A Practical Approach to History-Matching Water-recycling in Waterflood Reservoir Simulation - Method and Case Studies in South Belridge Diatomite Waterflood

2015 ◽  
Author(s):  
Zhengming Yang ◽  
Alfredo H. Urdaneta
Keyword(s):  
Case Studies ◽  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Method ◽  
Practical Approach ◽  
Water Recycling

Full-Field Modeling Using Streamline-Based Simulation: Four Case Studies

2002 ◽  
Vol 5 (02) ◽  
pp. 126-134 ◽  
Author(s):  
R.O. Baker ◽  
F. Kuppe ◽  
S. Chugh ◽  
R. Bora ◽  
S. Stojanovic ◽  
...  
Keyword(s):  
Case Studies ◽  
History Matching ◽  
Field Data ◽  
Historical Data ◽  
Simulation Method ◽  
Real Field ◽  
Streamline Simulation ◽  
History Match ◽  
Full Field ◽  
Streamline Method

Summary Modern streamline-based reservoir simulators are able to account for actual field conditions such as 3D multiphase flow effects, reservoir heterogeneity, gravity, and changing well conditions. A streamline simulator was used to model four field cases, with approximately 400 wells and 150,000 gridblocks. History-match run times were approximately 1 CPU hour per run, with the final history matches completed in approximately 1 month per field. In all field cases, a high percentage of wells were history matched within the first two to three runs. Streamline simulation not only enables a rapid turnaround time for studies, but it also serves as a different tool in resolving each of the studied fields' unique characteristics. The primary reasons for faster history matching of permeability fields using 3D streamline technology as compared to conventional finite-difference (FD) techniques are as follows: Streamlines clearly identify which producer-injector pairs communicate strongly (flow visualization). Streamlines allow the use of a very large number of wells, thereby substantially reducing the uncertainty associated with outer-boundary conditions. Streamline flow paths indicate that idealized drainage patterns do not exist in real fields. It is therefore unrealistic to extract symmetric elements out of a full field. The speed and efficiency of the method allows the solution of fine-scale and/or full-field models with hundreds of wells. The streamline simulator honors the historical total fluid injection and production volumes exactly because there are no drawdown constraints for incompressible problems. The technology allows for easy identification of regions that require modifications to achieve a history match. Streamlines provide new flow information (i.e., well connectivity, drainage volumes, and well allocation factors) that cannot be derived from conventional simulation methods. Introduction In the past, streamline-based flow simulation was quite limited in its application to field data. Emanuel and Milliken1 showed how hybrid streamtube models were used to history match field data rapidly to arrive at both an updated geologic model and a current oil-saturation distribution for input to FD simulations. FD simulators were then used in forecast mode. Recent advances in streamline-based flow simulators have overcome many of the limitations of previous streamline and streamtube methods.2-6 Streamline-based simulators are now fully 3D and account for multiphase gravity and fluid mobility effects as well as compressibility effects. Another key improvement is that the simulator can now account for changing well conditions due to rate changes, infill drilling, producer-injector conversions, and well abandonments. With advances in streamline methods, the technique is rapidly becoming a common tool to assist in the modeling and forecasting of field cases. As this technology has matured, it is becoming available to a larger group of engineers and is no longer confined to research centers. Published case studies using streamline simulators are now appearing from a broad distribution of sources.7–12 Because of the increasing interest in this technology, our first intent in this paper is to outline a methodology for where and how streamline-based simulation fits in the reservoir engineering toolbox. Our second objective is to provide insight into why we think the method works so well in some cases. Finally, we will demonstrate the application of the technology to everyday field situations useful to mainstream exploitation or reservoir engineers, as opposed to specialized or research applications. The Streamline Simulation Method For a more detailed mathematical description of the streamline method, please refer to the Appendix and subsequent references. In brief, the streamline simulation method solves a 3D problem by decoupling it into a series of 1D problems, each one solved along a streamline. Unlike FD simulation, streamline simulation relies on transporting fluids along a dynamically changing streamline- based flow grid, as opposed to the underlying Cartesian grid. The result is that large timestep sizes can be taken without numerical instabilities, giving the streamline method a near-linear scaling in terms of CPU efficiency vs. model size.6 For very large models, streamline-based simulators can be one to two orders of magnitude faster than FD methods. The timestep size in streamline methods is not limited by a classic grid throughput (CFL) condition but by how far fluids can be transported along the current streamline grid before the streamlines need to be updated. Factors that influence this limit include nonlinear effects like mobility, gravity, and well rate changes.5 In real field displacements, historical well effects have a far greater impact on streamline-pattern changes than do mobility and gravity. Thus, the key is determining how much historical data can be upscaled without significantly impacting simulation results. For all cases considered here, 1-year timestep sizes were more than adequate to capture changes in historical data, gravity, and mobility effects. It is worth noting that upscaling historical data also would benefit run times for FD simulations. Where possible, both SL and FD methods would then require similar simulation times. However, only for very coarse grids and specific problems is it possible to take 1-year timestep sizes with FD methods. As the grid becomes finer, CFL limitations begin to dictate the timestep size, which is much smaller than is necessary to honor nonlinearities. This is why streamline methods exhibit larger speed-up factors over FD methods as the number of grid cells increases.

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.

Gravity Drainage System: Investigation and Field Development Using Geological Modelling and Reservoir Simulation

2021 ◽  
Author(s):  
Obinna Somadina Ezeaneche ◽  
Robinson Osita Madu ◽  
Ishioma Bridget Oshilike ◽  
Orrelo Jerry Athoja ◽  
Mike Obi Onyekonwu
Keyword(s):  
Reservoir Simulation ◽  
Structural Control ◽  
History Matching ◽  
Drainage System ◽  
Production Performance ◽  
Gravity Drainage ◽  
Base Case ◽  
Proper Understanding ◽  
Field Development ◽  
Reservoir Performance

Abstract Proper understanding of reservoir producing mechanism forms a backbone for optimal fluid recovery in any reservoir. Such an understanding is usually fostered by a detailed petrophysical evaluation, structural interpretation, geological description and modelling as well as production performance assessment prior to history matching and reservoir simulation. In this study, gravity drainage mechanism was identified as the primary force for production in reservoir X located in Niger Delta province and this required proper model calibration using variation of vertical anisotropic ratio based on identified facies as against a single value method which does not capture heterogeneity properly. Using structural maps generated from interpretation of seismic data, and other petrophysical parameters from available well logs and core data such as porosity, permeability and facies description based on environment of deposition, a geological model capturing the structural dips, facies distribution and well locations was built. Dynamic modeling was conducted on the base case model and also on the low and high case conceptual models to capture different structural dips of the reservoir. The result from history matching of the base case model reveals that variation of vertical anisotropic ratio (i.e. kv/kh) based on identified facies across the system is more effective in capturing heterogeneity than using a deterministic value that is more popular. In addition, gas segregated fastest in the high case model with the steepest dip compared to the base and low case models. An improved dynamic model saturation match was achieved in line with the geological description and the observed reservoir performance. Quick wins scenarios were identified and this led to an additional reserve yield of over 1MMSTB. Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism.

A Fast Gridding Method for Capturing Geological Complexity and Uncertainty

2021 ◽  
Author(s):  
Yifei Xu ◽  
Priyesh Srivastava ◽  
Xiao Ma ◽  
Karan Kaul ◽  
Hao Huang
Keyword(s):  
Efficient Method ◽  
Reservoir Simulation ◽  
History Matching ◽  
Mapping Method ◽  
Reservoir Properties ◽  
Structural Framework ◽  
Assisted History Matching ◽  
Grid Topology ◽  
Geological Complexity ◽  
Complex Channel

Abstract In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.

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