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ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
- Conference date: September 3-6, 2018
- Location: Barcelona, Spain
- Published: 03 September 2018
21 - 40 of 172 results
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Numerical Modelling Of Electromagnetic-Based Hybrid Eor Techniques For Bitumen Recovery
Authors A. Sadeghi, H. Hassanzadeh, T. Harding, B. MacFarlane and P. HaghighatSummaryIn recent years, electromagnetic heating (EMH) has been the focus of ever-increasing theoretical and experimental studies both in the laboratory and field scale to examine if it can be used to heat up the geomaterials in field scale. EM-solvent based bitumen recovery methods, such as ESEIEH pilot in Athabasca oil field, mainly use radiofrequency waves to generate heat in reservoir, and thereby reduce the viscosity of the bitumen to mobilize it.
EM wave propagation in a reservoir poses a coupled multi-physical process that involves not only the heat transfer and fluid flow, but also EM field distribution, which currently, a non-coupled approach is followed by industry using a conventional thermal simulator and an external electromagnetic wave solver where both are linked through an interface. To address the mentioned issues, the present study presents a coupled compositional numerical modeling approach to explore the EM heating phenomena pertinent to fluid flow in oil sand reservoirs. Generic field equations governing the coupling between energy equation and EM wave propagation are derived using the Maxwell’s equations.
The developed in-house numerical simulator is used to study the importance of EM-induced volumetric heat generation in a multiphasic heterogeneous oil sand reservoir. Results reveals that electromagnetic heating can be a promising method for the development of low quality oil sands. EMH moderates the amount of needed energy and also cuts the emitted CO2 compare to SAGD process. Furthermore, the operating temperature of vapor chamber is less than 160 °C for the optimized EM-solvent scenario, while it is more than 220 °C for the SAGD method.
We presented a coupled approach for modeling of electromagnetic heating of oil sands. The developed model can be used as a toolbox to perform sensitivity analysis, design of experimental setups and pilot scale implementation of electromagnetic heating of oil sands.
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Fully-Implicit Solvers For Coupled Poromechanics Of Fractured Reservoirs
Authors N. Castelletto, M. Ferronato, A. Franceschini, R.R. Settgast and J.A. WhiteSummaryIn this work we present a family of preconditioners for accelerating the fully-implicit solution of linear systems encountered in two practical applications: (i) Lagrange multiplier-based fault mechanics simulations using a mixed finite element approach, and (ii) multiphase poromechanics based on a mixed finite element-finite volume formulation. We consider block preconditioning strategies and focus on various Schur complement approximations that are based on a combination of physical and algebraic arguments. The performance of the proposed framework is illustrated using two challenging numerical examples---a synthetic fault mechanics test with manufactured solution and a large-scale water flooding problem.
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Conditioning Reservoir Models To Pressure Transients By Iterative Ensemble Smoother
Authors A. Khrulenko, A. Shchipanov and R. BerenblyumSummaryRecent developments in permanent reservoir monitoring and well surveillance enable accurate, real-time downhole pressure measurements, providing enormous amount of data.
A number of previous studies had shown that integrating well test data into reservoir models improves significantly their predicting capability.
In our study, the iterative Ensemble Smoother was applied to condition permeability field to well test data. 1D and 2D synthetic reservoir models were used to investigate the method performance with respect to measurement error and localization which we consider important from practical point of view.
At first, we analyzed the influence of measurement error. Despite of high accuracy of the modern pressure gauges, the pressure data are often quite noisy. In practice, various filtering, denoising and smoothing techniques are employed in order to clarify the reservoir response and reduce data uncertainty. We evaluated several cases with different variance of measurement error. The comparison revealed that the pressure data noise has strong impact on the parameter estimation and the method convergence. In many cases, the noise caused the ensemble drifting away from the true solution.
Another important practical aspect is localization of model updates. During well test, pressure transients reflect pressure propagation away from the well. The propagation dynamic is governed by the formation properties within the disturbed reservoir domain. Therefore, the pressure measurements at a given time may be used for updating formation properties in the model only within the disturbed domain around the well. This would lead to the conclusion that a localization technique may be employed to relate model updates to relevant observations representing response from different reservoir areas. A time/distance dependent localization technique was tested to address this problem. The testing results showed that the proposed localization technique allowed for better estimation of permeability distribution (in terms of discrepancy with the true case). Validation by a blind test showed a better uncertainty propagation as the final ensemble retained significant diversity in areas remoted from test wells, in contrast to the non-localized case.
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Simulation Of Gaussian Random Fields Using The Fast Fourier Transform (Fft)
Authors P. Abrahamsen, V. Kvernelv and D. BarkerSummaryWe generate independent Gaussian random variables on a regular grid and use a spatial filter to smooth the independent random variables to obtain a spatially correlated Gaussian random field. The FFT is used to speed up the smoothing since convolution is a simple cell by-cell multiplication in the Fourier domain. A representation of the spatial convolution filter in the Fourier domain is efficiently obtained from the FFT of any stationary correlation function. Since FFT is cyclic, the grid must be padded to ensure that opposite sides are uncorrelated. The size of the padding is discussed in detail. Most standard covariance functions fail to be positive definite on finite cyclic domains. This causes striping artifacts in the final simulated realizations and failure to meet statistical properties such as variogram reproduction in the simulated realizations. These problems are addressed and solutions are provided to ensure near perfect statistical properties of the generated realizations. The method is fast and can generate a hundred million grid cell realization in approximately 1.5 minutes on a standard laptop PC. The method scales approximately linearly in the number of grid cells.
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Determining Nonsynchrony Between PDG Transient Temperature, Pressure, And Flow-Rate Data With Wavelet Transform
By F. WangSummaryTransient temperature, another source to provide reservoir information besides transient pressure, has been received much attention in recent years. Different from the immediate response of transient pressure due to flow rate change, there are obvious time lags between the transient temperature change and flow rate change. In this paper, the nonsynchrony between PDG transient temperature, pressure and flow-rate data was quantitatively investigated with wavelet transform (WT). Field PDG data analysis show that transient pressure changes nearly at the same time of flow rate change. However, due to the adiabatic expansion/compression effect, the time lags between transient temperature and flow rate are significant and cannot be neglected. Field PDG data demonstrates that averagely transient temperature changes about 0.225 hours later than the flow rate. Accurately identifying the time of flow events from PDG temperature data is difficult. The identified flow event time from PDG temperature is the time when Joule-Thomson effect dominates the reservoir temperature change. The flow events with time periods less than the time lags cannot be identified from PDG temperature data due to the adiabatic expansion/compression effect. Compared with the Haar wavelet, the the second derivative of the Gaussian wavelet can more accurately identify flow events from PDG temperature data. This study will be useful for improving the accuracy of transient identification from PDG data, and can benefit transient temperature analysis by clarifying the time lags due to adiabatic expansion/compression effect, and synchronize PDG transient temperature, pressure and flow-rate data for better data processing and analysis.
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Geomodeling Concept For The Reservoir With High Heterogeneity
Authors M. Nikolovski, M. Pesalj and M. TcukanovaSummaryThe “STC” oilfield was discovered in 1959., it is in production since 1960. and it is one of the most important oil field in Serbia. Up to 2017. on the field were drilled 87 wells.
Geological structure is represented by Sarmatian, Badenian and Jurassic formation. From the aspect of the current field development and quantity of the remaining balance reserves, the most important stratigraphic unit is Badenian which is made of terrigenous reservoirs of different composition and characteristics.
The main goal of this study is to identify the causes and control factors of the existence of different well production and water-cut reduction from Badenian. Badenian reservoir is characterized by high lithological heterogeneity and the rocks are composed from several lithofacies, such as limestone, sandstone and their variation. Each lithofacie has own petrophysical properties. Due to the frequent vertical and horizontal lithological heterogeneity, it was necessary to create a new geological model, including a detailed lithological characterization of badenian’s reservoirs and localize it.
In order to achieve this, it was necessary to include a detailed sedimentological and petrophysical interpretation of the badenia reservoirs, where all cores and well logs data were analyzed. The result of the interpretation is the separation of dominant lithofacies in the reservoir of Badenian. The next step was to define the diffusion of interesting litofacies, which required detailed analysis of seismic data and surface attributes analysis.
The result of all the above-mentioned modeling phases is a geological model that can help us in further development of the field, define the most prosperous zones for effective production wells allocation and necessary operations on existing wells. Also, oil reserves have been estimated for each of the facies, what will enable better field development and planning further production. The first positive results are seen in the overlapping of zones with a lower degree of water-cut in boreholes with increased content of sandstones, next to limestone, which leads to the conclusion that these zones have less water-cut of during the production. The research helped us to create search criteria for geological modeling of similar objects.
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The Ability of Multiple-Point Geostatistics for Modelling Complex Fracture Networks in Tight and Shale Reservoirs
Authors Hojjat Khani, Hamidreza Hamdi, Long Nghiem, Zhangxing Chen and Mario Costa SousaSummaryFracturing horizontal wells is an important technology that can make production from tight and shale formations economical. The fractured tight and shale formations are recognized by complex fracture networks around the primary hydraulic fractures. Microseismic mapping is a technique which can shed light on the activities happen around the main fractures which can direct us towards the extent of the fracture half-length and the secondary fracture networks in the stimulated reservoir volume (SRV). However, microseismic mapping does not necessarily indicate if the observed events can be directly related to the increased conductivities around the wellbore. There is rather a large uncertainty about the interpretation of the extent of effective (reopened) fracture network which can have a large impact on the performance of the flow simulations.
In this paper, a quantitative workflow is attempted to model the discrete fracture networks using multiple-point geostatistical algorithms to account for the uncertainty in the interpretation of the microseismic events. Uncertainty in microseismic data interpretation is also included in the algorithm (in terms of secondary probability maps) to account for the variability in the extent of the discrete fracture network within the stimulated reservoir volume (SRV). A sensitivity study is performed to understand the effect of different parameters on the well flow performance given different fracture network models. The results show that the connectivity of the fracture networks generated by the MPS method in this study is rather poor. Consequently, the permeability of the natural fractures has a dominant effect on the flow performance. In fact, the poor connectivity of fracture network does not allow to observe the effect of porosity of natural fracture and the permeability of hydraulic fracture on the flow performance. This research restresses that the MPS algorithm is not a push-a-button method to always generate reliable realizations. This work provides a guideline to better screen the generated geostatistical realization.
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Geologically Plausible History Matching With Structural Uncertainty
Authors G. De Paola, P. Koryuzlov, R. Rodriguez Torrado, A. Fernandez, E. Reding, M. Bartnik and M. SeignoleSummaryA geologically plausible history matching workflow has been applied to a complex reservoir to improve reservoir characterization. Different structural interpretations have also been included in the formulation which allow with a single workflow to match petrophysical properties, structural interpretations, fluid properties and fault transmissibilities avoiding any regional multipliers or inconsistent discontinuities. A multiobjective optimization was formulated to assimilate production and pressure timeseries as well as well test data. An inhouse implementation of a Particle Swarm Optimization allows to efficiently solve the optimization problem and provide multiple matching solutions for an improved uncertainty quantification. The multiobjective formulation allows the decision maker to screen the matching realizations based on the degree of confidence on the difference data type as well have better control selected the most representative realizations. The workflow proposed shows good match with the observed quantities and allows a review of the initial model of the field based on the improved understanding of the dynamic response of the reservoir. It is the first step before a field development plan optimization with structural uncertainty.
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Accelerating CMA-ES In History Matching Problems Using An Ensemble Of Surrogates With Generation-Based Management
Authors M. Sayyafzadeh, R. Koochak and M. BarleySummaryBecause of the quasi-gradient update embedded in CMA-ES algorithm, it can outperform most of the population-based algorithms, from a convergence speed standpoint. However, due to the computationally expensive fitness function associated with history matching, the reduction of function (simulation) calls can be favourable.
In this study, an ensemble of surrogates (proxies) with generation-based model-management is proposed to reduce the number of simulation calls efficaciously. Since the fitness function is highly nonlinear, an ensemble of surrogates (Gaussian process) is utilised. The likelihood term is divided into multiple functions, and each is represented via a separate surrogate. This improved the response surface fitting.
In generation-based management, a stochastically selected measure (surrogate or reservoir-simulation) should be used to evaluate all the individuals of each generation. CMA-ES requires ranking of the individuals to select the parents. Therefore, the generation-based model-management fits well in CMA-ES, as surrogates are normally better in ranking the individuals than approximating the fitness.
History matching for a real problem with 59 variables and PUNQ-S3 with eight variables was conducted via a standard CMA-ES and the proposed surrogate-assisted CMA-ES. The results showed that up to 65% and 50% less simulation calls for case#1 and case#2 were required.
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A Two-Level MCMC Based On The Distributed Gauss-Newton Method For Uncertainty Quantification
Authors J. Rafiee and A.C. ReynoldsSummaryWe present a methodology to obtain a correct sampling of the posterior probability density function (pdf) conditional to observations where this posterior pdf can be formally expressed using Bayes’ theorem. Generating a correct sampling of a multimodal posterior pdf is a challenging task which can only be achieved with Markov chain Monte Carlo (MCMC) methods. In standard MCMC such as random-walk MCMC, evaluation of acceptance probability for a proposed state requires a forward model run (a reservoir simulation run). When the forward model run is computationally expensive, we cannot afford to generate a long Markov chains with tens of thousands or more states. Therefore, it is critically important to design the MCMC such that it converges to the posterior pdf after generating a few thousand or less states.
Here, a two-level MCMC procedure which can sample multimodal posteriors relatively efficiently is developed and applied. In the first step, we use the distributed Gauss-Newton (DGN) method to generate many modes of the posterior pdf in parallel; this procedure estimates sensitivity matrices without the need of an adjoint solution. A Gaussian mixture model (GMM) is then constructed based on the distinct modes that we find in the first step. In the second step, the constructed GMM is used as the proposal distribution for our MCMC algorithm. Because the proposal distribution is constructed as a direct approximation of the target pdf (without the normalizing constant), the Markov chain(s) constructed should converge relatively quickly to the posterior distribution and applications of the two-level MCMC algorithm to test problems show that our proposed two-level MCMC is far more efficient than the random-walk MCMC.
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Revealing Hidden Reservoir Features During History Matching Using An Adjoint Method
Authors D.D. Awofodu, L. Ganzer and H. AlmuallimSummaryOur work focuses on developing a novel approach to improving reservoir characterization using the Adjoint method applied to history matching and optimization workflows. The developed approach, termed the “Adjointbased model screening method”, can be used to reveal hidden reservoir features not captured in reservoir models. The need for the development of our model screening method is necessitated by reservoir simulation models that miss important reservoir behaviours occurring beneath the surface. The impact of such modelling practice on history matching is the extreme tweaking of reservoir parameters to fit such models to available measured data. This paper demonstrates the strength of our approach in revealing the location of hidden faults and channels using synthetic homogeneous models.
Over the course of our research, an efficient model screening approach capable of revealing hidden reservoir behaviour has been developed and subjected to synthetic homogeneous blind tests. Our model screening approach utilizes reservoir permeabilities as input to screen our synthetic homogeneous models for hidden reservoir features like faults and channels. Observed data are generated from cases containing faults and/or channels and cases without these faults/channels are defined as the starting case. The Adjoint method is then used to reveal the location of these hidden reservoir features on a grid block basis.
In order to ascertain the superiority of our model screening approach, we compared the performance of our approach to other approaches reported in literature [Capacitance Resistance Model (CRM) and Interwell Numerical Simulation Model with Front Tracking (INSIM-FT)]. Results obtained demonstrate that our Adjoint-based model screening method is capable of handling varying and constant water injection rates as opposed to other approaches mentioned that can handle only varying injection rates. In addition, besides revealing the location of channels and faults, our approach infers the degree of transmissivity of faults. The developed approach was tested with 2-D and 3-D homogeneous models and results obtained proved that regardless of model dimensionality, hidden reservoir features can be revealed.
The most significant finding is that the accuracy of the adjoint-based model screening method in revealing the location of hidden reservoir features depends on the number of wells existing in the reservoir and their arrangement.
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Geostatistically-Consistent History Matching Of Lithofacies And Reservoir Properties Applied To Synthetic and Real-Field Cases
Authors E.S. Zakirov, I.M. Shiryaev, I.M. Indrupskiy, O.V. Lyubimova, E.Yu Arkhipova and D.P. AnikeevSummaryIn previous studies of the authors presented at the ECMOR-XIV and ECMOR-XV, an adjoint-based geostatistically-consistent approach was proposed for automated history matching of reservoir models. In the approach, reservoir property and facies distributions in a 3D models are consistently modified in the efficient iterative history matching procedure, with control parameters being anisotropic variogram parameters for the facies distribution and for reservoir properties distributions within each facie, as well as parameters of poroperm petrophysical correlations and values at pilot points. Derivatives of the objective function are obtained with adjoint method taking into account geostatistical relations between reservoir properties and variogram parameters. A concept of continuous "facies" and a weighting method for reservoir property calculations were also developed.
In this study we present the results of validation and further development of the geostatistically-consistent procedures for history matching.
In the first part of the study, we show and analyze the results of multivariant synthetic validation of the approach with simultaneous identification of anisotropic variogram parameters and reservoir properties at pilot points.
Then we describe the application of the continuous-facies approach to modeling of lithofacies and reservoir properties distributions for a real massive terrigenous gas reservoir in Western Siberia. We show that it proved successful in reflecting highly-heterogeneous distribution of reservoir properties with lens-like inclusions while preserving the overall geological consistency and continuous nature of sedimentation in the 3D model.
It is interesting to note that previous 3D dynamic flow model for field development planning was manually history matched through simulation of reservoir bodies' discontinuity. In other words, variogram ranges were artificially lowered for achieving desired level of reservoir disconnectivity. From production data analysis it is clear that gas-water contact advanced locally in vicinities of producing wells instead of an overall global contact elevation in accordance with global pressure distribution. Almost 80% of initial gas in place has been already produced, and pressure declined to almost 20% of its initial value. At this stage of development one could expect global displacement of gas by water, but reservoir heterogeneity played an important role forming hard-to-recover gas reserves at distant reservoir zones. All these peculiarities were successfully taken into account within the new 3D model built for a geostatistically-consistent history matching to production data. The results of this study would be presented in a conference paper and presentation.
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Petrophysical Parameters Inversion From Seismic Data Using An Ensemble-Based Method - A Case Study From A Compacting Reservoir
Authors T. Bhakta, E. Tolstukhin, C. Pacheco, X. Luo and G. NævdalSummaryWe implement an ensemble based petrophysical parameter inversion framework to estimate static as well as dynamic reservoir/ petrophysical parameters such as saturations, pressure and / or porosity fields using seismic data. Here, we consider acoustic impedance (Ip) data as the seismic data. The suggested approach is solved as a Bayesian inversion problem where the prior is provided as an ensemble of pressure-saturation and porosity fields. Here, the realizations of porosity and permeability fields of the prior model are generated using geostatistical methods and are further used in a reservoir simulator to obtain the realizations of pressures-saturations fields at the time of the seismic acquisition. The pressure-saturations and porosities are then changed to account for the information available from acoustic impedances using an iterative ensemble smoother. The outcome is a new ensemble of pressure-saturation and porosity fields that honor the seismic data.
The new approach differs from conventional deterministic petrophysical parameter inversion algorithms using seismic data by being stochastic, and more importantly, it pays more attention to the uncertainty quantification. Our results show that the suggested ensemble-based method is suitable to handle the nonlinear inverse problem and has the capacity of providing quantification of the uncertainty of the result.
We apply the proposed framework to a field-like 3D synthetic reservoir model, based on a compacting field scenario. The reservoir model consists of three fluid phases (water, oil and gas), and exhibits production related compaction. The numerical results from study indicates that the proposed framework can integrate the reservoir-engineering data as prior knowledge with the seismic data, achieving reasonable estimates of both the static and dynamic reservoir parameters.
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A Novel Multilevel Method For Assimilating Spatially Dense Data
Authors K. Fossum and T. MannsethSummaryInverted time-lapse seismic data are rich with respect to information about reservoir fluid flows. Ensemblebased data assimilation (EDA) of such spatially dense data into reservoir models requires a sufficiently large number of degrees of freedom (DOF). The DOF in straightforward EDA equals the ensemble size, E. Only a moderately sized E is, however, computationally feasible for large reservoir models. To increase the DOF, localization is routinely applied, but successful localization requires preconceived knowledge of the specific case and substantial manual effort. Alternative methods for increasing the DOF are therefore desirable. The large imbalance between data-space size (DSS) and DOF for problems with spatially dense data emphasizes this further.
We have considered generic methods for better balancing DSS and DOF. To decrease the DSS we used coarse data representation (CDR) of spatially dense data, that is, we map the data onto a regularly coarsened grid using averaging. To increase E (and, hence, the DOF) without increasing the computational cost of an ensemble forward run, we used simulations on a regularly coarsened grid with simple upscaling of reservoir properties (CGU). Results obtained with a combination of CDR and CGU, where the data and simulation grids were coarsened to the same level, were very good, but the optimal level varied from one case to another.
To avoid manual selection of an optimal level, we consider multilevel EDA using the novel Multilevel Hybrid EnKF (MlHEnKF) in combination with multilevel data representation (MDR) on a sequence of regularly coarsened grids. The resulting EDA method - MlHEnKF with MDR - can be applied in conjunction with localization, if desired. Assimilating inverted time-lapse seismic data in a reservoir-history-matching example, we assess the performance of the MlHEnKF with MDR by comparing the results to those obtained with a standard EDA approach.
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Evaluation of Regions of Influence for Dimensionality Reduction in Emulation of Production Data
Authors C.J. Ferrerra, G. Avansi, I. Vernon, D.J. Schiozer and M. GoldsteinSummaryOil and gas companies use reservoir simulation models for production forecasting and for business and technical decisions at the various stages of field management. The size and complexity of the reservoirs often requires reservoir models with a high resolution (number of grid blocks) to improve the reservoir behaviour prediction. As a consequence, simulation time becomes a limiting factor for routine workflows such as probabilistic history-matching, production optimization or uncertainty quantification, which requires a higher number of reservoir simulations. One possible solution to this problem is to use Bayesian statistic techniques known as emulation to substitute the simulator in parts of the workflow. An emulator is an approximate representation of a complex physical model; it is usually several orders of magnitude faster to evaluate than simulation, hence facilitating previously intractable calculations because of its speed. However, the challenge to incorporate spatial attributes, such as geostatistical realizations, as inputs remains. It is unfeasible to consider the reservoir spatial property value from each grid cell as a single input, so it is necessary to perform a dimensionality reduction to handle spatial properties as inputs in the emulation process. The use of region of influence is a way to deal with a high-dimensional model in an emulation setting and reduce the spatial properties space. Therefore, we evaluate different types of region of influence during the dimensionality reduction process to emulate production data of a complex numerical model. The regions of influence evaluated were defined using: streamlines, producer-injector pairs, Voronoi based on injection wells and Voronoi based on production wells. The dimensionality method considered were Principal Variables and Stepwise AIC. Our goal is to present and discuss alternatives to treat the high-dimensional input space, i.e., spatial reservoir properties instead of multipliers, to build effective emulators for production history data to use in oil industry workflows, which typically are time-consuming.
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Efficient Multi-Objective History-Matching Using Gaussian Processes
Authors H. Hamdi, I. Couckuyt, T. Dhaene and M. Costa SousaSummaryIn a multiobjective optimization approach, a trade-off is sought to balance between the optimality of different objectives. In this paper, we introduce a new efficient multiobjective optimization approach using sequential Gaussian Process (GP) modeling that can quickly find the Pareto solutions in a minimal number of model evaluations. This is the first time that we present this approach for history-matching. The difference with other optimization algorithms is elucidated for the cases where we can only afford to run a limited number of simulations. Unlike other surrogate-based methods, we do not aim for a greedy approach by minimizing the surface itself as there can be a large uncertainty in the surrogate approximations. Instead, statistical criteria are introduced to account for both proxy model uncertainty as well as its extrema.
This multiobjective optimization approach has been successfully applied for the first time to history match the production data (i.e. pressure, water and hydrocarbon rates) from a multi-fractured horizontal well in a tight formation. A GP surface is constructed for each misfit, to provide the predictions and the associated uncertainty for any unknown location. Multiobjective criteria, i.e., the hypervolume-based Probability of Improvement (PoI) and Expected Improvement (EI), are developed to account for the uncertainty of the misfit surfaces. The maximization of these statistical criteria ensures to balance between exploration and exploitation, even in higher dimensions. As such, a new point is selected whose values in different objectives are predicted to hopefully extend or dominate the solutions in the current Pareto set.
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Use Of Subsea Technologies For Produced Water Management In Mature Offshore Fields Using Integrated Asset Modeling
Authors O.J. Peña Piraneque, J.C. Hohendorff Filho and D.J. SchiozerSummaryThe results of the created methodology for evaluating the economic viability of installing subsea technologies for Oil-Water (O-W) Separation and Produced Water Re-Injection (PWRI) in mature offshore fields as a solution for water production management by using Integrated Asset Modeling (IAM) are presented. The methodology was tested in the benchmark case UNISIM-I-D showing excellent results, nevertheless, its application can be extensible to any other field where the installation of this kind of subsea systems is being analyzed.
Through the explicit coupling of specialized simulators of reservoir, multiphase flow in the tubing, production network, and economic modeling is possible both forecasting the production behavior of the field and generating the economic scenarios in a more realistic manner when the complex subsea systems are included to the production network. The equipment modeled consists of a subsea O-W separator located at the producer wellhead and a subsea pump that directly re-injects the separated water to the injector wellhead. Although the model has some simplifications, it permitted evaluating the implementation from a reservoir engineering perspective and knowing the production response without losing the representativeness of phenomena occurring in the field.
Besides being an economically attractive solution, it is also environmentally friendly because of the water used for injection is the produced water from the wells. Separating the water from the hydrocarbon stream has other additional benefits that favor the oil production from the reservoir and hence, positively influence the Oil Recovery Factor (ORF). For instance, the relief of water-and-liquid capacity of the platform, oil production anticipation associated with high amounts of produced water and decreasing the pressure drop along from the flowline to the platform. In fact, the results obtained from the economic model shows that this solution might be viable due to the revenues anticipation that from another way would not be possible to be earned without considering the implementation of these technologies.
This work can be considered as an interdisciplinary approach where including this kind of subsea technologies in the production network and its influence in the production of the reservoir have to be analyzed from a holistic point of view. Several disciplines as reservoir engineering, production engineering, and economic calculations are involved in building a coupled model that permits analyzing multiple production scenarios and network configurations, determining the best arrangement of the components, evaluating the economic viability of the project and supporting the making-decision process during field management.
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A Triangular Element-Based Finite Volume Formulation For Solving Problems Of Heat Transfer In Oil Reservoirs
Authors J.V. Contreras Sandia and C. AraujoSummaryCurrently, most of reservoir simulators have been developed using a finite volume method (FVM) as the numerical scheme to discretize the domain. However, FVM faces some issues to handle appropriately complex domains and boundaries. An element-based finite volume method (EbFVM) numerical scheme combines the FVM advantages and the ability of finite element methods to tackle complex reservoir domains.
The purpose of this work is to obtain a numerical formulation where EbFVM is applied to discretize the differential equation that describe diffusive situation for incompressible flow in a two-dimensional domain with problems of nonlinear characteristics in a transient regime.
The spatial discretization was performed by using a structured grid with triangular elements, which are very convenient to represent any two-dimensional complex domain with good accuracy. The conservation laws are locally applied in a secondary control volume grid, which was built around a node by connecting the centroid of each triangle with the midpoints of the triangle’s sides ( Minkowycs, 2006 ). The equation of the element was obtained from interpolation function depending on element coordinates and nodal values, as proposed in the work of Baliga and Patankar. Fluid and rock properties remain constant inside each element, but these properties may vary from element to element, and can be calculated according to the pressure and temperature values prevailing in each element. In the case of single-phase flow, the equation of state used was the fluid compressibility definition. The discretization of the time was developed with the implicit scheme, which is more numerically stable when solving problems with larger time steps, resulting in less computer time. The algorithm employed to discretize the conservation equation, was used to handle all conserved properties, in a sequential manner.
In this work, two examples were compared with solutions obtained from commercial simulation programs that employ the traditional FVM. One example involves a single phase flow and the other consists of heat injection by using a bottom hole heater. Numerical performance were studied with good accuracy in results.
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Symmetric Positive Definite Control-volume Distributed Multi-point Flux Approximation On Tetrahedral Grids
Authors R. Ahmed and M.G. EdwardsSummaryA three-dimensional symmetric positive definite (SPD) cell-centred control-volume distributed multi-point flux approximation (CVD-MPFA) is presented for porous media flow simulation on unstructured tetrahedral grids. The scheme depends on a single degree of freedom per control-volume and is derived in physical space, where the continuous fluxes are resolved directly along the face normals of the tetrahedra, maintaining exact grid geometry. Analysis and properties of the method will be presented.
Comparisons with the standard MPFA scheme shows that the new CVD-MPFA scheme yields well resolved pressure fields and improved convergence for homogeneous and heterogeneous as well as both isotropic and anisotropic full-tensor permeability fields.
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Unstructured CVD-MPFA Reduced-Dimensional Discrete Fracture Models For Two-Phase Flow
Authors Y. Xie and M.G. EdwardsSummaryControl-volume distributed multi-point flux approximation (CVD-MPFA) coupled with single-phase reduceddimensional discrete fracture models [1], are extended to two-phase flow, including gravity and capillary pressure.
Both continuous and discontinuous fracture models are considered coupled with higher resolution methods, leading to novel finite-volume schemes for flow in subsurface fractured porous media on unstructured grids. Performance comparisons are presented for tracer and two-phase flow problems on a number of 2D fractured media test cases including hybrid gravity and capillary pressure effects on unstructured meshes.
[1] R. Ahmed, M.G. Edwards, S. Lamine, B.A.H. Huisman and M. Pal
“Control Volume Distributed Multi-Point Flux Approximation coupled with a lower-dimensional fracture model” J. Comput. Phys vol 284 pp 462–489 March 2015
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