Case Studies Illustrating the Use of Reservoir Simulation Results in the Reserves Estimation Process

2007 ◽  
Author(s):  
Dean Channing Rietz ◽  
Adnan H. Usmani
Keyword(s):  
Case Studies ◽  
Reservoir Simulation ◽  
Estimation Process ◽  
Simulation Results

Case Studies Illustrating the Use of Reservoir Simulation Results in the Reserves Estimation Process

2009 ◽  
Vol 12 (01) ◽  
pp. 149-158 ◽  
Author(s):  
Dean C. Rietz ◽  
Adnan H. Usmani
Keyword(s):  
Case Studies ◽  
Reservoir Simulation ◽  
High Performance ◽  
Oil And Gas ◽  
Numerical Models ◽  
Simulation Software ◽  
Probabilistic Methods ◽  
Field Development ◽  
Estimation Process ◽  
Simulation Results

Summary Continuous improvements in reservoir simulation software and the availability of high performance computing equipment are making the use of simulation models commonplace for field development and planning purposes. Naturally, this trend has also increased interest in the use of reservoir simulation model results in the oil and gas reserves estimation process. As simulation specialists who work in a primarily reserves-evaluation company, the authors are routinely asked to evaluate, and in many cases incorporate, simulation results in the reserves estimation process. In addition, the authors are required to opine on the approach and tactics used by clients while they incorporate numerical models in their reserves bookings. Because limited published discussion exists on this topic, the purpose of this paper is to provide some examples of the approach used by the authors. We believe this approach to be appropriate and within the spirit of reserves interpretation as used by typical reserves regulatory bodies such as the U.S. Securities and Exchange Commission (SEC). Papers previously published have discussed the use of models in the reserves process, including the evaluation of the models themselves (Palke and Rietz 2001; Rietz and Usmani 2005). In contrast, this paper provides three case studies that illustrate how results from various models have been used to assist in quantifying reserves. Two of the examples are based on history-matched models, while the third focuses on a pre-production reservoir where no adequate history is available and probabilistic methods were incorporated to help understand the uncertainty in the forecasts. While there is no "cookbook" or step-by-step procedure for using simulation results to estimate reserves, the case studies presented in this paper are intended to both show some examples and also spark some debate and discussion. Undoubtedly there will be some disagreement with our techniques, but an open discussion should prove to be beneficial for both reserves evaluators and simulation specialists.

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

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
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