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First EAGE Integrated Reservoir Modelling Conference - Are we doing it right?
- Conference date: 18 Nov 2012 - 21 Nov 2012
- Location: Dubai, United Arab Emirates
- ISBN: 978-94-6282-069-2
- Published: 25 November 2012
21 - 40 of 47 results
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Seismic to Simulation Modeling of a Green Middle Eastern Naturally Fractured Reservoir
Authors M.M. Faskhoodi, D. Astratti, L. Souche and P. MenegattiThis Middle East case study addresses the modelling of a Type 2 (Nelson, 2001) green carbonate reservoir which production is acknowledged to be largely controlled by fractures. In the case of an undeveloped reservoir the main challenge is posed by the limited amount of hard data and production history. The ultimate objective was to minimize production forecast uncertainties. This was achieved by means of a workflow that integrates 3D seismic, well and production data.
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Hydraulic Fracture Stimulation via Coupled Reservoir Geomechanics – The Nikanassin Unconventional Formation in Canada
Authors Gaisoni Nasreldin, L. Gonzalez, J. Rivero, P. Welsh, R. Aguilera and N. KoutsabeloulisThis paper presents an integrated 3D workflow for multi-stage hydraulic fracture stimulation by embracing 3D geomechanics, with a view to help unlocking these reserves. In the proposed approach the geometry and orientation of the multiple hydraulic fractures are driven by the prevailing 3D stress state in the drainage zone in the vicinity of the stimulated well. A case study involving two-way geomechanical coupling approaches is used to carry out a Class C1 “prediction” of the performance of a horizontal well drilled in the Nikanassin naturally fractured tight gas formation (Western Canada Sedimentary Basin). The results of the computations demonstrate the benefits of including 3D geomechanics in dual porosity flow simulations, particularly in connection with closely matching the gas production history. Moreover, the ability of the method to estimate the unavoidable reduction in permeability of natural and hydraulic fractures associated with pressure depletion leads to more realistic production predictions when compared with cases when ignoring geomechanical effects. The telling conclusion is that the field of hydraulic fracture stimulation provides an object lesson in the need for coupled 3D geomechanical approaches. The method presented in this paper will help improve gas rates and recoveries from low-permeability reservoirs.
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Assimilation of Dynamic Data: Are We Doing it Right?
By D.S. OliverIn this talk, I will review the history matching problem for petroleum reservoirs, emphasizing progress and limitations, beginning with a discussion of the purpose of history matching. Because the data are typically quite limited, I will emphasize the ability to quantify uncertainty in reservoir predictions and the importance of the choice of model parameterization on the ability to match data and to assess uncertainty. Particular problems associated with updating of complex reservoir models will be identified. Although the focus of the talk will not be on methodology, I will discuss the consequences of various choices of methodology on parameterization and the limitations of various methodologies. Finally, the Norne Full-Field case will be used to illustrate many of the history matching concepts discussed earlier. This field is highly faulted, contains vertical flow barriers of unknown continuity, has multiple initial oil-water contacts and gas-oil contacts. Water and gas have both been injected at various times. Production data, including RFT and phase production and injection rates have been measured in a relatively large number of horizontal and vertical wells and 3D seismic surveys have been repeated periodically. Identifying model parameters with sufficient flexibility to match all of the data is a challenge.
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History Matching: Towards Geologically Reasonable Models
Authors Y. Melnikova, K.S. Cordua and K. MosegaardThis work focuses on the development of a new method for history matching problem that through a deterministic search finds a geologically feasible solution. Complex geology is taken into account evaluating multiple point statistics from earth model prototypes - training images. Further a function that measures similarity between statistics of a training image and statistics of any smooth model is introduced and its analytical gradient is computed. This allows us to apply any gradient-based method to history matching problem and guide a solution until it satisfies both production data and complexity of a prior model with desired accuracy. As a consequence of the approach, we sufficiently decrease the amount of forward simulations needed to resolve historical data and prior information.
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Novel Metric Space Methods for Data Integration
Authors D. Fenwick and J. CaersIn mathematics, a metric space is a set where a distance (called a metric) is defined between elements of the set. Metric space methods have been employed for decades in various applications, for example in internet search engines, image classification, or protein classification. In petroleum reservoir modelling however, metric space methods are not widely known. In this paper, we introduce the concept of a metric space in the framework of reservoir modelling. The metric space is formed by calculating a distance between two models. The distance is analogous to the well-known concept of an objective function in optimization, with the exception that the distance is not only evaluated between the model response and historical data, but also between two reservoir model responses. We describe how placing an ensemble of reservoir models in metric space allows for novel methods for visualization and analysis of model ensembles, reservoir uncertainty, and data integration.
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Dynamic Validation of a Multi-Point Statistics Model using Extended Well Test Data
Authors P. Bergey, P.J. Ruelland and H. HamdiIn Field A, an extended well test carried out over 5 months was used to validate 5 facies models generated with Multi-Point Statistics, each declined into a set of petrophysical models. The geological context of Field A is that of low NTG fluvial deposits which include channel and bar sandstones, alluvial splay siltstones and flood plain shales. Sparse static conditioning data, limited spatial information and naturally complex 3-D channel networks imply that various models can be equi-probable. Multipoint statistics (MPS) approaches were used to model sedimentary bodies in order to ensure the consistency between the model and the geological understanding. Simulation of the conditioned MPS models enabled to reproduce a set of extended well test responses and hence compare the dynamic behaviour of the models against that of the reservoir. Successive comparisons enabled to extract 2 MPS models which matched respectively the early and late time well test data. To obtain a better match over the whole well test data, an engineering-based hybridisation algorithm was used to combine the 2 better-fit models. The successful implementation of this algorithm resulted in promising results and enabled to obtain a quality match with the real well test data.
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Parameterization in History Matching: State of the Art and Perspectives
Authors M. Le Ravalec-Dupin and F. RoggeroHistory matching consists in estimating spatially variable petrophysical parameters among which facies, porosity or permeability. These parameters, which are keys to understanding fluid flows, are difficult to measure. However, they can be inferred from measurements of related variables such as flow rates, pressures, gas oil ratios... This is actually an inverse problem, which is intrinsically "ill-posed". This concern motivated the development of many techniques to solve history matching. They differ most significantly in their approaches to parameterization. Parameterization deserves special consideration because it strongly influences the "well-posedness" of the inverse problem and the physical validity of its solution. A suitable parameterization technique makes it possible to reduce the number of parameters, but also to preserve the spatial variability of the inverted properties. Otherwise, the inverted models lack geological realism. We propose to review the main parameterization techniques and distinguish two main families: those, which adopt either a blocked or a geostatistical description of spatial variability. Briefly, the first approach splits the reservoir into a number of discrete blocks characterized by uniform petrophysical properties. These ones are then considered as parameters to adjust in order to match the available production data. The geostatistical alternative views the properties of interest as stationary random fields. In this case, various methods have been proposed to vary realizations of random fields to fit the production data while still respecting the prior spatial variability captured either by two- or multi-point statistics. Referring to the multiscale nature of rocks, we suggest to merge the two main parameterization groups mentioned above, thus resulting in a multiscale parameterization approach. The technique envisioned is based upon sequential simulation and provides the ability to vary both continuous and discrete petrophysical properties at the block scale or at a finer scale while preserving a consistent link between the scales.
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Risk and Uncertainty in EP - Where Are We?
By T. NguyenUncertainty analysis and modelling in EP evaluation is one of the main focus for the oil industry during the last decade and many progress have been made on the subject, although there are still more improvements to come as the problem is far from being trivial. This key note talk will share with the audience the state of the art we are today with different uncertainty analysis and modelling techniques used by the industry in order to identify and reduce potential risks involved. Uncertainty analysis is the first and crucial step, aiming at identifying correctly the key uncertainties, their related nature (bias or variance errors) and their impact on the results under consideration. Weak signals from measurements should be scrutinized and analogues should be investigated in case of sparse data or information. Uncertainty modelling is the final step using either well known deterministic approach or more complex probabilistic ones. The process is closely linked with the geological and reservoir modelling tasks and must comply with the objective which has been set up beforehand. Their applications and limits are frequently subject of debate, as well as the long time it took to complete the whole process, sometimes with hundreds of models to be generated and investigated. At the end, the communication of the uncertainty results to different parties is not always an easy task, since the probabilistic language is seldom popular among operational staff and especially when the operational needs differ greatly from one group to another. Big challenges still lay ahead for a more comprehensive approach for uncertainty analysis and modelling with a trend towards larger models of the subsurface and more realistic representation of reservoir heterogeneities. However, mastering the uncertainties and risks is the sole way for a better exploration and development.
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Uncertainty Quantification: Are We Doing it Right?
By M BeardsellThere is no more easy oil! This means when we find hydrocarbons today it is a risky business …. small reservoirs, tight reservoirs, deep reservoirs etc. It is therefore more important than ever to quantify the risk associated with each prospect. Practically this means evaluating the uncertainty in our reservoir models. For more than a decade now software vendors have been providing tools to aid in the evaluation of uncertainty. With time these tools have become easier to use and cover a broader range of the modeling process. Despite this, uncertainty quantification has had limited adoption across the industry and tends to lie in the hands of a few experts. There is a shortage of best practice procedures and we can be sure that many mistakes are being made. This presentation will start by laying out the principal workflows and open up for discussion important questions such as: How do we decide which of the multitude of parameters we should focus on? How do we combine scenarios with realizations? What strategies are available to link the static uncertainty with the dynamic? How can Uncertainty play a role in history matching and prediction? What is the role of optimization?
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Realities and Expectations of Reservoir Modelling
More LessReservoir modelling both static and dynamic has advanced drastically with the advancement in computing and visualization technologies. However, the accuracy and the effectiveness of reservoir modelling rely on a number of factors that goes into the modelling processes and the data that is used to construct those models. The idea that a model will be solid representation of the reservoir in question has to be qualified carefully and the notion that one can build a perfect model has to be questioned. In my presentation I will touch on some of the factors that impact model results and show actual examples.
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Mapping The Distribution of Gas Hydrates Using Coloured Inversion and Rock Physics Modeling in Blake Ridges, USA
More LessGas hydrate is important to learn because it related to the stability of the seafloor that can lead to a blowout at an offshore drilling, but other than that can also be used for natural gas resources. The data used in this study is public domain seismic data in the form of PSTM 3D data from the Blake Ridge offshore, South Carolina,USA. Several studies in the Blake Ridge offshore concluded that this area is showing signs of the existence of gas hydrates. In this process, rock physics modeling and fluid substitution analysis is required to provide a picture of the behavior of the elastic parameters of the existence of gas hydrates and gas hydrate relationship with the matrix and other fluids in the rocks. Fluid substitution modeling results, the existence of gas hydrates lead to higher acoustic impedance that is expected to perform post-stack inversion estimates of hydrate distribution can be made. Mapping the distribution of gas hydrates can be done using coloured inversion techniques on 3D seismic data. Coloured Inversion can provide a picture of the relative impedance using the local velocity trend,gas hydrates represented by the most positive relative acoustic impedance.
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Improving Reservoir Characterization Using a Qualitative Analysis From Seismic Data. Orinoco Heavy Oil Belt, Venezuela
Authors L. Velasquez and J. CarreñoThe results of a structural and stratigraphic interpretation using seismic data of the area between Morichal and Cerro Negro Fields in The Orinoco Heavy Oil Belt in Venezuela are discussed. The seismic reflections at the top of the target interval (The Terciary Morichal Member of the Oficina Formation), have good continuity and reflection character, however, the challenge is to understand the reflection character at the center and in the bottom of the massive sands that comprise this reservoir, where the acoustic impedance contrast becomes poorer in a paleo-environments that suggest a high-energy of sedimentation. The methodology involves definition of regional markers integrating cores well logs and seismic data. To achieve the objective of the study, it has been fundamental the extraction of stratal slices from volumes derived from multi-attribute analysis and facies classification within target interval. These representative stratal slices and facies results for each unit have been correlated with sedimentological analysis, net sand maps and available production tests, constraining stochastic simulations (SIS) of properties for calculations of OOIP volumes for the area. Finally, feasibility study for seismic inversion performed in this study demonstrated that elastic parameters are the best choice to perform a quantitative seismic characterization for the area.
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Charactrization of Spatial Lithofacies Continuity of Arab-D Reservoir Using Modeled Semivariograms: Outcrop Approch
Authors H. Eltom, M. Makkawi and O. AbdulatifThere are strong links between semivariograms parameters and the geological variables of the Arab-D reservoir outcrop analogue, Central Saudi Arabia. Facies analysis of the outcrop revealed seven lithofacies including: platy mudstone, dolomitic mudstone, dolomitic wackestone, stromatoporoid wackestone and packstone, peloidal sandy grainstone, laminated fossiliferous sandy grainstone, and breccia. Indicator semivariograms for these lithofacies were constructed in different directions using lithofacies logs from the outcrop to determine the direction with the best spatial continuity. North-South direction has the best continuity for most of the lithofacies. Laminated platy mudstone, stromatoporoid wackestone and packstone, dolomitic mudstone and dolomitic wackestone show good semivariogram shape, while semivariogram for peloidal sandy grainstone, laminated fossiliferous sandy grainstone and breccia only loosely fits the data in both the major and minor directions. The differences in semivariograms parameters were attributed to variation of the geological variables which include vertical layering of the outcrop, lateral facies changes, and topographical control of the outcrop. The study of outcrop semivariograms allows for visualization of lateral and vertical lithofacies variation in a higher resolution than that of the oilfield scale. This study indicates that outcrop base semivariograms have a significant implication on subsurface reservoir characterization.
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DelineatingThickness Distribution Based on Snesim Simulation
Authors C. Pardosi, D. Hapsari, S. Winardhi and A. SyahputraThis Paper presents an approach in estimating sand distribution in a fluvial environment by using gradient of spectral decomposition as the soft constraint data. Training image used in the simulation is fluvial model with certain pattern and probability of sand and shale. As soft constraint, spectral decomposition is used to identify the thickness of sand within the area, and also to determine shale area. Then validators are used to analyze the reliability of MPS.
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Permeability and SW Modeling In Carbonate Reservoirs Using Cloud Transform
Authors M. Essam and A. Ghani GueddoudThe Cloud transform reproduces the conditional distributions of a dependent 3D parameter given an independent 3D parameter (Bashore et al. 1994). This distribution is estimated empirically from well data and the secondary variable. The estimated distribution will then be the basis for the cloud transform. This works in the same way as the Normal score transform except that the CDF being used is a 2D CDF estimated empirically from well data and the independent 3D parameter. This allows specifying a model that reproduces the scatter plot from the wells in a 3D volume. In a case study from offshore Abu Dhabi, the relationship between porosity and permeability is found to be non-linear and Cloud transform technique was applied for permeability distribution. The porosity, which is populated using Gaussian Simulation used as independent 3D parameter and an empirical relationship was derived between porosity (3D parameter) and log derived permeability (calibrated to core at well location for each rock type, which was used to estimate permeability distribution for the Cloud transform. The initial simulation results show positive results where good history match was reached without applying multiplier for the producing wells.
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Carbonate Rock Types Matrix, the Initial Rock Properties Classification Catalogue
Authors O. Al-Farisi, H. Belhaj, S. Ghedan, S. Negahban and J. GomesIn carbonate, the rock typing work that’s been performed during the last two decades had a little progress in term of providing reliable estimation of reservoir behavior. However, lately in 2009, the development of Conjunction Rock Properties Convergence, CROPC, a carbonate rock typing concept that provided an important and easy solution to the carbonate rock typing gaps has a major breakthrough. the need to identify more complex carbonate pore network had led to the initiation of developing the Carbonate Rock Type Matrix CaRTM, which will be detailed in this paper, as part of a Master of Science research project. In this novel concept the carbonate rocks were classified into homogeneous, single pore network, and heterogeneous rocks, dual and triple pore network with the utilization of the effective petrophysical properties of permeability, capillary pressure, saturation, porosity and height above free water level , all were classified in a conjunction matrix that honors these properties and at the same time enables generating sub groups as down scaling and estimation for unseen groups with infinite rock complexity capturing, at the same time it enables the ease to lump the groups and generates upscaled groups that make it easier for utilization.
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