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Second EAGE Integrated Reservoir Modelling Conference
- Conference date: 16 Nov 2014 - 19 Nov 2014
- Location: Dubai, United Arab Emirates
- ISBN: 978-94-6282-100-2
- Published: 16 November 2014
1 - 20 of 43 results
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Keynote Presentation: Efficient and Neutral Uncertainty Quantification for Discrete-facies Reservoir Models
Authors K. Mosegaard, Y. Melnikova and K.S. CorduaAlmost all methods used in geophysical data inversion and history matching are based on the leastsquares method. This method is essentially an aplication of Gaussian statistics, and when it is used in classical voxel-based models to defeat underdetermination (through a quadratic penalty function) it adds unphysical information to our inverse problem. The result is geologically unrealistic solutions.
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Sources of Uncertainty in Modeling Depositional Environments
By M. KhadhrawiDepositional environments can be modeled using a number of methods: geological or geophysical. The latter rely heavily on seismic data that represent indirect measurements of reservoir parameters. This practice introduces inherent uncertainty in the produced models. Uncertainty values for these models are taken directly from those of the input parameters.
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Reduce Facies Uncertainties Distrbution at Reservoir Scale: Insight from High-Resolution 4D Stratigraphical model
Authors C. Pellan, N. Rodriguez Morillas, M. Zen, T. Ait-Ettajer and L. FontanelliReducing uncertainties at appraisal stage of a reservoir is a key challenge to tackle, due to the limited number of exploration wells. The prediction of the facies architecture inside complex reservoirs is another challenging task, that cannot be assessed using classical geostatistics methods. The facies are used as the main guideline to populate the properties of static model, reducing error in facies assignation and propagation in the mesh is mandatory. In such a composite context, a tool able to constrain physically and geologically the sediments spatial distribution is a serious advantage. In this framework, 4D Forward Stratigraphic Modeling can be a useful tool to assess facies distribution. For the last 15 years this tool has been applied, commonly to exploration studies, and is now applied to reservoir scale studies. 4D Forward stratigraphic modelling is a powerful tool to populate with an inner logic a three dimension geological model. The rules of physics, sedimentology and stratigraphy are honored and allowed the building of predictive environment of deposition and lithologies. At fine scale, Forward Stratigraphic Models could be used as a new method suitable for representing geologically reservoir heterogeneities in the static reservoir modelling, during the very challenging well-poor appraisal phase
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Importance of Conceptual Geological Models in 3D Reservoir Modelling
Authors J.A. Cavero Loayza, N. Orellana, I. Yemez and V. SinghThis paper briefly describes a methodology which covers different components of Conceptual Geologic model building and its importance in terms of improved understanding of reservoir depositional environments (from basin to reservoir scale), geometry, connectivity and diagenetic history. The importance of integrating the 3D conceptual geologic model with the 3D reservoir models (static and dynamic) has been demonstrated through an example of silici-clastic reservoirs. This example has limited geoscientifc and engineering data with high subsurface uncertainties. The study shows that different possible geologic scenarios incorporated in digital representation of 3D reservoir models lead to significantly different Hydrocarbon-In-Place, recoverable resources and production forecasts. Several benefits derived from this integration such as better 3D reservoir characterization, quantifying the impact of key reservoir uncertainties on fluid-flow characteristics and associated risks, providing essential information to the management in terms of more reliable production forecasts for informed decision makings are highlighted.
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Assisted History Matching with Application of Adjoint Method Sensitivity Computation: Case Study North German Basin Oilfield
Authors K. N. Awemo, I. Ajala, C.A. Schwietzer, L. Ganzer, R. Schulze-Riegert and H. AlmuallimHistory matching, which is an inverse problem, is traditionally performed by a trial and error approach to minimize the mismatch between observed and simulated data. Modification of parameters on sequential simulation usually leads to rock properties which are way far from geological interpretation. This renders the predictive power of the simulation model doubtful. In the presented approach, the adjoint method is used to capture the derivatives of the mismatch (sensitivities) with respect to each parameter at the grid level. Adjoint methods derive the analytical sensitivities based on prior knowledge of fluid flow equations implemented in a dynamic simulator. During the modification step, the sensitivity and rock property updates are iteratively calculated and implemented grid cell by grid cell until convergence is reached. The workflow is applied to history matching of an abandoned North German oilfield model with long production life. The outcome suggested that, with the use of this technique, improvements can be achieved beyond the scope of manual approach using a small number of simulation runs. Both the history match quality and the predictive capability of the dynamic simulation model are improved.
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Applying the Multi-level Monte Carlo Method to Quantify Uncertainty for Chemical EOR Processes
More LessUncertainty in reservoir heterogeneity can be detrimental to the success of Chemical EOR processes. An enhanced uncertainty quantification method is introduced, the Multi-level Monte Carlo (MLMC) method. This method is used for quantifying spatial uncertainty for chemical EOR processes. The permeability field is assumed to be the random input and the recovery factor is the qunatity of interest. This method is based on running a small number of expensive simulations on the finer scale model and a large number of less expensive simulations on coarser scale models and then combining the results to produces the quantities of interest. This reduces computational cost. This approach was implemented by a MATLAB code and was applied to stylized reservoir models using Gaussian generated two and three dimensional permeability realizations. Different coarsening algorithms were used such as the renormalization and pressure solver methods. Polymer and Surfactant-Polymer flooding processes were simulated. The results are compared with running Monte Carlo for the fine scale model while equating the computational cost for the MLMC method. The results show that it is possible to robustly quantify spatial uncertainty for chemical EOR processes while greatly reducing the computational requirement, up to two orders of magnitude compared to traditional Monte Carlo.
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Petrophysical Properties Modeling Using Integrated DFN Algorithm with Influence Radius of Permeability and Porosity
Authors H. Sarkheil and A. TalebiModeling of petrophysical properties in hydrocarbon reservoirs are used for a variety of algorithms. One of these new mathematical algorithms is DFN algorithms (Discrete Fracture Network). This study focused on part of the hydrocarbon formations of Khangiran field. 52 wells are drilled in this area of which 6 wells for reservoir studies in the northwestern part of the research. In order to analyze and model the hydrocarbon reservoir porosity and permeability in this range, there are different ways including geostatistical methods, intelligent algorithms, algorithms for discrete fracture network geometry, fractals, and neural network algorithm. Analysis methods and modeling techniques from fractal geometry of these models are appropriate. This technique can be used to calculate fractal dimension based on the box counting method to estimate and calculate porosity and permeability. This method has been used in the modeling area, the proper description of petrophysical properties of reservoir-prone areas to enter the production phase description as a new ellipsoid based prediction models as an influence radius of permeability and porosity. The results obtained by this model with geological features, the results of mud loss data and production results show an appropriate compatibility with lower uncertainty.
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Effective Case Selection Based on Static and Dynamic Reservoir Uncertainty
More LessUncertainty evaluation is often done separately and/or subsequently for the static and dynamic reservoir model. Methods combining both domains in one workflow are not common practice in the industry. The reasons are manifold. Nevertheless such workflows are highly requested. One possible approach is attempted. The presented workflow helps to identify more realistically the volumetric cases which fulfill the requirements of the static and dynamic uncertainty evaluation and provides an effective selection of a limited number of cases to be simulated. This is organized by a streamline simulation of the individual static model realizations. By comparing and analyzing the relationship of both volumes in a cross-plot and clustering them the number of cases can be reduced significantly.
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A Workflow to Quantify Uncertainty in a Gas Field Development to Optimize the Number of Producing Wells With Hydraulic Fracturing
Authors H. Monfared and A. DanialiA complete workflow including stochastic geological modelling, history matching and risked production forecasting has been designed. The impact of hydraulic fracturing through the variability of well productivity has been studied.
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Reservoir Integrated Modelling (RIM) Process: A Key to Optimum Field Development
Authors I. Yemez, V. Singh and E. IzaguirreTo ensure the correct and optimized workflow for the generation of reservoir model, a high-level fit-for-purpose RIM process along with sub-workflows for each discipline have been developed that can be applied at different stages of field development depending upon the business need, resource limitation and input data availability. Application of these workflows has been demonstrated through an example to assess the project risk by capturing the uncertainties in production and economic forecasts. Finally, this study emphasizes that the integrated reservoir modelling process will continue to evolve as new techniques and technologies are developed and implemented. This in turn, will enhance our ability to capture the physical realities of the real world and reduce the risk associated with the field developments.
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Assessment of Water Saturation and Permeability and Quantification of their Uncertainties Using Sedimentological Rules and Capillary Forces in Carbonate Reservoirs
Authors T. Ait-Ettajer and L. FontanelliThe reserves estimation and the development of the carbonate reservoirs are challenging tasks due to the co-existence of multiple pore systems and the uncertainty in their distribution. The characteristics of those pore systems govern the storage, during the charging phase of the reservoir, and the natural flow of the hydrocarbon. The paper proposes to quantify the uncertainty related to the pore system distribution and to assess its impact on the modeling of the water saturation (Sw) and the permeability (K).
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Interaction Between Uncertainty Data and Operational Decisions in the Risk Management in Mature Gas Field
Authors Y.I. Iturbe and G. FavaThe Laslau Mare field is located in the Transylvania Basin, Romania, is a multi-reservoir, brown gas field, vertically split in 6 production packages with different fluid contacts and pressure levels; shale intercalations divide the reservoir layers from each package in several separated sandstone or shaly-sandstone bodies; to add complexity the two upper packages have high water production risk and the lower packages have low permeability as tight reservoirs. The field requires combining the latest technology from our side with the operational experience of ROMGAZ to refine the understanding of the reservoir and its productivity. The actual phase includes redevelopment and optimization of the field, with newer technologies and well interventions to insure profitable results. The Uncertainty management and Decision-making have contributed to the successful performance of the project and open another business opportunities. The fact that decisions facing eventualities and weak data were correct has been demonstrated with increased production, almost three times of its value on September 2003.
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Uncertainty Management: Understanding Why May Lead to How to Do it
Authors L.S. Sandjivy and A.S. ShtukaThe poster illustrates an operational concept for uncertainty management in E&P, to maximize the value of oil/gas reservoirs (i.e. the number of producible barrels per dollar spent at each and every step of the exploration/production cycle). Managing uncertainty in E&P means the ability to quantify and propagate uncertainty throughout successive geoscience workflows, always using a consistent and objective geostatistics model. Two recent case studies illustrate uncertainty management in E&P. The first case study shows how to successfully implement a new exploration well. The second shows how to optimize safety in storing high intensity nuclear waste in a shale environment.
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Uncertainty Evaluation of a Deepwater Reservoir
Authors Y. Liu, L. Qi, B. Keyser, J. de la Colian and R. ScammanIn this study, we quantitatively evaluated uncertainty of a few major static factors: structure, facies proportions, porosity and permeability ranges and variograms, water saturation, and formation factor. To evaluate uncertainty, typically modelers first select a few uncertainty factors to build high, mid and low cases for each. Then the high-high-high… case for all factors is deemed as the P10 case, the mid-mid-mid… case deemed as the P50 case, and the low-low-low… case as the P90 case. However, the problem with this workflow is that the all-high, all-mid, and all-low cases may not coincide with the P10, P50 and P90 cases when all the uncertainty factors are integrated. In this paper, we present a better alternative of constructing the P10, P50, P90 models, which we believe can characterize the uncertainty space more precisely. The reservoir is a deep-water reservoir at the appraisal stage. Seven uncertainty factors are considered and Plackett-Burmman experimental design is chosen to construct the initial 25 static models. These 25 models are used for static evaluation. Then three models (P10, P50, P90) are constructed for dynamic simulation purpose.
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A Workflow to Quantify and Model Structural Uncertainty: Application to a Deepwater Giant Reservoir, Offshore Mozambique
Authors A. Luciani and A. AvellaThe uncertainties related to structural seismic interpretation and velocity modeling can have a significant impact on gas in place evaluation. The risks and high investments associated with appraising and developing a deep-water reservoir make reliable quantification of these uncertainties a fundamental step of the risk analysis and ultimately the decision making. These considerations are a key driver for the development plan of the deepwater Mamba Complex, a super giant gas field discovery in offshore northern Mozambique. The magnitude of the discovery and the rapid development planning introduce a series of technical challenges, including the evaluation and quantification of the structural uncertainty, and its integration in the reservoir model. This paper proposes a workflow that effectively integrates probabilistic and scenario-based approaches, aiming to mitigate the risk associated with the development of the Mamba complex, and to help the decision maker.
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Evaluating Property Uncertainty in Mature Fields – Demonstration of a Feasible Multiple-Geomodel Approach
Authors S. Kuhlmann, B. Wendt and H.J. YuA mature field with over 30 years of production history constrains uncertainty of the in-place and recoverable volumes. The amount of available static data also provides insight into the intra-dependencies between variables. Honoring dependencies of variables in a stochastic uncertainty study adds complexity but results in realistic models leading into production forecasting and improved economic evaluations. The workflows used and developed for evaluating the property uncertainty of a Norwegian chalk field will be presented. The focus of the uncertainty study was on maintaining a consistent reservoir description throughout the full modeling cycle; starting from petrophysics and ending with a suite of simulation models that provide a high quality history match without ignoring any of the fundamental underlying principles.
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The Only Wrong Way to Use Seismic in Static Modeling is Not to Use it at All
Authors W. Gaber, K. Frederick, S. Ownby, E. Diaz, J. Johnston and R. VillafuerteStatic modeling of a hydrocarbon field accumulation is a critical stage in any field development plan. The static model, which gives an estimate of the hydrocarbons in place, can significantly impact decisions around possible ways the field can be developed. The estimated volumes and the dynamic results may vary dramatically if input data is violated or if a stochastic approach is used to construct the rock property models.
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Topology vs. Topography: Sometimes Less is More
By B. ThomsonThe software tools we use for interpretation and modelling are powerful and complex. The outputs they generate are always extremely precise (to the nth decimal place!) but may not always be as reliable as we wish them to be. To support business decisions we required integrated work products that are reliable rather than being merely precise. So how do we move from precision to reliability?
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A Philosophical Perspective on Scenarios, Realizations and their Definitions – What Should Managers do With That?
Authors E. Tawile, P. Schirmer and J.P. RolandoThe current approach on how scenarios and realizations are dealt with will be first shown. Then we will show that they have limitations and that these definitions are relevant from the geosciences standpoint but become irrelevant from a petroleum project perspective. This pushes towards linking the two and globalizing the uncertainty approach by taking into account the downstream (in the petroleum project sense) impact of a given uncertainty. In addition, there is an ambivalent requirement from management towards their technical teams on how uncertainties should be managed. Once the problems have been posed, some thoughts will be proposed towards changing mindsets on how uncertainties should be dealt with and what managers should do with them.
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