A Study of the Influence of Number of Components on Compositional Reservoir Simulation Results

2001 ◽  
Author(s):  
M.A. Hildebrand ◽  
K. Liu ◽  
A.G. Mezzatesta
Keyword(s):  
Reservoir Simulation ◽  
Number Of Components ◽  
Simulation Results

Functionals of random mappings: exact and asymptotic results

1991 ◽  
Vol 23 (3) ◽  
pp. 437-455 ◽  
Author(s):  
P. J. Donnelly ◽  
W. J. Ewens ◽  
S. Padmadisastra
Keyword(s):  
Frequency Spectrum ◽  
Exact Results ◽  
Asymptotic Results ◽  
Number Of Components ◽  
Random Mapping ◽  
Random Mappings ◽  
Simulation Results ◽  
Central Tool

A random mapping partitions the set {1, 2, ···, m} into components, where i and j are in the same component if some functional iterate of i equals some functional iterate of j. We consider various functionals of these partitions and of samples from it, including the number of components of ‘small' size and of size O(m) as m → ∞the size of the largest component, the number of components, and various symmetric functionals of the normalized component sizes. In many cases exact results, while available, are uniformative, and we consider various approximations. Numerical and simulation results are also presented. A central tool for many calculations is the ‘frequency spectrum', both exact and asymptotic.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Integration of Seismic Monitoring and Reservoir Simulation Results for a Steamflood at South Casper Creek Oil Field, Wyoming

1992 ◽  
Author(s):  
P.F. Johnston ◽  
G.R. Andersen ◽  
Noboru Wachi ◽  
D.S. Lee ◽  
F.G. Martens ◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
Seismic Monitoring ◽  
Oil Field ◽  
Simulation Results

SYSTEMATIC SYNTHESIS OF OTA-BASED T-T FILTERS USING NAME METHOD

2013 ◽  
Vol 22 (03) ◽  
pp. 1350002 ◽  
Author(s):  
YONG-AN LI
Keyword(s):  
Class Ii ◽  
Class I ◽  
Number Of Components ◽  
Simulation Results ◽  
Systematic Synthesis

According to the terminal relations of the DO-OTA, eight nullor–mirror models for the DO-OTA are presented in this paper, Moreover, the NAME method for two different classes of T-T filters is considered. The class I filters, employing three SISO-OTAs, have 16 different forms, and the class II filters, employing one SISO-OTA and one DISO-OTA or one SIDO-OTA, have eight different forms. Due to use of canonic number of components, the circuits are easy to be integrated and their parameters can be tuned electronically through tuning bias currents of the OTAs. The MULTISIM simulation results have been included to verify the workability of the derived circuit.

Use of dimensionless scaling groups to interpret reservoir simulation results

2018 ◽  
Vol 163 ◽  
pp. 270-282 ◽  
Author(s):  
Yacine Debbabi ◽  
David Stern ◽  
Gary J. Hampson ◽  
Matthew D. Jackson
Keyword(s):  
Reservoir Simulation ◽  
Simulation Results

scholarly journals Economic Evaluation of Y Gas Field Development Using Reservoir Simulator

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Anita Theresa Panjaitan ◽  
Rachmat Sudibjo ◽  
Sri Fenny
Keyword(s):  
Development Strategy ◽  
Reservoir Simulation ◽  
Gas Field ◽  
Field Development ◽  
Development Scenarios ◽  
Black Oil ◽  
Simulation Results ◽  
Reservoir Simulation Model ◽  
Attractive Case ◽  
Reservoir Simulator

<p>Y Field which located around 28 km south east of Jakarta was discovered in 1989. Three wells have been drilled and suspended. The initial gas ini place (IGIP) of the field is 40.53 BSCF. The field will be developed in 2011. In this study, reservoir simulation model was made to predict the optimum development strategy of the field. This model consisted of 1,575,064 grid cells which were built in a black oil simulator. Two field development scenarios were defined with and without compressor. Simulation results show that the Recovery Factor at thel end of the contract is 61.40% and 62.14% respectively for Scenarios I and II without compressor. When compressor is applied then Recovey Factor of Scenarios I and II is 68.78% and 74.58%, correspondingly. Based on the economic parameters, Scenario II with compressor is the most <br />attractive case, where IRR, POT, and NPV of the scenario are 41%, 2.9 years, and 14,808 MUS$.</p>

RESERVOIR SIMULATION FOR RESERVOIR MANAGEMENT

1986 ◽  
Vol 26 (1) ◽  
pp. 397
Author(s):  
A.B. Kaliszewski
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
History Matching ◽  
Reservoir Management ◽  
Primary Objective ◽  
Geological Mapping ◽  
Management Tool ◽  
The Subject ◽  
Simulation Results ◽  
Very High

The Hutton reservoir in the Merrimelia Field (Cooper-Eromanga Basin) was the subject of a 3-D reservoir simulation study. The primary objective of the study was to develop a reservoir management tool for evaluating the performance of the field under various depletion options.The study confirmed that the ultimate oil recovery from this strong water drive reservoir was not adversely affected by increasing total fluid offtake rate. However, any decisions regarding changes to the depletion scheme such as increasing production rates, if based solely on computer simulation results, should be viewed with caution. Careful monitoring of any changes to the depletion philosophy and checking of actual data against simulation predictions are essential to ensure that oil production rate and ultimate recovery are optimised.The model assisted in evaluating the economics of development drilling. While the simulation results are dependent on the validity of geological mapping, the model was useful in confirming that, due to very high transmissibility in the Hutton reservoir, additional wells would only accelerate production rather than increase ultimate recovery. The issue of drilling wells thus became one of balancing the benefits of accelerating production against the geological risk associated with that well.Interaction between the reservoir engineer and various disciplines, particularly development geology, is critical in the development and application of a good working simulation model. This was found to be especially important during the history matching phase in the study. If engineers and development geologists can learn more of the others' discipline and appreciate the role that each has to play in simulation studies, the validity of such models can only be improved.The paper addresses a number of the pitfalls commonly encountered in application of reservoir simulation results.

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