History Matching Finite Difference Models With 3d Streamlines

Emanuel, A.S.; Milliken, William

doi:10.2523/49000-ms

Cited by 13 publications

(15 citation statements)

References 5 publications

(6 reference statements)

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“…Streamlines can be used to identify injector-producer pairs and associated regions that should be changed to improve the history match (20,21) as shown in Figure 4. This is very useful in the history matching process, where one of the key aspects is determining what parameters and where those changes should be made (i.e., in what regions).…”

Section: How Is Streamline Technology Different From Finite Differencmentioning

confidence: 99%

“…Assisted history matching (AHM), on the other hand, shows some promise to be a practical tool for conditioning permeability fields (20,21) . Manual engineering input is always required in AHM field studies because it is not always clear at the start of a history match which parameters are important and how to adjust those parameters.…”

Section: Assisted History Matchingmentioning

confidence: 99%

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Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Baker

2001

Journal of Canadian Petroleum Technology

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24Journal of Canadian Petroleum Technology FIGURE 1: Difference between finite difference model vs. streamline model for transporting fluids [adapted from Grinestaff, SPE 54616 (20) .] FIGURE 2: Example of typical reservoir flows. (Streamline Time = 01/01/1991.) FIGURE 3: Example of typical reservoir flows. (Streamlines Times = 12/31/1999.) April 2001, Volume 40, No. 4 25 FIGURE 4: Identification of history match regions. FIGURE 5: Relationship between (watercut or water rate) vs. time and spatial position of streamlines (map view).

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Section: How Is Streamline Technology Different From Finite Differencmentioning

confidence: 99%

Section: Assisted History Matchingmentioning

confidence: 99%

Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Baker

2001

Journal of Canadian Petroleum Technology

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“…The use of streamlines in finite difference models has been used to improve cycle time. This process has also been referred to as assisted history matching (Emanuel and Milliken, et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Integrated Production Model Calibration Applied to a Gulf of Mexico Sub-sea Field

2010

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Reservoir, Production and Operations Engineers utilize integrated production models (IPM) to understand and optimize performance in reservoirs, wells and pipeline networks. This often requires an initial calibration followed by subsequent updates.IPM calibration may offer several challenges since it involves non-unique solutions for reservoirs, wells and pipeline network parameters. In addition, suspect results can occur from manual data entry, uncertainties in subsurface parameters, subjective interpretation and poor quality data. These challenges are exacerbated when the user needs to repeat the procedure periodically (e.g. every month) or when new alternative scenarios require subsequent evaluation.In this work, a consistent and systematic procedure was developed to assist in the calibration of an IPM under the presence of uncertainties. The procedure was successfully applied to a subsea Gulf of Mexico (GOM) field which consists of several reservoirs, wells, sub-sea pipeline network and surface equipment.The IPM calibration is based on a workflow engine that adjusts IPM parameters to minimize an error function. The workflow engine is used to couple the IPM to a spreadsheet and an optimizer. The spreadsheet computes an objective function (weighted error function of measured vs. simulated results for each IPM variable) and the optimizer follows a gradient-free taboo and scatter search algorithm. Finally, IPM parameters (e.g. reservoir tank pressures, well productivity indexes (PI), pipeline internal diameter, roughness, equivalent choke diameter and choke correction) are selected from the objective function global minimum.The calibration procedure was also tested by different users leading to similar results. The workflow engine permitted the evaluation of thousands of IPM scenarios in a few days, which would be impossible to do manually. The calibrated IPM predicted field production rates within 4.2% of the actual performance. The procedure developed in this work can be extended to deal with any combination of well, reservoir and pipeline models.

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“…The updated masses and saturations at the end of the time step are mapped back to the original grid. Nowadays streamline simulation is used in a range of applications that includes history matching (Emanuel and Milliken 1998;Maschio and Schiozer 2004;Cheng et al 2007;Stenerud et al 2008;Gross et al 2004;Fenwick et al 2005), quantification of uncertainties (Christie et al 2002;Ligero et al 2003), flood surveillance (Batycky et al 2008), CO 2 storage (Qi et al 2007), compositional simulation (Thiele et al 1997;Osako and Datta-Gupta 2007), and dual porosity modeling of fractured reservoirs (Di Donato et al 2004;Kozlova et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Streamline Simulation with Compressibility and Spatial and Dynamic Variation in Oil Composition

Beraldo

2008

All Days

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There are several oil fields offshore Brazil with horizontal oil density variations. API tracking, that is available in some commercial finite difference simulators, can deal with such cases by allowing definition of an initial oil gravity distribution and tracking variations of oil density, due to the movement of oil.Streamline-based simulators can be much faster than conventional finite difference simulators when applied to large and heterogeneous models. However, this approach is most accurate and efficient when it is assumed that the rock and fluids are incompressible. In previous work , an incompressible formulation for streamline simulation with an API Tracking option using two components in the oleic phase was presented.This paper presents a compressible formulation for streamlines that also considers API tracking. It extends the work of Cheng et al. (2006) and Osako and Datta-Gupta (2007) by consistently accounting for flux of mass and volume along streamlines. It was described how mass and volume can be mapped between the underlying grid and streamlines to minimize mass balance errors and how consideration of cumulative volume in a streamline can substitute for time-of-flight (Beraldo 2008).The method was implemented in a three-dimensional two-phase streamline-based simulator. Tests based on a Brazilian oilfield model and on the SPE10th Comparative Case demonstrate that the implementation can reproduce the results of a conventional simulator, while being substantially faster for finely-resolved models, even when compressibility is significant.

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History Matching Finite Difference Models With 3d Streamlines

Cited by 13 publications

References 5 publications

Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Integrated Production Model Calibration Applied to a Gulf of Mexico Sub-sea Field

Streamline Simulation with Compressibility and Spatial and Dynamic Variation in Oil Composition

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