Ensemble-Based Assisted History Matching With 4D-Seismic Fluid-Front Parameterization

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 183901, “Ensemble-Based Assisted History Matching With 4D-Seismic Fluid-Front Parameterization,” by Mario Trani, Konrad Wojnar, Arthur Moncorgé, and Philippe Berthet, Total, prepared for the 2017 SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 6–9 March. The paper has not been peer reviewed. An ensemble-based 4D-seismic history-matching case is presented in the complete paper. Seismic data are reparameterized as distance to a 4D anomaly front and assimilated with production data. This study shows that adding the 4D reparameterized seismic data in addition to the production data keeps a reasonable match with production data while constraining the overall gas distribution in the reservoir to the observed seismic data. Introduction The complex construction of a petro-elastic model renders the use of quantitative seismic data in history-matching work flows quite challenging. Several authors have investigated quantitative approaches for incorporating a large number of seismic data into history-matching work flows for production data. The work flows adopted in most of these studies require significant reduction of the uncertainty space or produce on a single history-matched model. In recent years, an algorithm that has gained increased popularity is the ensemble Kalman filter (EnKF). The EnKF and its derived algorithms are called ensemble methods, and their most notable characteristic is being computationally feasible for large systems and being relatively easy to implement. Several authors have investigated the effect of assimilating 3D- or 4D-seismic data with EnKF and have had to face the problem of building an accurate petroelastic model. To circumvent this problem, some authors have reparameterized 4D-seismic anomaly-front data into arrival times. Despite the advantage of eliminating seismic inversion, this method presents the disadvantage of at least doubling the simulation time for accurate ensemble arrival-time predictions. In the complete paper, the assisted history matching is performed on a large turbiditic field with the ensemble method to assimilate production and 4D-seismic data by use of the distance-to-front parameterization. The field is a large turbiditic body, with initial fluid pressure close to the bubblepoint. Oil production causes the pressure to fall below the bubblepoint in the very early life of the reservoir, leading to a widespread gas exsolution. The time-lapse change in gas saturation is considered the only factor responsible for the observed negative relative changes in seismic velocity seen over the entire reservoir. There is water injection occurring, but with a local effect, and it is therefore neglected. The innovation of this study is that the distance-to-front parameterization is applied to the gas phase, which can appear everywhere in the field, rather than coming from an injection source. Another innovation of this study is that the binarization of the simulated time-lapse anomaly is performed without use of a petroelastic model, which would be necessary to relate the measurements to fluid-property changes and to decide a threshold for binarizing observations and pressure. However, the effect of gas is so widespread and evident that the petroelastic model can be replaced by a clustering approach based on the gas-saturation change of the reservoir cells.