A New Approach to History Matching Using Reservoir Characterization and Reservoir Simulation Integrated Studies

2015 ◽  
Author(s):  
G.D. Avansi ◽  
D.J. Schiozer
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Reservoir Characterization ◽  
New Approach ◽  
Integrated Studies

A Gauss-Newton Trust-Region Solver for Large-Scale History-Matching Problems

SPE Journal ◽  
2017 ◽  
Vol 22 (06) ◽  
pp. 1999-2011 ◽  
Author(s):  
Guohua Gao ◽  
Hao Jiang ◽  
Paul van Hagen ◽  
Jeroen C. Vink ◽  
Terence Wells
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Large Scale ◽  
Computational Cost ◽  
Trust Region ◽  
Uncertain Parameters ◽  
Memory Usage ◽  
New Approach ◽  
Matching Problems ◽  
Cpu Time

Summary Solving the Gauss-Newton trust-region subproblem (TRS) with traditional solvers involves solving a symmetric linear system with dimensions the same as the number of uncertain parameters, and it is extremely computational expensive for history-matching problems with a large number of uncertain parameters. A new trust-region (TR) solver is developed to save both memory usage and computational cost, and its performance is compared with the well-known direct TR solver using factorization and iterative TR solver using the conjugate-gradient approach. With application of the matrix inverse lemma, the original TRS is transformed to a new problem that involves solving a linear system with the number of observed data. For history-matching problems in which the number of uncertain parameters is much larger than the number of observed data, both memory usage and central-processing-unit (CPU) time can be significantly reduced compared with solving the original problem directly. An auto-adaptive power-law transformation technique is developed to transform the original strong nonlinear function to a new function that behaves more like a linear function. Finally, the Newton-Raphson method with some modifications is applied to solve the TRS. The proposed approach is applied to find best-match solutions in Bayesian-style assisted-history-matching (AHM) problems. It is first validated on a set of synthetic test problems with different numbers of uncertain parameters and different numbers of observed data. In terms of efficiency, the new approach is shown to significantly reduce both the computational cost and memory usage compared with the direct TR solver of the GALAHAD optimization library (see http://www.galahad.rl.ac.uk/doc.html). In terms of robustness, the new approach is able to reduce the risk of failure to find the correct solution significantly, compared with the iterative TR solver of the GALAHAD optimization library. Our numerical results indicate that the new solver can solve large-scale TRSs with reasonably small amounts of CPU time (in seconds) and memory (in MB). Compared with the CPU time and memory used for completing one reservoir simulation run for the same problem (in hours and in GB), the cost for finding the best-match parameter values using our new TR solver is negligible. The proposed approach has been implemented in our in-house reservoir simulation and history-matching system, and has been validated on a real-reservoir-simulation model. This illustrates the main result of this paper: the development of a robust Gauss-Newton TR approach, which is applicable for large-scale history-matching problems with negligible extra cost in CPU and memory.

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.

Integrated reservoir characterization: Beyond tomography

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 354-364 ◽  
Author(s):  
Larry Lines ◽  
Henry Tan ◽  
Sven Treitel ◽  
John Beck ◽  
Richard Chambers ◽  
...  
Keyword(s):  
History Matching ◽  
Preferential Flow ◽  
Reservoir Characterization ◽  
Seismic Anisotropy ◽  
Flow Direction ◽  
Geophysical Methods ◽  
Reservoir Properties ◽  
Traveltime Tomography ◽  
Sonic Log ◽  
Primary Water

In 1992, there was a collaborative effort in reservoir geophysics involving Amoco, Conoco, Schlumberger, and Stanford University in an attempt to delineate variations in reservoir properties of the Grayburg unit in a West Texas [Formula: see text] pilot at North Cowden Field. Our objective was to go beyond traveltime tomography in characterizing reservoir heterogeneity and flow anisotropy. This effort involved a comprehensive set of measurements to do traveltime tomography, to image reflectors, to analyze channel waves for reservoir continuity, to study shear‐wave splitting for borehole stress‐pattern estimation, and to do seismic anisotropy analysis. All these studies were combined with 3-D surface seismic data and with sonic log interpretation. The results are to be validated in the future with cores and engineering data by history matching of primary, water, and [Formula: see text] injection performance. The implementation of these procedures should provide critical information on reservoir heterogeneities and preferential flow direction. Geophysical methods generally indicated a continuous reservoir zone between wells.

scholarly journals Skenario Pengembangan Untuk Meningkatkan Recovery Factor Pada Lapangan TR Lapisan X Dengan Menggunakan Simulasi Reservoir

PETRO ◽  
2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Muhamad Taufan Azhari
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Control Volume ◽  
Base Case ◽  
Case Scenario ◽  
Injection Wells ◽  
Base Case Scenario ◽  
Development Scenarios ◽  
Production Wells ◽  
Grid Construction

<p>Reservoir simulation is an area of reservoir engineering in which computer models are used to predict the flow of fluids through porous media. Reservoir simulation process starts with several steps; data preparation, model and grid construction, initialization, history matching and prediction. Initialization process is done for matching OOIP or total initial hydrocarbon which fill reservoir with hydrocarbon control volume with volumetric method.</p><p>To aim the best encouraging optimum data, these development scenarios of TR Field Layer X will be predicted for 30 years (from 2014 until January 2044). Development scenarios in this study consist of 4 scenarios : Scenario 1 (Base Case), Scenario 2 (Base Case + Reopening non-active wells), Scenario 3 (scenario 2 + infill production wells), Scenario 4 (Scenario 2 + 5 spot pattern of infill injection wells).</p>

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