Use of Reservoir Simulation, Parallel Computing and Optimization Techniques to Accelerate History Matching and Reservoir Management Decisions

1999 ◽  
Author(s):  
D.J. Schiozer
Keyword(s):  
Parallel Computing ◽  
Reservoir Simulation ◽  
History Matching ◽  
Reservoir Management ◽  
Optimization Techniques ◽  
Management Decisions

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.

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.

A Graph Network Based Approach for Reservoir Modeling

2021 ◽  
Author(s):  
Wenyue Sun ◽  
Sathish Sankaran
Keyword(s):  
History Matching ◽  
Network Modeling ◽  
Time Of Flight ◽  
Reservoir Management ◽  
Ease Of Use ◽  
Analytical Models ◽  
Productivity Index ◽  
Model Complexity ◽  
Modeling Approach ◽  
Uncertainty Estimates

Abstract Reservoir management routinely requires assimilating historical data and predicting field performance against multiple production strategies before implementing them in the field. However, traditional numerical methods are often cumbersome to characterize, build and calibrate at a timescale that can be used reliably for such short-term decision cycles such as production forecasting, IOR optimization and production rate control. Simpler analytical models make assumptions and lack the rigor needed to adequately model these systems. Pure data-driven methods may lack physical insights or have limited range of applicability. Model fidelity, speed, interpretability, suitability to automate and ease-of-use are some key modeling traits that are desired for reservoir management purposes. In this work, we propose to use a reservoir graph-network modeling approach (RGNet), based on the concept of diffusive time of flight, to forecast well performance using routinely measured field measurements (e.g. bottomhole pressure and rates). We propose a novel, model order reduction method based on discretized time of flight for multiple wells with interference. It simplifies the 3D reservoir flow problem into a flow network representation that can be solved as a 2D simulation model with any general-purpose reservoir simulator. Parameters in RGNet model cover well productivity index, grid pore volume and transmissibility, which are estimated through a history-matching process. After history matching, multiple posterior RGNet models are generated to quantify subsurface uncertainties. The RGNet modeling approach allows various fluid-flow physics to be modeled within the grids and boundary conditions, and is applicable to a range of conventional and unconventional reservoirs with different flow mechanisms. We applied the proposed approach on a field case reservoir models for multiple wells with interference. By virtue of the reduced complexity, the modeling methodology is highly scalable and still retains physical interpretability. The calibration method produces parsimonious models and provides uncertainty estimates in history matching parameters with range of outcomes. In addition, the RGNet models are much faster to simulate, over 1000x speed up, compared with full-physics models. We then used RGNet models for well-control and flood optimization and achieved significant improvement over field net-present-values. Parameterization of the proposed reservoir graph-network modeling approach provides a unique and sustainable way to reduce model complexity needed for reservoir management purposes. The method is rooted in physical principles and provides an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.

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