4D Seismic History Matching of a Real Field Case with EnKF: Use of Local Analysis for Model Updating

Fahimuddin, Abul; Aanonsen, Sigurd Ivar; Skjervheim, Jan-Arild

doi:10.2118/134894-ms

Cited by 18 publications

(4 citation statements)

References 20 publications

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“…Details about the PEM implementation can be found in Emerick et al [8]. Among the elastic properties typically used for seismic data history matching, the most common choices are pressure-wave impedance (P-impedance or acoustic impedance) and Poisson's ratio; see, e.g., [15,21,22,40,47]. However, other seismic attributes such as amplitudes [23] and time shifts [27,41] have also been used.…”

Section: Time-lapse Seismic Datamentioning

confidence: 99%

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History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

2012

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The ensemble Kalman filter (EnKF) has become a popular method for history matching production and seismic data in petroleum reservoir models. However, it is known that EnKF may fail to give acceptable data matches especially for highly nonlinear problems. In this paper, we introduce a procedure to improve EnKF data matches based on assimilating the same data multiple times with the covariance matrix of the measurement errors multiplied by the number of data assimilations. We prove the equivalence between single and multiple data assimilations for the linearGaussian case and present computational evidence that multiple data assimilations can improve EnKF estimates for the nonlinear case. The proposed procedure was tested by assimilating time-lapse seismic data in two synthetic reservoir problems, and the results show significant improvements compared to the standard EnKF. In addition, we review the inversion schemes used in the EnKF analysis and present a rescaling procedure to avoid loss of information during the truncation of small singular values.

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Section: Time-lapse Seismic Datamentioning

confidence: 99%

“…Aanonsen et al [1] present a comprehensive review of EnKF applications to reservoir problems. Some recent field applications of EnKF can be found in [6,9,15,39]. Even though the EnKF was originally proposed as an alternative to the extended Kalman filter [13,Chap.…”

Section: Introductionmentioning

confidence: 99%

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

2012

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“…In stochastic approaches, Skjervheim et al (2007) used the ensemble Kalman smoother to assimilate the time-lapse seismic data of changes in acoustic impedance and compressional velocity. Fahimuddin et al (2010) similarly used the seismic derived impedance data with the ensemble Kalman filter with a covariance localization method. Feng and Mannseth (2010) applied a pseudo seismic data in the form of maps of saturation changes and investigated the impact of the seismic data in the presence of noise on permeability estimation.…”

Section: Introductionmentioning

confidence: 99%

Streamline-Based Time Lapse Seismic Data Integration Incorporating Pressure and Saturation Effects

Watanabe

Han

Datta‐Gupta

et al. 2013

SPE Annual Technical Conference and Exhibition

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We present an efficient history matching approach that simultaneously integrates 4-D repeat seismic surveys with well production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data is areally dense but sparse in time, while the production data is continuous in time but averaged over interwell spacing. The joint history matching is performed using streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. Previous approaches to seismic data integration have mostly incorporated saturation effects but the pressure effects have largely been ignored. We propose, for the first time, streamline-based analytic approaches to compute parameter sensitivities that relate the seismic data to reservoir properties while accounting for both pressure and saturation effects via appropriate rock physics models. The inverted seismic data (e.g., changes in acoustic impedance or fluid saturations), is distributed as a 3-D high-resolution grid cell property or as a vertically integrated two-dimensional map. We derive pressure drop sensitivities along streamlines in addition to our previous work of water saturation sensitivity computation. The novelty of the method lies in the analytic sensitivity computations which make it computationally efficient for high resolution geologic models. We demonstrate the versatility of our approach by implementing it in a finite difference simulator which incorporates detailed physical processes, while the streamline trajectories provide for rapid evaluation of the sensitivities. The efficacy of our proposed approach is demonstrated with both synthetic and field applications. The synthetic example is the SPE benchmark Brugge field case. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. For both the synthetic and the field cases, the advantages of incorporating the time-lapse variations are clearly demonstrated through improved estimation of the permeability distribution, pressure profile, and fluid saturation evolution and swept volumes.

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“…There have been substantial efforts on the integration of production and time-lapse seismic data for improving reservoir characterization and reservoir model history-matching (Huang et al, 1997;Kretz et al, 2002;Waggoner et al, 2003;Gosselin et al, 2003;Stephen et al, 2006;Roggero et al, 2007;Emerick et al, 2007;Dong and Oliver, 2008;Dadashpour et al, 2010;Fahimuddin et al, 2010;Landa and Kumar, 2011;Jin et al, 2011). In those work, the time-lapse changes of seismic attributes need to be obtained from processing of base and monitor seismic surveys before integrating into the history-matching process.…”

Section: Introductionmentioning

confidence: 99%

Joint inversion of time-lapse crosswell seismic and production data for reservoir monitoring and characterization

2013

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We present a fluid-flow constrained inversion approach for joint interpretation of time-lapse seismic and production data. In this approach, the full-waveform seismic inversion is integrated into the traditional history-matching process. Hence, the interpretation of time-lapse seismic data is constrained by the fluid-flow physics in the reservoir. The key component in the workflow is a fluid-flow simulator, which computes not only the production data in the wells, but also the temporal and spatial distribution of fluid properties, such as fluid saturation and pressure. These fluid properties, together with prior rock properties, can be transformed to acoustic properties using the prescribed petro-elastic model. A finite-difference frequency-domain acoustic solver is then used to simulate the time-lapse seismic responses on the reservoir. We use a multiplicative-regularized Gauss-Newton scheme to update the reservoir model iteratively until good match between measured and simulated data is achieved. The derivative of seismic data with respect to acoustic properties is calculated using the adjoint method, and then connected to reservoir parameters using a chain rule derived from the petro-elastic model, while the derivative of production data with respect to reservoir parameters is calculated using the gradientsimulator method. A synthetic crosswell example is employed to demonstrate that the estimation of permeability and flooding front movement can be significantly improved from the joint inversion of time-lapse seismic and production data.

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4D Seismic History Matching of a Real Field Case with EnKF: Use of Local Analysis for Model Updating

Cited by 18 publications

References 20 publications

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

Streamline-Based Time Lapse Seismic Data Integration Incorporating Pressure and Saturation Effects

Joint inversion of time-lapse crosswell seismic and production data for reservoir monitoring and characterization

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