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Search Deutsch, Clayton V.: Pages with reference errors
...s of both models are shown in Figures 16 and 17, respectively. The true results computed from the reference fine-scale model are plotted as the thick, light curves. It is evident that the reservoir models n...loser to the true results with significantly less uncertainty. The low permeability barrier in the reference find grid model between the injection well and W1 is not well captured in the inverse coarse grid... models when the injected water is at pore volume injected (PV J) of 1.0. The true values from the reference field are shown in the same figure by bullets. The accuracy and uncertainty of forecasting are la...
... at well i 1,..., ny and time t ty, ..., th. [Wl]: is the inverse covariance matrix of observation errors at time f. If pressure measurement ...errors at different wells are independent, {W)]. is a diagonal matrix with the form of Wit [Wh ot Wn,,...ient of the objective function on the null space of the gradients of the binding constraints (see reference 9 for details). (B-5)...
...aints and the static data as well. A review of available inverse techniques has been presented in reference '8. In this paper, the Sequential Self-Calibration (SSC) inverse technique is adapted to invert pe...
Abstract This paper presents a methodology to generate maps of high resolution permeability from multiple well single-phase flow rate and pressure data. The dynamic, i.e. temporal, production data contains important information about the interwell permeability distribution that should be integrated with static data, such as well and seismic data, to generate reservoir models to provide reliable input to reservoir simulation and reservoir management. A two-step procedure is proposed for such data integration:establish the spatial constraints on large-scale permeability trends due to the production data using an inverse technique, and construct the detailed geostatistical reservoir models subject to those spatial constraints using geostatistical techniques. The single-phase pressure and production data could be provided by permanent pressure gauges, simultaneous multiple well tests, or flow rates under primary depletion. Production data and reservoir petrophysical properties, specifically permeability. are nonlinearly related through flow equations. Establishing the spatial constraints on permeability due to production data calls for the solution of a difficult inverse problem. This paper adapts the Sequential Self-Calibration (SSC) inverse technique to single-phase multiple- well transient pressure and rate data. The SSC method is an iterative geostatistically-based inverse method coupled with an optimization procedure that generates a series of coarse grid 2-D permeability realizations, whose numerical flow simulations correctly reproduce the production data. Inverse results using two synthetic data sets show this SSC implementation to be flexible, computationally efficient, and robust. Fine-scale models generated by down-scaling the SSC generated coarse-scale models (using simulated annealing) are shown to preserve the match to the production data at the coarse-scale. Finally, reservoir performance prediction results show how the integration of production data can dramatically improve the accuracy of production forecasting with significantly less uncertainty. Introduction Optimal reservoir management requires reliable performance forecasts with as little uncertainty as possible. Incomplete data and inability to model the physics of fluid flow at a suitably small scale lead to uncertainty. Uncertainties in the detailed description of reservoir lithofacies porosity, and permeability are large contributors to uncertainty in reservoir performance forecasting. Reducing this uncertainty can only be achieved by integrating additional data in reservoir modeling. A large variety of geostatistical techniques have been developed that construct reservoir models conditioned to diverse types of static data including hard well data and soft seismic data. Commonly, a number of techniques are applied sequentially to model the large reservoir geometry, the lithofacies, and then petrophysical properties such as porosity and permeability. However, conventional geostatistical techniques including Gaussian, indicator, annealing-based, or object-based methods are not suited to directly integrate dynamic production data. Production data and reservoir petrophysical properties are related to each other through flow equations which are highly nonlinear. As a consequence, accounting for dynamic engineering data in geostatistical reservoir modeling is a difficult inverse problem. Nevertheless, historical production data are often the most important information because they provide a direct measure of the actual reservoir response to the recovery process that form the basis for reservoir management decisions. Integrating dynamic production data is an important outstanding problem in reservoir characterization. Ideally, we want to directly match all types of production data in the reservoir model at the required resolution simultaneously with other types of geological and geophysical data. A number of inverse techniques have been developed for this purpose. P. 115^
- Europe (1.00)
- North America > United States > Texas (0.46)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Spatial Reasoning (0.75)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Constraint-Based Reasoning (0.75)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.34)
...SPE 84276 3 Application quicker the convergence of the simulated values to the A reference porosity field with 50x50 grid cells is shown in ...reference values. For the same absolute values, the connected Figure 2(a). There are 6 wells in it and 5 of t...rpretation are uncertain, therefore, because the porosity and seismic data (from the GSLIB data the reference values were assigned an error variance of 5%. sets) are actually positively correlated. The standar...
... In most cases, the simulated surfaces and inner/outer drainage radii. porosity does not match the reference value from the well test There may be multiple annular regions around the same and the simulated va...
...ortant issues that warrant further research. The application University Press, New York (2002) 376 pages. 2. Goovaerts, P.: Geostatistics for natural resources evaluation, of smoothing techniques on the ...updated seismic map should Oxford University Press, New York (1997) 483 pages. be explored. The allowable deviation of seismic data is an 3. Bortoli, L. J., Alabert, F., Haas,...d edition, Oxford University u i sampled location of porosity data Press, New York (1998) 369 pages. ( i 1,2, , n ) 9. Wen, X. H., Deutsch, C. V. and Cullick, A.S.: "High-resolution V i reservo...
Abstract Optimal reservoir management requires reliable reservoir performance forecasts with as little uncertainty as possible. There is a need for improved techniques for dynamic data intergration to construct realistic reservoir models by using geostatistical techniques. This paper gives a method to create porosity models that honor interpreted pore volumes from well test data. Well porosity data, seismic data and well test results are integrated in sequential simulation. Seismic data is modified iteratively until the co-simulated porosity matches the interpreted well test pore volume. A number of examples are shown. Introduction There are many data that can be used to constrain reservoir models including core data, well logs, seismic and production data. There are few wells during reservoir exploration. Seismic data is areally extensive. The large-scale information provided by seismic data is accounted for in the structural framework and facies model. Seismic may also provide additional information on large-scale porosity variations within the facies. Production data are extraordinarily important because they are direct observations of reservoir performance. Any reliable reservoir characterization study should account for these dynamic data . Well test data is one kind of production data that can provide average porosity and permeability in some volume near the well. In fact, average porosity is an input to well test analysis, but it must be adjusted during well test interpretation in order to make the actual pressure curves and theoretical type curves match better. Because effective pore volume in the area around a well is a basic concept used in well test model and can be calculated by multiplying average effective porosity by formation thickness and relevant area, the basic idea of this paper is to account for the pore volume from well test data by slight modifications to seismic data when co-simulating porosity. This makes the model more predictive since it matches interpreted flow data and decreases uncertainty in the porosity model. Methodology Hard data include the facies assignments, porosity, and permeability observations taken from core and well logs that provide reliable measurements at the scale we are modeling. All other data including seismic data and production history are called soft data and must be calibrated to the hard data. Seismic data are frequently used as secondary data for co-simulation of porosity based on the relationship between porosity and seismic . The seismic data are often impedance values from seismic inversion or some other attribute if an inversion has not been undertaken. The sole calibration parameter is the correlation coefficient between the Gaussian transform of porosity and the Gaussian transform of seismic. Seismic data constrains the spatial distribution of porosity. Well test data can be seen as additional soft data that the porosity model must reproduce . The two soft data (seismic data and well test) must be considered simultaneously. Two significant complexities make this difficult. First, the volume scale difference between the hard data, the modeling scale, the seismic scale, and the well test make it very difficult to quantify the relationship between the data types. Second, the cross correlation or redundancy between the different soft data must be modeled at the same time as their correlation to the hard data. Finally, porosity does not average linearly after Gaussian transformation. For these reasons, a full cokriging approach is not practical. It is conceptually straightforward and practically efficient to slightly modify or update the seismic data to carry the information of the well test data. Using the updated seismic data as secondary data for Gaussian simulation will decrease the uncertainty of the results.
- Europe > Norway > North Sea > Central North Sea > Central Graben > PL 018 > Block 2/4 > Greater Ekofisk Field > Ekofisk Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > PL 018 > Block 2/4 > Greater Ekofisk Field > Ekofisk Field > Ekofisk Formation (0.99)