Automatic History Matching by use of Response Surfaces and Experimental Design (original) (raw)
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Schemes for automatic history matching of reservoir modeling: A case of Nelson oilfield in UK
Petroleum Exploration and Development, 2012
Schemes for automatic history matching of reservoir modeling are studied for the Nelson oilfield in the Central North Sea. A complete workflow of automatic history matching involves selection of reservoir variables that require modification and parameter updating schemes, automatic history matching, data analysis, and combination of the best results to obtain an ensemble of best reservoir models. Automatic history matching of Nelson field is conducted using production and time-lapse seismic data, with global single-variable approach, regional multi-variable approach and local multi-variable approach as updating schemes, net to gross, horizontal and vertical permeability as updating parameters. It is revealed that local multi-variable approach can effectively improve history matched results by reducing the number of simulation models, saving computing time and increasing the simulation precision. Global single-variable approach is only a suitable parameter updating scheme for cases where the history matching parameters are independent. Regional multi-variable approach is suitable for the cases where there is strong dependency between properties chosen for updating, and there are wells very close together with strong interaction. Local multi-variable approach is very useful when the history matching parameters are dependent but each selected region for updating is independent of others.
2019
One of the major problems associated with history matching is the non-uniqueness of the solutions. A major flaw in this traditional history matching is that it lacks robustness as it shows a bias to the production data being matched while neglecting the mechanics governing other production data and such solutions generated are erroneous and gives a poor representation of the reservoir being matched.In this study, data driven and numerical modeling of a synthetic PUNQS3 reservoir were carried out. Single objective function, aggregated and multi-objective functions were adopted for the reservoir history matching. A proxy model was developed with data generated from a reservoir simulator using Artificial Neural Network (ANN) and the Response Surface Methodology (RSM). Firefly Optimization (FFO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms were used for the history matching process.The results showed that the history matching process was strongly influenced b...
A new approach for history matching of oil and gas reservoir
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010
This work proposes a new approach for history matching using Kernel PCA to adjust the reservoir permeability field obeying geostatistical constraint. Although there are several methodologies in literature for history matching, most of them don't take into account geostatistical restrictions. Besides, history matching is a problem of huge dimensionality. So, Kernel PCA was chosen due to its ability to compress and accurately reconstruct data in addition to being able to extract non-linear characteristics.
A General History Matching Algorithm for Three-Phase, Three-Dimensional Petroleum Reservoirs
SPE Advanced Technology Series, 1993
A numerical algorithm is developed to estimate absolute permeability in multi-phase. multilayered petroleum reservoirs based upon noisy observation data, such as pressure, water cut, gasoil ratio and rates of liquid and gas production from individual layers. A Chevron Black-Oil code is used as the basic reservoir simulator in conjunction with this history matching algorithm. Since the history matching inverse problem is ill-posed due to its large dimensionality and the insensitivity of the permeability to measured well data, regularization and spline approximation of the spatially varying absolute permeability are employed to render the problem computationally well behaved. A stabilizing functional with a gradient operator is used to measure the non-smoothness of the parameter estimates in the regularization approach, and the regularization parameter is determined automatically during the computation. The numerical minimization algorithm is based on the partial conjugate gradient method of Nazareth. Numerical examples are considered in two-and three-phase reservoirs with three layers. The effects of the degree of regularization. spline approximation versus zonation, and differing true areal permeability distributions on the performance of the method are considered.
Reservoir Simulation Design Strategy for Next-Generation Multi-level Assisted History Matching
International Petroleum Technology Conference, 2014
This paper introduces the bases for the design of next-generation automated workflows to implement advanced assisted history-matching (AHM) techniques. The paper presents procedures for geostatistical modeling, high-end dynamic flow simulation modeling, and the use of streamline tracing and visualization to generate a basic (fundamental) model for AHM. The accuracy of the base model is essential because this model is the starting point of the AHM process; therefore, the quality of the AHM process is dependent on the base model. The geomodel benefits from a combination of multiple lithotype proportion mapping (LPM) and plurigaussian simulation (PGS), which successfully represents complex, carbonate depositional settings with eight lithofacies and high-permeability channels. By honoring geostatistical variograms and core-log constraints, a reservoir model is generated with 1.4 million cells. The LPM indicated that 108 layers are sufficient to describe the vertical resolution of lithofacies in the reservoir. A three-dimensional (3D), three-phase, black-oil single-porosity numerical simulation model was developed. The dynamic model has three-phase relative permeability normalization that computes the effects of parameterizing rock type and permeability distribution in the static model. The model is complex, as it has 16 equilibrium regions and two pressure volume temperature (PVT) regions. The simulation model includes 49 wells in 5 waterflood patterns to match 50 years of production, 12 years of injection, and 8 years of forecasting. The model was optimized for minimum simulation time. The base case was used for a) closed-loop, multilevel probabilistic history matching with parameterization of geostatistical and reservoirdynamic properties and b) dynamic model ranking (DMR) and uncertainty quantification based on predicted oil recovery factor (ORF). This workflow was implemented at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. It generates faster and more accurate history matching updates, produces a high-resolution reservoir model with no upscaling, and calculates waterflood indicators, including voidage replacement, water injector efficiency, producer well allocations, sweep efficiencies, and recovery factors.
Recent progress on reservoir history matching: a review
Computational Geosciences, 2011
History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. In this review paper, we will summarize key developments in history matching and then review many of the accomplishments of the past decade, including developments in reparameterization of the model variables, methods for computation of the sensitivity coefficients, and methods for quantifying uncertainty. An attempt has been made to compare representative procedures and to identify possible limitations of each.
Adaptive Selection of Parameterization for Reservoir History Matching
ECMOR VIII - 8th European Conference on the Mathematics of Oil Recovery, 2002
We consider estimation of the absolute permeability, and enforce regularization by seeking to select the parameter space such that over-parameterization is avoided. We perform the parameterization selection during the history match, by solving a sequence of parameter estimation problems with increasing resolution for the permeability. The number and spatial location of new degrees of freedom to be introduced in the permeability representation at a stage in the estimation sequence are selected according to predetermined criteria. Alternative criteria are discussed and tested .
Petro-Elastic Parameters Effects on History Matching Procedures
SPE EUROPEC/EAGE Annual Conference and Exhibition, 2011
Rock physics models are the quantitative link between the seismic information and reservoir properties. Thus, several authors have been used this link to develop methodologies aiming to integrate seismic derived information and production history matching. Nevertheless, there are significant uncertainties related to petro-elastic parameters definition and its effects on typical history matching results. This paper evaluates this problem through the application of two history matching procedures. The empirical allowed range of petro-elastic parameters regarding fluid and rock behavior, such as temperature, salinity and porosity, mineral and dry rock modulus, respectively, are combined to define different data sets. Each set defines petro-elastic models to be coupled to a numerical reservoir model providing synthetic impedance distributions. These sets of impedance are then used in two different integrated history matching techniques. First, each set is combined with production data defining a global objective function to be minimized and provide a new horizontal permeability distribution resulting in an updated model. Secondly, a constrained inversion procedure is applied to convert impedance into saturation and pressure distributions. These maps indicate the optimization regions and the quantitative seismic information to improve the global objective function minimization accuracy aiming to derive a new permeability distribution. Thus, permeability and bottom-hole pressure from the updated reservoir models are compared to evaluate the several combinations of petro-elastic parameters effects. The major goals of this paper were to quantify the petro-elastic parameters definition effects on history matching procedures, indicating which of them is more sensitive to their variations. Furthermore, it was possible to assess their individual impact in the history matching results and highlight the importance of a careful definition of these parameters and its coherent integration with fluid flow models. This approach also contributes to quantitative integration aspects of oil reservoir development and management.
Computational Geosciences, 2008
A method for history matching of an in-house petroleum reservoir compositional simulator with multipoint flux approximation is presented. This method is used for the estimation of unknown reservoir parameters, such as permeability and porosity, based on production data and inverted seismic data. The limitedmemory Broyden-Fletcher-Goldfarb-Shanno method is employed for minimization of the objective function, which represents the difference between simulated and observed data. In this work, we present the key features of the algorithm for calculations of the gradients of the objective function based on adjoint variables. The test example shows that the method is applicable to cases with anisotropic permeability fields, multipoint flux approximation, and arbitrary fluid compositions.