Vertical Aggregation of Reservoir Simulation Models for Numerical Well Testing

2008 ◽  
Author(s):  
R.A. Archer ◽  
J. Koh
Keyword(s):  
Reservoir Simulation ◽  
Simulation Models ◽  
Well Testing ◽  
Numerical Well Testing

The Athel: A Challenging Formation in South Oman

1998 ◽  
Vol 1 (04) ◽  
pp. 354-358
Author(s):  
P.M. O'Dell
Keyword(s):  
Reservoir Simulation ◽  
Simulation Models ◽  
Hydraulic Fractures ◽  
Well Testing ◽  
Well Test ◽  
Pressure Transient ◽  
Original Manuscript ◽  
Project Costs ◽  
Challenges And Opportunities ◽  
The Difference

This paper (SPE 50981) was revised for publication from paper SPE 37748, first presented at the 1997 SPE Middle East Oil Show held in Bahrain, 15-18 March. Original manuscript received for review 19 March 1997. Revised manuscript received 19 May 1998. Paper peer approved 26 May 1998. Summary The Athel silicilyte is a deep, tight formation containing light oil and dissolved sour gas. Because the potential volume is large, there is interest in early development. However, because individual wells are very expensive, every opportunity to gather information must be used. Well testing (production tests, pressure/volume/temperature (PVT) sampling, production logging runs, and pressure transient tests) has been used extensively to characterize the reservoir, to guide appraisal activities, and to shape the ultimate development. Key issues to be resolved before development are initial and sustained productivity and project costs. Production tests have demonstrated both challenges and opportunities in producing from this unique formation. Pressure transient tests have indicated that effective reservoir permeability is one-tenth of cleaned core plug permeability. The difference is likely caused by some combination of sealed fractures and the plugging effects of bitumen in the reservoir. Production logging has been used to measure the fraction of pay contributing to production. Reservoir simulation models, based on well test results, have been used to predict initial rates and ultimate recoveries for various well types (vertical, multiple drainhole, and multiple hydraulic fractures). Project costs (number of wells required) are based on these reservoir simulation results. P. 354

Numerical Well Testing: A Method To Use Transient Testing Results in Reservoir Simulation

2005 ◽  
Author(s):  
Medhat M. Kamal ◽  
Yan Pan ◽  
Jorge Luis Landa ◽  
Olubusola Olayemi Thomas
Keyword(s):  
Reservoir Simulation ◽  
Well Testing ◽  
Numerical Well Testing

The consistent approach for reservoir simulation models construction and continuous update of Central-Khoreyver uplift fields

2018 ◽  
pp. 44-47
Author(s):  
E.A. Sadreev ◽  
◽  
M.M. Khairullin ◽  
S.Kh. Kurelenkov ◽  
A.V. Chorniy ◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
Simulation Models ◽  
Consistent Approach

Automatic Well Completions and Reservoir Grid Data Quality Assurance for Reservoir Simulation Models

2015 ◽  
Author(s):  
Tariq Al-Zahrani ◽  
Muath Al-Mulla ◽  
Mohammed Al-Nuaim
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Reservoir Simulation ◽  
Simulation Models ◽  
Grid Data ◽  
Well Completions

Convolutional Neural Network Formulation to Compare 4-D Seismic and Reservoir Simulation Models

2021 ◽  
pp. 1-14
Author(s):  
Klaus Rollmann ◽  
Aurea Soriano-Vargas ◽  
Forlan Almeida ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Reservoir Simulation ◽  
Simulation Models

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.

scholarly journals Study of the Efficiency of Methods for Enhanced Condensate Recovery, Based on Reservoir Simulation Models

2017 ◽  
Vol 3 (1) ◽  
pp. 79-90
Author(s):  
Evgeny S. Makarov ◽  
Anton Y. Yushkov ◽  
Alexander S. Romanov
Keyword(s):  
Reservoir Simulation ◽  
Simulation Models
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