Inversion of the Fluvial Channel Reservoir Permeability Field Using Ensemble Kalman Filter Based on Discrete Cosine Transform

Wang, Yu‐Dou; He, Jiguang; Li, Gaoming

doi:10.1002/cjg2.1636

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…where, I and a denote the current and radius of the transmitter loop respectively, h denotes the height of the transmitter coil from the ground ( The 47-point Hankel filter coefficient proposed by [7] and the 300-point sinusoidal filter coefficient proposed by [8] are utilized to transform the frequency domain response to the time domain, so the timedomain response can be calculated by:…”

Section: Atem 1d Ip Forward Modelling With Natural Noisementioning

confidence: 99%

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Lu,

Wang,

et al. 2023

J. Phys.: Conf. Ser.

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Due to the induced polarization (IP) effects, abnormal fast decay and sign reversals are often observed in the field airborne transient electromagnetic (ATEM) responses, this phenomenon can well be explained by introducing the four-parameter Cole-Cole resistivity model. However, the introduction of multiple parameters makes the non-uniqueness of inversion more serious. To improve this problem, a weighted 1D laterally constrained inversion is proposed to recover the four IP parameters and thickness via adding different weighted factors chosen by lateral constraint conditions, in this paper, a synthetic polarization model is developed and 1D forward modeling with synthetic natural noise is conducted to imitate the field response. The proposed inversion is employed on the synthetic data and the results demonstrate that the method can improve the inversion stability and obtain more reliable result.

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Section: Atem 1d Ip Forward Modelling With Natural Noisementioning

confidence: 99%

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Lu,

Wang,

et al. 2023

J. Phys.: Conf. Ser.

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“…It is an adequate alternative to the EnKF if sequential propagation of the model is not required (e.g., when dynamic data are not assimilated frequently enough). An iterative ES is proposed to correct the nonlinear relationship between variables after the updating step, where an additional forecast step (conforming step) should be implemented after each updating step (Wen and Chen 2006). A similar approach to the ES, where data are integrated sequentially and the model is updated up to the current timestep conditional to data from current and past timesteps, is called the ensemble Kalman smoother (Evensen and van Leeuwen 2000;Evensen 2009).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs

Zagayevskiy

Deutsch

2015

Journal of Canadian Petroleum Technology

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This paper presents the applications of the ensemble-Kalmanfilter (EnKF) inverse-modelling technique to petroleum-reservoir characterization of thermally operated oil fields in northern Alberta, Canada. The EnKF is applied to 2D-and 3D-case studies based on the steam-assisted-gravity-drainage heavy-oil-extraction method. The modelling technique integrates effectively both static and dynamic data (petrophysical core data from wellbores, continuous temperature data measured by thermocouples, and 4D-seismic attributes) into a petroleum-reservoir model. Assimilated secondary information provides better insight into geologic properties of a reservoir and improves production forecasting. The method performs well for linear or slightly nonlinear systems that follow a Gaussian distribution, but shows worse performance for nonlinear or non-Gaussian systems. Integration of a large amount of data with a small number of realizations leads to ensemble collapse because of insufficient degrees of freedom. Increasing the ensemble size is a solution, but by increasing forecasting time. To overcome these issues and reduce computational time, matrix localization techniques and a shortcut based on replacement of the model realizations with their mean in forecasting are suggested. Even though the EnKF has been proved to be simple in implementation and effective for modelling of continuous linear systems, special care should be taken for modelling nonlinear systems, including categorical variables (e.g., geologic facies). IntroductionWhile developing a petroleum reservoir, one seeks responsible management and profit maximization. A clear understanding of petroleum-reservoir geology helps to select appropriate technology for oil extraction and place production/injection wells and surface facilities effectively. Modelling of the petroleum system in conjunction with its static and dynamic components in the form of petrophysical properties and time variants of physical conditions of a reservoir leads to efficient field development. This conceptual approach is vital in northern Alberta, where large portions of the heavy-oil and bituminous reservoirs are developed by the steam-assisted-gravity-drainage (SAGD) oil-extraction method.To accurately describe behaviour of the reservoir and predict future performance, various computational tools are available for petroleum-reservoir characterization. Most of them use a concept of data assimilation, in which available data are integrated into the model by minimizing the difference between the simulated data of the current model estimate and the actual data at observation locations. While both static and dynamic data are being assimilated into the model to improve its quality, spatial structure and physical principles embedded in the model should be preserved (Oliver et al. 2008).Reservoir characterization is an inverse problem, where limited data are used to infer the entire spatial configuration of the modelling system (Oliver et al. 2008). A solution to the inverse problem is not unique, which me...

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“…As an optimal estimation technique, the Kalman filter has been widely used in parameters estimation for dynamic systems. Wang et al (2011) used the Kalman filter to invert for the fluvial channel reservoir permeability field. Zhao et al (2014) applied the Kalman filter in the imaging of ionosphere TEC.…”

Section: Introductionmentioning

confidence: 99%

Time‐lapse Inversion of Self‐potential Data Using Kalman Filter

Cui

Wei

Zhu

et al. 2017

Chinese J of Geophysics

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It is very common to use the self-potential methods in environmental and engineering applications, especially in some monitoring services. However, the monitored data of each time step are always inverted and interpreted independently. That means the valuable correlation information of time-lapse data is totally ignored. In order to take full advantage of the correlation information, a time-lapse inversion was proposed to promote the reliability of data interpretation. Based on the Darcy's law and Archie's formulas, a dynamic geoelectric model was built to simulate the transportation of contaminant plume in underground porous medium. Then this dynamic model can be used as a state model for the Kalman filtering. And the corresponding observation model can be obtained from conventional self-potential forward calculation. Thus, a Kalman filter recursion can be constructed by using the state model and observation model. During the recursion, the information of geoelectric model evolution and observed self-potential data are fused to achieve a time-lapse inversion of self-potential data. The time-lapse inversion algorithm was tested by both noise added synthetic self-potential data and laboratory observation data from self-potential monitoring over a sandbox. The numerical test shows the validity, robustness, and tolerance to noise of the time-lapse inversion. And the results of physical data test also demonstrate that the time-lapse inversion can invert real time-lapse self-potential data successfully and retrieve the dynamic geoelectric model exactly.

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Inversion of the Fluvial Channel Reservoir Permeability Field Using Ensemble Kalman Filter Based on Discrete Cosine Transform

Cited by 3 publications

References 24 publications

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Weighted Laterally Constrained Inversion of Airborne Transient Electromagnetic Data with IP Effects

Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs

Time‐lapse Inversion of Self‐potential Data Using Kalman Filter

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