Ekofisk 4D Seismic - Seismic History Matching Workflow

Abstract

This presentation outlines an integrated workflow that incorporates 4D seismic data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow benefits are also presented. A seismic monitoring programme has been established at Ekofisk with 4D seismic surveys that were acquired over the field in 1989, 1999, 2003, 2006 and 2008. Ekofisk 4D seismic data is becoming a quantitative tool for describing the spatial distribution of reservoir properties and compaction. The seismic monitoring data is used to optimize the Ekofisk waterflood by providing water movement insights and subsequently improving infill well placement. Reservoir depletion and water injection in Ekofisk lead to reservoir rock compaction and fluid substitution. These changes are revealed in space and time through 4D seismic differences. Inconsistencies between predicted 4D differences (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. This process is captured using the following workflow: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rockphysics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock physics forecasts, and (4) update the reservoir model to better match actual production/injection data and/or the 4D seismic response. The above-mentioned Seismic History Matching (SHM) workflow employs rock-physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. Parameterization techniques are then used to constrain and update the reservoir model. This workflow updates geological parameters in an optimization loop through minimization of a misfit function. It is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool using evolutionary algorithm. In summary, the Ekofisk 4D SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines to optimize well placement and reservoir management.

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

“…In this context, including 4D seismic data in the history matching, called Seismic History Matching (SHM), has the potential to improve matching quality, as it allows saturation maps to be matched, as well as matching production data. For instance, Tolstukhin et al (2012) applied the SHM to a portion of the Ekofisk field. They identified the eight most important attributes through traditional sensitivity analyses (one at a time), including fracture's attributes.…”

Seismic History Matching Using a Fast-Track Simulator to Seismic Proxy

“…The more constraints a history matching scheme includes, the less uncertain will its outputs be (Landa and Roland, 1997, Wang and Kovscek, 2002, Katterbauer et al, 2015. Compared with the well production historical data (oil/gas/water rate, bottom hole pressure) which has been mainly used for history matching, time-lapse seismic (or 4D seismic) has a better areal coverage of the reservoir, and is thus potential useful as a further constraint (Oliver and Yan, 2011, Katterbauer et al, 2015, Stephen et al, 2014, Tolstukhin et al, 2012. Combining 1D well production data as high resolution vertical constraint and 4D seismic data as spatial constraint, seismic history matching (SHM) has been proven to provide more reliable model realisations in past decades (Huang et al, 1998, Gosselin, et al, Jin et al, 2011.…”

Time-lapse walkaway vertical seismic profile monitoring for CO2 injection at the SACROC enhanced oil recovery field: A case study

“…Impedance contrasts and seismic response changes such as amplitude changes and tuning effects have been used to characterize CO 2 accumulations in thin layers, and velocity pushdown effects that are caused by slower propagation of seismic waves through the CO 2 saturated area have been identified (Arts et al, 2004a). Quantitative methods have also been proposed to directly deliver reservoir property changes such as pore pressure and fluid saturation by linking the rock-physics modeling, reservoir simulation, and 4D seismic response simulation (Landa and Kumar, 2011;Tolstukhin et al, 2012). However, these methods are conducted with poststack data or even 1D wave propagation, which focus on a local region and lose general information during the stacking process.…”