The Mishrif formation in Abu Dhabi comprises progradational shelf margin facies. The western platform sediments are characterized by stacked clinoforms of clean high energy carbonates, with generally good reservoir properties at the top deteriorating gradually toward the west flank. In contrast in the east the formation is thicker and characterized by more differentiated, coarsening, shoaling- upwards sequences. High quality reservoir facies occur only near the prograding shelf edges. The reservoir in this study had finely layered pillar grid models of the field using onlap and offlap layering to capture the vertical property heterogeneity and layering within the clinoforms implied by the depositional environment. However, this grid structure posed challenges for flow simulation as there were unphysical barriers and connections across the clinoform boundaries caused by the pinching out layers at boundaries between clinoform units. By construction, in a depogrid each clinoform may be independently gridded with coordinate lines that do not need to be continuous through the vertical extent of the reservoir. Layers within a sequence can truncate arbitrarily against bounding discontinuities since the cells are polyhedral and the grid globally unstructured. Therefore, an evaluation of the depogrid cut-cell grid was undertaken for the reservoir. A volume-based model was constructed using horizon surfaces and fault surfaces extracted from the pillar grid model, and the depospace transform calculated. The depogrid was created using the same areal resolution as the pillar grid. The layer parameters were set to give approximately the same number of layers in each zone as the pillar grid. The rock type base properties from the pillar grid were upscaled onto the depogrid, in the interest of time, to populate the static depogrid model. Reservoir fluid properties, relative permeability, and capillary pressure curves were taken from existing simulation models of the field. A high-resolution reservoir simulator was then run on the depogrid model. Run time comparisons of the pillar and depogrid simulations were made The evaluation concluded that the cut-cell stratigraphically layered depogrid provided a more geologically consistent representation of the complex stacked clinoform structural elements and required significantly fewer grid layers to achieve the required vertical resolution. The depogrid simulations could represent the expected connectivity for flow. Improved run times were observed

Abstract Characterization of polymer flow behavior, using data from laboratory measurements, is an essential step in the development of predictive reservoir simulation models of polymer enhanced oil recovery (EOR). We describe a methodology for calibrating simulation parameters, using detailed observations from multiphase coreflood experiments and taking account of the core geometry and polymer rheology. A series of multiphase brine and polymer floods were performed on an oil-filled sandstone core plug, at different concentrations and at different flow rates. Measured data included pressure drop along the core and oil saturation derived from nuclear magnetic resonance. A fine-scale, three-dimensional simulation grid was designed to capture accurately the geometry of the experimental apparatus, including the coreholder platens for fluid injection and extraction. The simulation input parameters were adjusted to match the experimental results of each coreflood, and sensitivity studies were performed to assess the impact of uncertainties. In the coreflood experiments, different recovery efficiencies were observed, depending on the type of aqueous solution and the injection flow rates. An initial relative permeability model was defined by matching a constant-rate brine flood. Further tests were performed for brine, xanthan, and hydrolyzed polyacrylamide (HPAM) solutions, at incrementally increasing flow rates, and the resulting residual oil saturations were used to define capillary desaturation curves. Experimental data also showed that the apparent polymer solution viscosity at different shear rates differed from the values predicted by conventional rheology. To represent this behavior in the simulator, a new method for estimating the apparent aqueous-phase viscosity during multiphase flow has been developed and validated. At each step in the simulation study, sensitivity studies were used to check the quality of the experimental results, and unexpected behavior was explained or corrected. High accuracy is required when designing EOR processes. In this project, close collaboration between research scientists and simulation experts led to development of innovative workflows to interpret experimental results, build the simulation grid, and characterize the polymer properties.

Abstract A dual-porosity simulation model is a coarse upscaled representation of a naturally fractured reservoir. Fluid transfer between the matrix and the fracture is described by a matrix-fracture transfer function, which is dependent on a shape factor. However, the basic formulation assumes pseudosteady-state conditions and requires modifications to capture transient effects. This paper describes the use of a dynamic shape factor for matrix-fracture transmissibility with block-to-block effects to improve the simulation of oil recovery in a dual-porosity model. A simple fine-grid single-porosity model is compared with a coarse-grid dual-porosity equivalent for a gas-oil system under gravity drainage without capillary effects. A time-varying relationship between the shape factor and the matrix oil saturation is derived by numerical analysis. Vertical block-to-block connections are included in the model to match oil reinfiltration from the fractures to the matrix. The saturation-dependent shape factor correlation is generalized for other matrix block sizes. An improved match to fine-grid recovery is achieved in the dual-porosity simulation through use of a dynamic transfer function and block-to-block effects. The methodology is shown to be appropriate for a range of matrix sizes and, with different relative permeability curves, for the matrix blocks. However, attention must be paid to the relationship between simulation grid cell size and geologic matrix block size. Additional study of block-to-block flows is recommended to further improve the predictive capability of the model. Fast and accurate simulation of flow in naturally fractured reservoirs is often difficult to achieve. The standard matrix-fracture transfer model is unsuitable for many situations. In the example presented here, we show how a coarse simulation model can be used effectively to capture variations in dynamic matrix-fracture transfer behavior over time for a specific case. The approach can be generalized and applied to similar studies.