Integration Of 4D Seismic Data Research Articles

Image-oriented distance parameterization for ensemble-based seismic history matching

Time-lapse (4D) seismic data provides spatial information about dynamic changes in a reservoir. This information can be used in combination with production data from wells to adjust reservoir models through a history matching procedure. However, quantitative use of 4D seismic data for history matching is generally challenging. This is partly because of the high dimensionality and the large uncertainty associated with the seismic data. To circumvent these difficulties, some methods have been proposed with a focus on reparameterization of seismic attributes and reformulation of seismic objective functions so that the integration of 4D seismic data is more robust and efficient. A distance parameterization of seismic anomalies due to saturation effects was previously proposed to history match reservoir models in combination with the ensemble Kalman filter (EnKF). Because the parameterization reduces both nonlinearity and the effective number of data, an improved functioning of the EnKF could be achieved. The distances measured from simulated fronts to observed fronts were used as innovations in the EnKF such that the same number of simulated measurements is obtained for each simulated model realization. However, it turns out that this definition is not always an effective measure of the actual differences between simulated and observed fronts as we demonstrate through the experiments in this paper. Using concepts from the field of image analysis, we generalize the defined innovations as a directed local Hausdorff distance, providing a one-directional dissimilarity measure (from simulated to observed fluid fronts, or in a broader sense, to detected features in 4D seismic data). A more robust distance parameterization approach is proposed based on the full local Hausdorff distance that measures the distances between observed and simulated fronts in both directions. Additionally, a comparison is made between contour-based and area-based characterizations of the differences between images that are commonly used for shape matching within the field of image analysis. Numerical experiments in a synthetic 2D case and realistic synthetic 3D case based on the Norne field are presented in which improved functioning of the proposed method is demonstrated.

Stochastic Data Integration: History Matching Production and Time-Lapse-Seismic Data

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 146418, ’A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse-Seismic Data,’ by Long Jin, SPE, Faruk O. Alpak, SPE, and Paul van den Hoek, SPE, Shell International E&P, and Carlos Pirmez, Tope Fehintola, SPE, Fidelis Tendo, and Elozino Olaniyan, SPE, Shell Nigeria E&P, prepared for the 2011 SPE Annual Technical Conference and Exhibition, Denver, 30 October-2 November. The paper has not been peer reviewed. Quantitative integration of spatial and temporal information provided by time-lapse (4D) -seismic surveys into dynamic reservoir models requires efficient and effective data-integration algorithms. The particle-swarm-optimization (PSO) method emerged as more effective compared with the neighborhood algorithm (NA) and very-fast-simulated-annealing (VFSA) methods in the Imperial College Fault Model (ICFM) problem. The PSO method was also effective in the field application. The VFSA method required comparatively more iterations to converge because of its sequential nature, but it has advantages when moderate computing resources are available. Introduction The use of 4D-seismic data is increasingly important for reservoir monitoring and management. However, quantitative integration of 4D-seismic data with historical production data into reservoir-simulation models is a challenging task. A key issue of joint history matching, involving production and time-lapse-seismic data, is proper processing of 4D-seismic data such that physical information (fluid saturations) is captured by geophysical measurements properly. Various types of geophysical measurements have been assimilated in the literature, including inverted impedance, inverted saturation, and fluid fronts. Another important issue centers on appropriate selection of model parameters to be adjusted to obtain a history match. In this work, three stochastic data-integration methods were evaluated: NA, VFSA, and PSO. They were compared on the joint history-matching problem involving production and time-lapse-seismic data. Stochastic Data-Integration Methods Data integration in the presence of time-lapse-seismic data consists of updating reservoir dynamic models by jointly assimilating both 4D-seismic and production data. The goal is to use the complementary nature of 4D-seismic data (spatial and temporal information) and production data (temporal information) to produce more-predictive reservoir models, and in turn, make optimal reservoir-management decisions. In this work, the focus was on nonintrusive stochastic data-integration methods. No special information (e.g., adjoint sensitivity coefficients or gradients of the objective function) was needed from the flow and seismic forward models. A generic iterative workflow for joint 4D-seismic and production history matching conducted by a stochastic sampling engine (optimizer) is shown in Fig. 1. The main idea was to perturb the uncertain (model) parameters identified by comparing the synthetic data (production and 4D-seismic) with the measurements simultaneously. The basic idea is also shown in Fig. 2.

History Matching of Production and 4D Seismic Data: Application to the Girassol Field, Offshore Angola

Time-lapse seismic provides a source of valuable information about the evolution in space and time of the distribution of hydrocarbons inside reservoirs. Seismic monitoring improves our understanding of production mechanisms and makes it possible to optimize the recovery of hydrocarbons. Although 4D seismic data are increasingly used by oil companies, they are often qualitative, due to the lack of suitable interpretation techniques.Recent modeling experiments have shown that the integration of 4D seismic data for updating reservoir flow models is feasible. However, methodologies based on sequential interpretation of 4D seismic data, trial and error processes and fluid flow simulation tests require a great effort from integrated teams. The development of assisted history matching techniques is a significant improvement towards a quantitative use of 4D seismic data in reservoir modeling.This paper proposes an innovative methodology based on advanced history matching solutions to constrain 3D stochastic reservoir models to both production history and 4D seismic attributes. In this approach, geostatistical modeling, upscaling, fluid flow simulation, downscaling and petro-elastic modeling are integrated into the same history matching workflow. Simulated production history and 4D seismic attributes are compared to real data using an objective function, which is minimized with a new optimization algorithm based on response surface fitting. The gradual deformation method is used to constrain the facies realization, globally or locally, which populates the geological model at the fine scale. Moreover, a new method is proposed to update facies proportions during the optimization process according to 4D monitoring information.We present here a successful application to the Girassol field. Girassol is a large, complex and faulted turbidite field, located offshore Angola. First, a detailed geostatistical geological model was built to describe reservoir heterogeneity at the fine scale, while respecting 3D base seismic data. Second, the model was constrained to production data and 4D seismic attributes, applying gradual deformation to facies realizations and varying facies proportions. The integration of 4D seismic data led to better production forecasts and improved predictions confirmed by a new seismic survey shot two years after the history matching period. 4D seismic data also contributed to better characterize the spatial distribution of heterogeneities in the field. As a result, the fine scale geological model was improved consistently with respect to the fluid flow simulation model and the observed data. The Girassol study, already presented in (Roggero et al., 2007, 2008), has been updated with recent information and a more detailed presentation concerning the construction of the geological model based on 3D seismic data.

Integration of 4D seismic data and the dynamic reservoir model reveal new targets in Gannet C

Two sets of 4D seismic data gave major new insights into the structure and dynamic behavior of the Gannet C oil and gas reservoir in the U.K. Central North Sea. The 4D data revealed major extensions of reservoir units previously presumed to be absent or thin over much of the reservoir. Furthermore, in a subsea field with significant uncertainty of production allocation, 4D also proved an invaluable history-matching parameter for the dynamic model. Together, the dynamic model and the 4D data gave rise to the identification of one recompletion opportunity and two infill well opportunities, to produce oil volumes in existing and newly identified reservoir sands. These now have been realized, roughly trebling oil production from the field.

4D Seismic Data Assist Management of Reservoirs With Horizontal Wells Between Fluid Contacts

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 83956, "Integration of 4D Seismic Data Into the Management of Oil Reservoirs With Horizontal Wells Between Fluid Contacts," by Peter J. Clifford, SPE, Robert Trythall, Ronnie S. Parr, Tim P. Moulds, SPE, Tim Cook, and Peter M. Allan, BP plc, and Phil Sutcliffe, SPE, RML, prepared for 2003 Offshore Europe, Aberdeen, 2-5 September.