Use of New Horizontal Grids in Reservoir Simulation Models Improves the Chance of Success in Developing Marginal Thin Oil Rim Reservoirs using Horizontal Wells

1998 ◽  
Author(s):  
Hsu Hsiu-Hsyong
Keyword(s):  
Reservoir Simulation ◽  
Simulation Models ◽  
Horizontal Wells ◽  
Oil Rim

An Improved Methodology for Gridding Fractured Reservoirs for Simulation

2021 ◽  
Author(s):  
Sheldon Gorell ◽  
Jim Browning ◽  
Justin Andrews
Keyword(s):  
Reservoir Simulation ◽  
Unstructured Grids ◽  
Fractured Reservoirs ◽  
Simulation Models ◽  
Horizontal Wells ◽  
Design Parameters ◽  
Structured Grids ◽  
Fractured Horizontal Wells ◽  
The Impact ◽  
Base Grid

Abstract A significant amount of research for gridding of complex reservoirs, including models with fractures, has focused on use of unstructured grids. While models with unstructured grids can be extremely flexible, they can also be expensive, both in configuring, computationally, and visual display. Even with this focus on unstructured grids, most reservoir simulation models are still built on structured grids. Current methods for creating reservoir simulation models with structured grids often involve defining a base grid upfront and then "somehow" inserting one or more Features of Interest (FOI's) into the model. Applied to fractured horizontal wells with many stages it can be extremely difficult to accurately align wells and completions within a pre-existing simulation grid. This work describes and demonstrates a methodology to resolve such issues. This approach changes the order of model design and creation steps. This paper describes the process where FOI's are identified, a base grid is designed around the FOI's, then local grid refinements (LGR's) are defined as desired. Applied to a horizontal well with fractures, the well and completion locations are defined before the detailed grid definition is created. This process is illustrated for generalized FOI's, and then applied to fractured horizontal wells. Formulas for creation of models for wells with evenly space homogeneous completions are presented. Numerical testing and analyses are presented that show the impact of the gridding parameters and various design parameters on performance of reservoir simulations.

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.

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