Effect of Scale Dependent Data Correlations in an Integrated History Matching Loop Combining Production Data and 4D Seismic Data

2003 ◽  
Author(s):  
S.I. Aanonsen ◽  
I. Aavatsmark ◽  
T. Barkve ◽  
A. Cominelli ◽  
R. Gonard ◽  
...  
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Dependent Data ◽  
Production Data ◽  
Data Correlations ◽  
4D Seismic

Combining Saturation Changes and 4D Seismic for Updating Reservoir Characterizations

2006 ◽  
Vol 9 (05) ◽  
pp. 502-512 ◽  
Author(s):  
Arne Skorstad ◽  
Odd Kolbjornsen ◽  
Asmund Drottning ◽  
Havar Gjoystdal ◽  
Olaf K. Huseby
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Seismic Survey ◽  
Production Data ◽  
Reservoir Model ◽  
Simple Method ◽  
4D Seismic ◽  
Elastic Inversion

Summary Elastic seismic inversion is a tool frequently used in analysis of seismic data. Elastic inversion relies on a simplified seismic model and generally produces 3D cubes for compressional-wave velocity, shear-wave velocity, and density. By applying rock-physics theory, such volumes may be interpreted in terms of lithology and fluid properties. Understanding the robustness of forward and inverse techniques is important when deciding the amount of information carried by seismic data. This paper suggests a simple method to update a reservoir characterization by comparing 4D-seismic data with flow simulations on an existing characterization conditioned on the base-survey data. The ability to use results from a 4D-seismic survey in reservoir characterization depends on several aspects. To investigate this, a loop that performs independent forward seismic modeling and elastic inversion at two time stages has been established. In the workflow, a synthetic reservoir is generated from which data are extracted. The task is to reconstruct the reservoir on the basis of these data. By working on a realistic synthetic reservoir, full knowledge of the reservoir characteristics is achieved. This makes the evaluation of the questions regarding the fundamental dependency between the seismic and petrophysical domains stronger. The synthetic reservoir is an ideal case, where properties are known to an accuracy never achieved in an applied situation. It can therefore be used to investigate the theoretical limitations of the information content in the seismic data. The deviations in water and oil production between the reference and predicted reservoir were significantly decreased by use of 4D-seismic data in addition to the 3D inverted elastic parameters. Introduction It is well known that the information in seismic data is limited by the bandwidth of the seismic signal. 4D seismics give information on the changes between base and monitor surveys and are consequently an important source of information regarding the principal flow in a reservoir. Because of its limited resolution, the presence of a thin thief zone can be observed only as a consequence of flow, and the exact location will not be found directly. This paper addresses the question of how much information there is in the seismic data, and how this information can be used to update the model for petrophysical reservoir parameters. Several methods for incorporating 4D-seismic data in the reservoir-characterization workflow for improving history matching have been proposed earlier. The 4D-seismic data and the corresponding production data are not on the same scale, but they need to be combined. Huang et al. (1997) proposed a simulated annealing method for conditioning these data, while Lumley and Behrens (1997) describe a workflow loop in which the 4D-seismic data are compared with those computed from the reservoir model. Gosselin et al. (2003) give a short overview of the use of 4D-seismic data in reservoir characterization and propose using gradient-based methods for history matching the reservoir model on seismic and production data. Vasco et al. (2004) show that 4D data contain information of large-scale reservoir-permeability variations, and they illustrate this in a Gulf of Mexico example.

Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization

2011 ◽  
Vol 14 (05) ◽  
pp. 621-633 ◽  
Author(s):  
Alireza Kazemi ◽  
Karl D. Stephen ◽  
Asghar Shams
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Production Data ◽  
Similar Degree ◽  
Full Field ◽  
Matching Process ◽  
Production History ◽  
Induced Changes ◽  
4D Seismic

Summary History matching of a reservoir model is always a difficult task. In some fields, we can use time-lapse (4D) seismic data to detect production-induced changes as a complement to more conventional production data. In seismic history matching, we predict these data and compare to observations. Observed time-lapse data often consist of relative measures of change, which require normalization. We investigate different normalization approaches, based on predicted 4D data, and assess their impact on history matching. We apply the approach to the Nelson field in which four surveys are available over 9 years of production. We normalize the 4D signature in a number of ways. First, we use predictions of 4D signature from vertical wells that match production, and we derive a normalization function. As an alternative, we use crossplots of the full-field prediction against observation. Normalized observations are used in an automatic-history-matching process, in which the model is updated. We analyze the results of the two normalization approaches and compare against the case of just using production data. The result shows that when we use 4D data normalized to wells, we obtain 49% reduced misfit along with 36% improvement in predictions. Also over the whole reservoir, 8 and 7% reduction of misfits for 4D seismic are obtained in history and prediction periods, respectively. When we use only production data, the production history match is improved to a similar degree (45%), but in predictions, the improvement is only 25% and the 4D seismic misfit is 10% worse. Finding the unswept areas in the reservoir is always a challenge in reservoir management. By using 4D data in history matching, we can better predict reservoir behavior and identify regions of remaining oil.

scholarly journals An Ensemble 4D-Seismic History-Matching Framework With Sparse Representation Based On Wavelet Multiresolution Analysis

SPE Journal ◽  
2016 ◽  
Vol 22 (03) ◽  
pp. 985-1010 ◽  
Author(s):  
Xiaodong Luo ◽  
Tuhin Bhakta ◽  
Morten Jakobsen ◽  
Geir Nævdal
Keyword(s):  
Sparse Representation ◽  
Multiresolution Analysis ◽  
Seismic Data ◽  
History Matching ◽  
Production Data ◽  
Reflection Coefficients ◽  
Wavelet Coefficients ◽  
Wavelet Multiresolution Analysis ◽  
Matching Performance ◽  
4D Seismic

Summary In this work, we propose an ensemble 4D-seismic history-matching framework for reservoir characterization. Compared with similar existing frameworks in the reservoir-engineering community, the proposed one consists of some relatively new ingredients, in terms of the type of seismic data in choice, wavelet multiresolution analysis for the chosen seismic-data and related-data noise estimation, and the use of recently developed iterative ensemble history-matching algorithms. Typical seismic data used for history matching, such as acoustic impedance, are inverted quantities, whereas extra uncertainties may arise during the inversion processes. In the proposed framework, we avoid such intermediate inversion processes. In addition, we also adopt wavelet-based sparse representation to reduce data size. Concretely, we use intercept and gradient attributes derived from amplitude vs. angle (AVA) data, apply multilevel discrete wavelet transforms (DWTs) to attribute data, and estimate noise level of resulting wavelet coefficients. We then select the wavelet coefficients above a certain threshold value, and history match these leading wavelet coefficients with an iterative ensemble smoother (iES). As a proof-of-concept study, we apply the proposed framework to a 2D synthetic case originated from a 3D Norne field model. The reservoir-model variables to be estimated are permeability (PERMX) and porosity (PORO) at each active gridblock. A rock-physics model is used to calculate seismic parameters (velocity and density) from reservoir properties (porosity, fluid saturation, and pressure); then, reflection coefficients are generated with a linearized AVA equation that involves velocity and density. AVA data are obtained by computing the convolution between reflection coefficients and a Ricker wavelet function. The multiresolution analysis applied to the AVA attributes helps to obtain a good estimation of noise level and substantially reduce the data size. We compare history-matching performance in three scenarios: (S1) with production data only, (S2) with seismic data only, and (S3) with both production and seismic data. In either Scenario S2 or Scenario S3, we also inspect two sets of experiments, one with the original seismic data (full-data experiment) and the other adopting sparse representation (sparse-data experiment). Our numerical results suggest that, in this particular case study, the use of production data largely improves the estimation of permeability, but has little effect on the estimation of porosity. Using seismic data only improves the estimation of porosity, but not that of permeability. In contrast, using both production and 4D-seismic data improves the estimation accuracies of both porosity and permeability. Moreover, in either Scenario S2 or Scenario S3, provided that a certain stopping criterion is equipped in the iES, adopting sparse representation results in better history-matching performance than using the original data set.

A method for performing history matching of reservoir flow models using 4D seismic data

2003 ◽  
Vol 9 (1) ◽  
pp. 83-90 ◽  
Author(s):  
M. Lygren ◽  
K. Fagervik ◽  
T.S. Valen ◽  
A. Hetlelid ◽  
G. Berge ◽  
...  
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Flow Models ◽  
Reservoir Flow ◽  
4D Seismic

A Case Study of Effective Metrics for History Matching the 4D Seismic Data

2019 ◽  
Author(s):  
H. Amini ◽  
M. Rodriguez ◽  
D. Wilkinson ◽  
G.R. Gadirova ◽  
C. MacBeth
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
4D Seismic

Reservoir Model Maturation and Assisted History Matching Based on Production and 4D Seismic Data

2014 ◽  
Author(s):  
Gerard J.P. Joosten ◽  
Asli Altintas ◽  
Gijs Van Essen ◽  
Jorn Van Doren ◽  
Paul Gelderblom ◽  
...  
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
Reservoir Model ◽  
Assisted History Matching ◽  
4D Seismic

A Study of the Impact of 4D-Seismic Data on TSVD-Based Schemes for History Matching

2010 ◽  
Author(s):  
Flavio Dickstein ◽  
Paulo Goldfeld ◽  
Gustavo Pfeiffer ◽  
Elisa Amorim ◽  
Rodrigo dos Santos ◽  
...  
Keyword(s):  
Seismic Data ◽  
History Matching ◽  
4D Seismic ◽  
The Impact

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

SPE Journal ◽  
2010 ◽  
Vol 15 (04) ◽  
pp. 1077-1088 ◽  
Author(s):  
F.. Sedighi ◽  
K.D.. D. Stephen
Keyword(s):  
Regression Analysis ◽  
Seismic Data ◽  
History Matching ◽  
Time Lapse ◽  
Business Decisions ◽  
Order Of Magnitude ◽  
Reservoir Simulation Model ◽  
The Uk ◽  
Pressure Changes ◽  
4D Seismic

Summary Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to identify the subspaces, which are then searched separately but simultaneously with an adapted version of the quasiglobal stochastic neighborhood algorithm. We have applied this approach to the Schiehallion field, located on the UK continental shelf. The field model, supplied by the operator, contains a large number of barriers that affect flow at different times during production, and their transmissibilities are highly uncertain. We find that we can successfully represent the misfit function as a second-order polynomial dependent on changes in barrier transmissibility. First, this enables us to identify the most important barriers, and, second, we can modify their transmissibilities efficiently by searching subgroups of the parameter space. Once the regression analysis has been performed, we reduce the number of models required to find a good match by an order of magnitude. By using 4D seismic data to condition saturation and pressure changes in history matching effectively, we have gained a greater insight into reservoir behavior and have been able to predict flow more accurately with an efficient inversion tool. We can now determine unswept areas and make better business decisions.

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