Coupling Sequential-Self calibration and Genetic Algorithms to Integrate Production Data in Geostatistical Reservoir Modeling (original) (raw)
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Mathematical Geology, 2006
A geostatistically-based inverse technique, the sequential-self calibration (SSC) method, is used to update reservoir models so that they match observed pressure, water cut and time-lapse water saturation derived from 4-D seismic. Within the SSC, a steady-state genetic algorithm (GA) is applied to search the optimal master point locations, as well as the associated optimal permeability perturbations at the master locations. GA provides significant flexibility for SSC to parameterize master point locations, as well as to integrate different types of dynamic data because it does not require sensitivity coefficients. We show that the coupled SSC/GA method is very robust. Integrating dynamic data can significantly improve the characterization of reservoir heterogeneity with reduced uncertainty. Particularly, it can efficiently identify important large-scale spatial variation patterns (e.g., well connectivity, near well averages, high flow channels and low flow barriers) embedded in the reservoir heterogeneity. Using dynamic data, however, could be difficult to reproduce the permeability values on the cell-by-cell basis for the entire model. This reveals the important evidence that dynamic data carry information about large-scale spatial variation features, while they may be not sufficient to resolve the individual local values for the entire model. Through multiple realization analysis, the large-scale spatial features carried by the dynamic data can be extracted and represented by the ensemble mean model. Furthermore, the region informed by the dynamic data can be identified as the area with significant reduced variances in the ensemble variance model. Within this region, the cell-by-cell correlation between the true and updated permeability values can be significantly improved by integrating the dynamic data.
Automatic calibration of groundwater models using global optimization techniques
1999
Abstract The problem of a groundwater model calibration is posed as a multiextremum (global) optimization problem, rather than the more widely considered single-extremum (local) optimization problem. Several algorithms of randomized search incorporated in the global optimization tool GLOBE are considered (including the canonical genetic algorithm and more recently developed adaptive cluster covering), and applied to the calibration of the groundwater model TRIWACO.
Stochastic optimization for global minimization and geostatistical calibration
Journal of Hydrology, 2002
This study proposes a stochastic optimization technique that uses a gradient-based method as the primary optimization method, as well as a geostatistical conditional simulation to perturb and calibrate parameters at every local minimum. If the optimization process is trapped at a local minimum due to the limitations of the gradient-based method, it generates equiprobable parameter fields using a geostatistical conditional simulation. Among the generated fields, the optimization process selects one that enables the objective function to be reduced below the value of that at the local minimum, and then reactivates the gradient-based optimization. In generating equi-probable parameter fields, a constrained number of points (noted as releasing points) are randomly selected, and spatially correlated values are generated at the releasing points, conditioned to optimum parameters at the local minimum. By applying the stochastic optimization to synthetic permeability fields, it is observed that an inversed permeability field reproduces not only global distribution but also local spatial variability of the reference fields. In addition, the pressure distributions of the inversed and the reference field were much alike. To investigate dynamic properties of the inversed field and the reference field, streamline simulation was performed on both fields. Streamlines of the inversed field showed similar trajectories to those of the reference field, and time of flight (TOF) distribution of the inversed field was analogous to that of the reference field. The stochastic optimization technique proposed in this paper enables an inverse process to converge to a global minimum while preserving geostatistical properties such as mean, standard deviation, and variogram of an original field. Therefore, the stochastic optimization will be efficient in predicting future performance of a field from constrained number of permeability and pressure observation data.
Reconstruction of Existing Reservoir Model for Its Calibration to Dynamic Data
Earth Science Frontiers, 2008
The increase in computer power and the recent developments in history-matching can motivate the reexamination of previously built reservoir models. To save the time of engineers and the CPU time, four distinct algorithms, which allow for rebuilding an existing reservoir model without restarting the reservoir study from scratch, were formulated. The algorithms involve techniques such as optimization, relaxation, Wiener filtering, or sequential reconstruction. They are used to identify a stochastic function and a set of random numbers. Given the stochastic function, the random numbers yield a realization that is close to the existing reservoir model. Once the random numbers are known, the existing reservoir model can be submitted to a new history-matching process to improve the data fit or to account for newly collected data. A practical implementation is presented within the context of facies reservoirs. This article focuses on a previously built facies reservoir model. Although the simulation procedure is unknown to the authors, a set of random numbers are identified so that when provided to a multiple-point statistics simulator, a realization very close to the existing reservoir model is obtained. A new history-matching procedure is then run to update the existing reservoir model and to integrate the fractional flow rates measured in two producing wells drilled after the building of the existing reservoir model.
Integration of production data into reservoir models
Petroleum Geoscience, 2001
The problem of mapping reservoir properties, such as porosity and permeability, and of assessing the uncertainty in the mapping has been largely approached probabilistically, i.e. uncertainty is estimated based on the properties of an ensemble of random realizations of the reservoir properties all of which satisfy constraints provided by data and prior geological knowledge. When the constraints include observations of production characteristics, the problem of generating a representative ensemble of realizations can be quite difficult partly because the connection between a measurement of water-cut or GOR at a well and the permeability at some other location is by no means obvious. In this paper, the progress towards incorporation of production data and remaining challenges are reviewed.
2007
Abstract: This paper describes the new concept of a ‘Statistical Reservoir Model ’ to determine significant well-pair correlations. We solve this conceptual problem using a predictive error filter, combined with Bayesian methods that identify those well pairs that are related to each other with statistical significance, for the Gullfaks reservoir in the North Sea. Significant, long-range, corre-lations in the whole field are found at an optimal time lag of one month. The correlation function for significantly-correlated well pairs, after normalization for the distribution of available wells, shows a long-range power-law decay that is consistent with a critical-point response at the reser-voir scale. A principal component analysis shows a strong correlation with the location and orien-tation of faults that intersect the main producing horizon. A predictive experiment shows that the model performs very well both in history matching and predictive mode on a time scale of about one mont...
SPE Reservoir Evaluation & Engineering, 2000
The stochastic inversion of spatial distribution of lithofacies from multiphase production data is a difficult problem. This is true even for the simplest case, addressed here, of a sand/shale distribution and under the assumption that reservoir properties are constant within each lithofacies. Two geostatistically based inverse techniques, sequential self-calibration (SSC) and GeoMorphing (GM), are extended for such purposes and then compared with synthetic reference fields. The extension of both techniques is based on the one-to-one relationship existing between lithofacies and Gaussian deviates in truncated Gaussian simulation. Both techniques attempt to modify the field of Gaussian deviates while maintaining the truncation threshold field through an optimization procedure. Maintaining a fixed threshold field, which has been computed previously on the basis of prior lithofacies proportion data, well data, and other static soft data, guarantees preservation of the initial geostatistical structure. Comparisons of the two techniques using 2D and 3D synthetic data show that the SSC is very efficient in producing sand/shale realizations matching production data and reproducing the large-scale patterns displayed in the reference fields, although it has difficulty in reproducing small-scale features. GM is a simpler algorithm than SSC, but it is computationally more intensive and has difficulty in matching complex production data. Better results could be obtained with a combination of the two techniques in which SSC is used to generate realizations identifying large-scale features; then, these realizations could be used as input to GM for a final update to match small-scale details.
Applied Soft Computing, 2019
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights. A new intelligent framework to optimize well placement under field constraints. Successful application of the proposed framework for a real well placement project. A new hybrid technique for constraint handling is presented. Genetic algorithm, sampling design, and surrogate model to enhance the framework. Quite satisfactory results of the framework in a fractured unconventional reservoir.