12,974 research outputs found
Continuous reservoir model updating by ensemble Kalman filter on Grid computing architectures
A reservoir engineering Grid computing toolkit, ResGrid and its extensions, were developed and applied to designed reservoir simulation studies and continuous reservoir model updating. The toolkit provides reservoir engineers with high performance computing capacity to complete their projects without requiring them to delve into Grid resource heterogeneity, security certification, or network protocols. Continuous and real-time reservoir model updating is an important component of closed-loop model-based reservoir management. The method must rapidly and continuously update reservoir models by assimilating production data, so that the performance predictions and the associated uncertainty are up-to-date for optimization. The ensemble Kalman filter (EnKF), a Bayesian approach for model updating, uses Monte Carlo statistics for fusing observation data with forecasts from simulations to estimate a range of plausible models. The ensemble of updated models can be used for uncertainty forecasting or optimization. Grid environments aggregate geographically distributed, heterogeneous resources. Their virtual architecture can handle many large parallel simulation runs, and is thus well suited to solving model-based reservoir management problems. In the study, the ResGrid workflow for Grid-based designed reservoir simulation and an adapted workflow provide tools for building prior model ensembles, task farming and execution, extracting simulator output results, implementing the EnKF, and using a web portal for invoking those scripts. The ResGrid workflow is demonstrated for a geostatistical study of 3-D displacements in heterogeneous reservoirs. A suite of 1920 simulations assesses the effects of geostatistical methods and model parameters. Multiple runs are simultaneously executed using parallel Grid computing. Flow response analyses indicate that efficient, widely-used sequential geostatistical simulation methods may overestimate flow response variability when compared to more rigorous but computationally costly direct methods. Although the EnKF has attracted great interest in reservoir engineering, some aspects of the EnKF remain poorly understood, and are explored in the dissertation. First, guidelines are offered to select data assimilation intervals. Second, an adaptive covariance inflation method is shown to be effective to stabilize the EnKF. Third, we show that simple truncation can correct negative effects of nonlinearity and non-Gaussianity as effectively as more complex and expensive reparameterization methods
Sensitivity of Fractured Reservoir Performance to Static and Dynamic Properties, and History Matching
Imperial Users onl
Impact of Stratigraphic and Sedimentological Heterogeneity on Hydrocarbon Recovery in Carbonate Reservoirs
Imperial Users onl
Streamline Simulation to Improve Polymer Enhanced Oil Recovery for a Mature Oil Field in Austria
Imperial Users onl
Multi-scale uncertainty quantification in geostatistical seismic inversion
Geostatistical seismic inversion is commonly used to infer the spatial
distribution of the subsurface petro-elastic properties by perturbing the model
parameter space through iterative stochastic sequential
simulations/co-simulations. The spatial uncertainty of the inferred
petro-elastic properties is represented with the updated a posteriori variance
from an ensemble of the simulated realizations. Within this setting, the
large-scale geological (metaparameters) used to generate the petro-elastic
realizations, such as the spatial correlation model and the global a priori
distribution of the properties of interest, are assumed to be known and
stationary for the entire inversion domain. This assumption leads to
underestimation of the uncertainty associated with the inverted models. We
propose a practical framework to quantify uncertainty of the large-scale
geological parameters in seismic inversion. The framework couples
geostatistical seismic inversion with a stochastic adaptive sampling and
Bayesian inference of the metaparameters to provide a more accurate and
realistic prediction of uncertainty not restricted by heavy assumptions on
large-scale geological parameters. The proposed framework is illustrated with
both synthetic and real case studies. The results show the ability retrieve
more reliable acoustic impedance models with a more adequate uncertainty spread
when compared with conventional geostatistical seismic inversion techniques.
The proposed approach separately account for geological uncertainty at
large-scale (metaparameters) and local scale (trace-by-trace inversion)
Processing and analysis of transient data from permanent down-hole gauges (PDG)
The Permanent Downhole Gauge (PDG) can monitor the reservoir in real time over a
long period of time. This produces a huge amount of real time data which can
potentially provide more information about wells and reservoirs. However, processing
large numbers of data and extracting useful information from these data brings new
challenges for industry and engineers.
A new workflow for processing the PDG data is proposed in this study. The new
approach processes PDG data from the view of gauge, well and reservoir. The gauge
information is first filtered with data preprocessing and outlier removal. Then, the
well event is identified using an improved wavelet approach. The further processing
step of data denoise and data reduction is carried out before analyzing the reservoir
information.
The accurate production history is very essential for data analysis. However, the
accurate production rate is hard to be acquired. Therefore, a new approach is created
to recover flow rate history from the accumulated production and PDG pressure data.
This new approach is based on the theory that the relation between production rate and
the amplitude of detail coefficient are in direct proportion after wavelet transform.
With accurate pressure and rate data, traditional well testing is applied to analyze the
PDG pressure data to get dynamic reservoir parameters. The numerical well testing
approach is also carried out to analyze more complex reservoir model with a new
toolbox. However, these two approaches all suffer from the nonlinear problem of PDG
pressure. So, a dynamic forward modelling approach is proposed to analyze PDG
pressure data. The new approach uses the deconvolution method to diagnose the linear
region in the nonlinear system. The nonlinear system can be divided into different
linear systems which can be analyzed with the numerical well testing approach.
Finally, a toolbox which includes a PDG data processing module and PDG data analysis
module is designed with Matlab
Impact of Stratigraphic Heterogeneity on Hydrocarbon Recovery in Carbonate Reservoirs: Effect of Fluid Properties and Development Strategy
Imperial Users onl
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
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