Reservoir characterization using gradual deformation method with Ensemble Kalman filter

Thesis(masters) --서울대학교 대학원 :에너지시스템공학부,2008.8.Maste

노달 계산결과로부터 원자로심내의 출력분포 재생을 위한 방법

학위논문(석사) - 한국과학기술원 : 핵공학과, 1988.2, [ vi, 58, [1] p. ]The objective of this thesis is to develop an accurate and computationally efficient method for reconstruction of pointwise power distributions from coarse-mesh nodalcalculations. Provided that homogenized parameters are properly determined in each node, the analytic nodal method code (ANM) calculates global reactor power shapes and criticality accurately. But inherent in nodal procedures, there is inevitable loss of information on local heterogeneous quantities. Hence a special technique is needed in order to recapture local properties where and when desired. In this study, an improved form function method which reflects the exponential transition of the thermal flux near the assembly surface is developed for the reconstruction of the heterogeneous fluxes. The distinctive feature of the new form function method is that the form function of the thermal flux is dependent on the form function of the fast flux which is approximated by the bi-quadratic polynomials, and the dependency is represented by the hyperbolic functions. Use of the new form function method in several PWR benchmark problems reduces the maximum errors in the reconstructed thermal flux to those in the reconstructed fast flux. In realistic PWER cores, use of this method also results in improved pointwise power reconstruction; the maximum reconstruction errors even for assemblies adjacent to the steel baffle are only half of those obtained by using the conventional bi-quadratic form function method.한국과학기술원 : 핵공학과

Influence of injection strategies on local capillary trapping during geological carbon sequestration in saline aquifers

Local capillary trapping (LCT) of CO2 is caused by the intrinsic heterogeneity of storage aquifers. It is computationally intensive to model LCT using conventional reservoir flow simulators. This work proposes a fast proxy method. We decouple the LCT modeling into two parts: permeability-based flow simulation using a connectivity analysis, and identification of local capillary traps (capillary entry pressure-based) using a geologic criterion. The connectivity analysis is employed to rapidly approximate CO2 plume evolution through estimating the arrival time of CO2. This analysis uses the geostatistical realization of permeability fields as input. The geologic criteria algorithm is used to estimate the potential local capillary traps from a given capillary entry pressure field. This field, through the Leverets j-function, is correlated to the permeability field used in the connectivity analysis. We then quantify the total volume of local capillary traps identified within the capillary entry pressure field that can be filled during CO2 migration. We conduct several simulations in the reservoirs with different levels of heterogeneity under various injection scenarios. We demonstrate the reservoir heterogeneity affects the optimal injection rate in maximizing LCT during CO2 injection. This work enhances our understanding of the effects of injections strategies on LCT.OAIID:RECH_ACHV_DSTSH_NO:T201815691RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080664CITE_RATE:5.503DEPT_NM:응용공학과EMAIL:[email protected]_YN:YN

Synchrotron X-ray Scattering Study of the Initial Stage of Thin Film Growth by ALD.

2

Cost-optimal design of pressure-based monitoring networks for carbon sequestration projects, with consideration of geological uncertainty

Leakage from geologic faults and abandoned wells represents one of the major risks to industrial-scale carbon capture and storage (CCS) projects. Current CCS regulations and best practice guidance suggest that operators emplace risk-informed monitoring, verification, and accounting (MVA) plans to protect public safety and reduce property and environmental damage. Deep subsurface pressure monitoring is regarded as one of the most cost-effective technologies for early leakage detection in CCS projects. In practice, however, the number of deep pressure monitoring wells that an operator can deploy often remains limited because of the high costs associated with drilling, instrumenting, and operating these wells. Thus, optimal design of the pressure monitoring network is essential to minimizing monitoring and liability costs and gaining public support. In this work, we present a general, binary integer programming approach to solve an optimal monitoring well network design problem under multiple constraints. Specifically, our approach helps a CCS operator to design a cost-optimal monitoring network that covers all potentially leaky locations (in a worst-case-scenario sense) while satisfying a prescribed carbon dioxide (CO2) storage performance criterion and considering geological uncertainty. Instead of using cost surrogates as has been done in many other studies, our formulation allows the user to directly assess total costs in terms of monitoring costs and potential economic losses associated with brine and CO2 leakage. Our numerical examples demonstrate that a cost-optimal monitoring network may save millions of dollars in total costs, including well construction and leakage costs. Factors exerting the most impact on the cost-optimal monitoring network design are unit leakage damage costs, pressure threshold for leakage detection, and geological uncertainty.OAIID:RECH_ACHV_DSTSH_NO:T201815736RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080664CITE_RATE:4.078DEPT_NM:응용공학과EMAIL:[email protected]_YN:YN

Fast evaluation of well placements in heterogeneous reservoir models using machine learning

Surrogate models, or proxies, provide computationally inexpensive alternatives for approximating reservoir responses. Proxy models are routinely developed to generate spatially-varying output such as field pressures and saturations, or well responses such as production rates and bottom- hole pressures. In this study, a machine learning approach is adopted to predict reservoir responses based on injector well locations. The proxy developed in this work is trained to reproduce reservoir-wide objective functions, i.e., total profit, cumulative oil/gas produced, or net CO2 stored. Because of the geological complexity of most reservoirs, slight adjustments in injector well locations could yield dramatic changes in the objective function responses. Hence, most proxies do not include well locations as inputs in their formulation. This complex relationship between well locations and reservoir-wide responses makes nonparametric, machine learning-based methods an attractive option. We introduce a machine learning approach in which the primary predictors are physical well locations, and the primary response is a defined objective function such as NPV. The complexity of the response surface with respect to well locations necessitates that we augment the predictor variables with well-to-well pairwise connectivities, injector block permeabilities and porosities, and initial injector block saturations. Introducing well-to-well connectivities yields significant improvements in prediction accuracy. Connectivities are represented by 'diffusive times of flight' of the pressure front, which is computed using the Fast Marching Method. A handful of training observations are obtained from numerical reservoir simulations. The Extreme Gradient Boosting method is then used to build an intelligent model for making predictions given any set of observations. The proposed approach is demonstrated using five synthetic case studies: i) a homogeneous reservoir waterflood, ii) a channelized reservoir waterflood, iii) a 20-model ensemble waterflood, iv) a CO2 flood in a heterogeneous reservoir, v) a CO2 flood in a heterogeneous reservoir with complex topography. Results show a significant correlation between proxy predictions and reservoir simulation results.OAIID:RECH_ACHV_DSTSH_NO:T201815752RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080664CITE_RATE:2.382DEPT_NM:응용공학과EMAIL:[email protected]_YN:YN

Integration of an Iterative Update of Sparse Geologic Dictionaries with ES-MDA for History Matching of Channelized Reservoirs

This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelized reservoir such as pattern and continuity. Integration of ES-MDA, DCT, and K-SVD is conducted in a complementary way as the initially static dictionaries are updated with dynamic data in each assimilation of ES-MDA. This update of dictionaries allows the coupled algorithm to yield an ensemble well conditioned to static and dynamic data at affordable computational costs. Applications of the proposed algorithm to history matching of two channelized gas reservoirs show that the hybridization of DCT and iterative K-SVD enhances the matching performance of gas rate, water rate, bottomhole pressure, and channel properties with geological plausibility

A learning-based data-driven forecast approach for predicting future reservoir performance

Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. In this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO2) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.OAIID:RECH_ACHV_DSTSH_NO:T201815728RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080664CITE_RATE:3.512DEPT_NM:응용공학과EMAIL:[email protected]_YN:YN

Utilization of multiobjective optimization for pulse testing dataset from a CO2-EOR/sequestration field

In a geological carbon storage project, leakage should be monitored to ensure safe long-term storage of injected CO2. Leakage can be detected early and cost-effectively by monitoring subsurface pressure. The uncertainty in geological models also needs to be sufficiently reduced to detect leakage based on pressure monitoring data. This study presents numerical results of field pulse testing experiments that are designed to detect leakage based on pressure monitoring data for periodical CO2 injection at a CO2 enhanced oil recovery field in Mississippi, USA. In the pulse test, sinusoidal pressure patterns are captured in transitional pressure data because CO2 injection and shut-in are repeated. The patterns are parameterized and history-matched efficiently in the frequency domain. Sensitivity analyses of pulse test parameters such as injection period and rate show that the frequency domain is more advantageous than the time domain for estimating leakage probability and well connectivity. We also conduct multi-objective history matching of pulse testing parameters in the frequency domain for reducing the uncertainty in geological models. This history matching reveals a clearer trade-off relationship between the matching qualities than conventional global-objective history matching, thereby being advantageous to yielding converged and diversified geological models for uncertainty quantification.OAIID:RECH_ACHV_DSTSH_NO:T201815719RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A080664CITE_RATE:2.382DEPT_NM:응용공학과EMAIL:[email protected]_YN:YN