Combined Stochastic and Deterministic Reservoir Characterization Leads to Faster History Matching of a Horizontal Well in a Complex Fluvio-Lacustrine Environment

1994 ◽  
Author(s):  
P.M. Calatayud ◽  
F.M. Petit ◽  
Philippe Tessier ◽  
R.J. Norris ◽  
F.G. Alabert
Keyword(s):  
History Matching ◽  
Horizontal Well ◽  
Reservoir Characterization ◽  
Lacustrine Environment

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 A Parallel Stochastic Framework for Reservoir Characterization and History Matching

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Sunil G. Thomas ◽  
Hector M. Klie ◽  
Adolfo A. Rodriguez ◽  
Mary F. Wheeler
Keyword(s):  
Porous Media ◽  
Spatial Distribution ◽  
Multiphase Flow ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Species Transport ◽  
Medium Permeability ◽  
Stochastic Framework ◽  
Sensor Information

The spatial distribution of parameters that characterize the subsurface is never known to any reasonable level of accuracy required to solve the governing PDEs of multiphase flow or species transport through porous media. This paper presents a numerically cheap, yet efficient, accurate and parallel framework to estimate reservoir parameters, for example, medium permeability, using sensor information from measurements of the solution variables such as phase pressures, phase concentrations, fluxes, and seismic and well log data. Numerical results are presented to demonstrate the method.

Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jihoon Park ◽  
Jeongwoo Jin ◽  
Jonggeun Choe
Keyword(s):  
Uncertainty Quantification ◽  
Travel Time ◽  
History Matching ◽  
Reservoir Characterization ◽  
Uncertainty Assessment ◽  
Dynamic Responses ◽  
Time Inversion ◽  
Dynamic Data ◽  
Reservoir Models ◽  
Permeability Distribution

For decision making, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since initial models constructed with limited data have high uncertainty, it is essential to integrate both static and dynamic data for reliable future predictions. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching, and multiple realizations of reservoir models should be computed. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline-based inversion and distance-based clustering. A distance between each reservoir model is defined as the norm of differences of generalized travel time (GTT) vectors. Then, reservoir models are grouped according to the distances and representative models are selected from each group. Inversions are performed on the representative models instead of using all models. We use generalized travel time inversion (GTTI) for the integration of dynamic data to overcome high nonlinearity and take advantage of computational efficiency. It is verified that the proposed method gathers models with both similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances reliably, while reducing the amount of calculations significantly by using the representative models.

Enhanced History Matching of Gas Reservoirs With an Aquifer Using the Combination of Discrete Cosine Transform and Level Set Method in ES-MDA

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Sungil Kim ◽  
Hyungsik Jung ◽  
Jonggeun Choe
Keyword(s):  
Discrete Cosine Transform ◽  
Level Set Method ◽  
Level Set ◽  
Measurement Errors ◽  
History Matching ◽  
Reservoir Characterization ◽  
Gas Reservoirs ◽  
Cosine Transform ◽  
Ensemble Smoother ◽  
Non Gaussian

Reservoir characterization is a process to make dependable reservoir models using available reservoir information. There are promising ensemble-based methods such as ensemble Kalman filter (EnKF), ensemble smoother (ES), and ensemble smoother with multiple data assimilation (ES-MDA). ES-MDA is an iterative version of ES with inflated covariance matrix of measurement errors. It provides efficient and consistent global updates compared to EnKF and ES. Ensemble-based method might not work properly for channel reservoirs because its parameters are highly non-Gaussian. Thus, various parameterization methods are suggested in previous studies to handle nonlinear and non-Gaussian parameters. Discrete cosine transform (DCT) can figure out essential channel information, whereas level set method (LSM) has advantages on detailed channel border analysis in grid scale transforming parameters into Gaussianity. However, DCT and LSM have weaknesses when they are applied separately on channel reservoirs. Therefore, we propose a properly designed combination algorithm using DCT and LSM in ES-MDA. When DCT and LSM agree with each other on facies update results, a grid has relevant facies naturally. If not, facies is assigned depending on the average facies probability map from DCT and LSM. By doing so, they work in supplementary way preventing from wrong or biased decision on facies. Consequently, the proposed method presents not only stable channel properties such as connectivity and continuity but also similar pattern with the true. It also gives trustworthy future predictions of gas and water productions due to well-matched facies distribution according to the reference.

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