Full Field History Matching For Chemical Flooding With The Adjoint Method

2013 ◽  
Author(s):  
Sippe G Douma ◽  
Issa M Abu-Shiekah ◽  
Zakariya Yahya Kindi
Keyword(s):  
History Matching ◽  
Adjoint Method ◽  
Chemical Flooding ◽  
Full Field

Geologically Consistent History Matching Honouring Rock Types Driven by Adjoint Method

2020 ◽  
Author(s):  
Bettina Jenei ◽  
Leonhard Ganzer ◽  
Hussein Almuallim ◽  
Roman Manasipov
Keyword(s):  
History Matching ◽  
Adjoint Method ◽  
Rock Types

Multi-Realisation Full-Field History-Matching For CBM Resources a New Approach with Global Optimisation Coupled with Geo-Statistical Filtering

2013 ◽  
Author(s):  
Laurent D. Alessio ◽  
Haitham Nasri ◽  
Sophoana Pan ◽  
Lee Thian Urn Clement ◽  
Chua Ket Peng
Keyword(s):  
History Matching ◽  
Global Optimisation ◽  
New Approach ◽  
Full Field

Parameters Estimation for Chemical Flooding Modeling Using Ensemble-Based Method

2021 ◽  
Author(s):  
Xindan Wang ◽  
Yin Zhang ◽  
Abhijit Dandekar ◽  
Yudou Wang
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
History Matching ◽  
Simulation Models ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Chemical Flooding ◽  
Multiple Model ◽  
Assisted History Matching ◽  
Matching Techniques

Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.

Optimization of Tracer Injection Schemes for Improved History Matching

2021 ◽  
Author(s):  
Hsieh Chen ◽  
Hooisweng Ow ◽  
Martin E Poitzsch
Keyword(s):  
History Matching ◽  
Large Scale ◽  
Reservoir Characterization ◽  
Statistical Significance ◽  
Scale Up ◽  
Production Optimization ◽  
Tracer Injection ◽  
Full Field ◽  
Multiple Data ◽  
Mean Square Errors

Abstract Interwell tracers are powerful reservoir surveillance tools that provide direct reservoir flow paths and dynamics, which, when integrated with near real-time production optimization, can greatly improve recovery factor, and return on investment, the so-called "Advanced Tracers System" (ATS). Applying full field ATS is attractive for resource-holders, especially for those with large waterflood operations. However, to scale up ATS to cover large fields with potentially tens to hundreds of injectors and producers, the required unique tracer variations ("barcodes") and materials and associated analysis may increase rapidly. Here, we explore different tracer injection schemes that can acquire the most information while using reduced numbers of tracers, thereby controlling costs in field operations. We tested the designs of various modified tracer injection schemes with reservoir simulations. Numerical experiments were performed on synthetic fields with multiple injector and producer wells in waterflooding patterns. Two tracer injection schemes were considered: In Scheme 1, all injectors were injected with unique tracers representing the most information-rich case. In Scheme 2, some injectors were injected with the same tracers ("recycling" the same barcodes), and some injectors received no tracer injection ("null" barcodes). Production and tracer breakthrough data was collected for history matching after waterflooding simulations on the synthetic fields. The ensemble smoother with multiple data assimilation with tracers algorithm was used for history matching. We calculated the root-mean-square errors (RMSE) between the reference data and the history matched production simulation data. To improve the statistics, 20 independent testing reference synthetic fields were constructed by randomizing the number and locations of high permeability zones crossing different injectors and producers. In all cases, the history matching algorithms largely reduced the RMSE thereby enhancing reservoir characterization. Analyzing the statistical significance with p-values among testing cases, first, as expected, the data mismatch is highly significantly lower after history matching than before history matching (p < 0.001). Second, the data mismatch is even lower when history matching with tracers (both in Scheme 1 and 2) than without tracers (p < 0.05), demonstrating clearly that tracers can provide extra information for the reservoir dynamics. Finally, and most importantly, history matching with tracers in Scheme 1 or in Scheme 2 result in statistically the same data mismatch (p > 0.05), indicating the cost-saving "recycling" and "null" tracer barcodes can provide equally competent reservoir information. To the best of our knowledge, this is the first study that evaluated the history matching qualities deriving from different tracer injection schemes. We showed that through optimal designs of the tracer injections, we can acquire very similar information with reduced tracer materials and barcodes, thus reducing costs and field operational complexities. We believe this study facilitates the deployment of large-scale reservoir monitoring and optimization campaigns using tracers such as ATS.

History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother

2014 ◽  
Vol 17 (02) ◽  
pp. 244-256 ◽  
Author(s):  
Yan Chen ◽  
Dean S. Oliver
Keyword(s):  
History Matching ◽  
Oil And Gas ◽  
Field Model ◽  
Model Parameters ◽  
Data Types ◽  
Full Field ◽  
Ensemble Smoother ◽  
Main Fault ◽  
Iterative Ensemble Smoother ◽  
Partial Communication

Summary Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based methods. In this paper, we describe the application of the iterative ensemble smoother to the history matching of the Norne field, a North Sea field, with a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of the identification of important variables, the generation of an initial ensemble, the plausibility of results, and the efficiency of minimization. We also discuss the challenges encountered in the use of the ensemble-based method for complex-field case studies that are not typically encountered in synthetic cases. The Norne field produces from an oil-and-gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and 9 injectors in the field. Water-alternating-gas injection is used as the depletion strategy. Production rates of oil, gas, and water of 22 producers from 1997 to 2006 and repeat-formation-tester (RFT) pressure from 14 different wells are available for model calibration. The full-field simulation model has 22 layers, each with a dimension of 46 × 112 cells. The total number of active cells is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother (LM-EnRML) is used for history matching. The model parameters that are updated include permeability, porosity, and net-to-gross (ntg) ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four different reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from LM-EnRML. LM-EnRML is able to achieve improved data match compared with the manually history-matched model after three iterations. Updates from LM-EnRML do not introduce artifacts in the property fields as in the manually history-matched model. The automated workflow is also much less labor-intensive than that for manual history matching.

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