Xue, Yang (Shell International Exploration and Production, Inc.) | Araujo, Mariela (Shell International Exploration and Production, Inc.) | Lopez, Jorge (Shell International Exploration and Production, Inc.)
4D seismic is widely deployed in offshore operations to monitor improved oil recovery methods including water flooding, yet its value for enhanced well and reservoir management (WRM) is not fully realized due to the long cycle times required for quantitative 4D seismic data assimilation into dynamic reservoir models. To shorten the cycle, we designed a simple inversion workflow to estimate reservoir property changes directly from the 4D attribute maps using Machine Learning methods. Thousands of training data sets are generated by Monte Carlo sampling from the rock physics model within reasonable ranges of the relevant parameters. Machine Learning methods are then applied to build the relationship between the rock property changes and the 4D attributes, and the learnings are used to estimate the rock property changes given the 4D attribute maps. The estimated reservoir property changes (e.g. water saturation changes
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 204B (Anaheim Convention Center)
Presentation Type: Oral
Continuous seismic monitoring was deployed at Pad 31 in the Peace River area to provide temporal and areal information on steam injection and production induced changes in the reservoir. In all there are over 700 daily seismic volumes. To digest this wealth of information we developed a method to invert 4D amplitude and time-shift attributes into rock property changes that are related to the production effects through the rock physics model. Visualization of "daily" changes, captured by the seismic data, enables interpretation of the complex reservoir processes – pressure, temperature, and saturation changes.
Presentation Date: Monday, October 17, 2016
Start Time: 1:25:00 PM
Location: Lobby D/C
Presentation Type: POSTER
Barker, Timothy (Shell International Exploration and Production Inc.) | Xue, Yang (Shell International Exploration and Production Inc.) | Przybysz-Jarnut, Justyna (Shell Global Solutions International B.V.)
Continuous seismic monitoring has been deployed at Peace River Pad 31 to provide temporal and areal insight to steam injection and production induced changes in the reservoir. To quantify the changes, a rock physics model has been defined that incorporates geologic variability and expected production effects. Interpretation of the complex seismic response requires a systematic approach to gain insight into and quantification of reservoir changes from thermal EOR (e.g. pressure, temperature, and steam thickness) to facilitate communication with surveillance engineers and influence operating decisions.
The Peace River bitumen deposits were discovered in 1951 in northwestern Alberta, Canada. They contain billions of barrels of heavy oil with 7-10 API and are under production employing steam injection.
The reservoir in the Peace River area is the Bluesky formation. It is lower Cretaceous in age and the siliciclastic sediments were deposited in a marginal marine setting. In the area of this study, the reservoir, a high porosity and unconsolidated sand, averages around 25 m in thickness and is at a depth of about 550 m below the surface. An unconformity separates the reservoir from the underlying Paleozoic carbonates. The top seal is formed by the marine Wilrich shale.
Operations at Pad 31 began in 2001 and there have been 7 cycles of steam stimulation. In 2014, the pad was instrumented with CGG’s SeisMovie® system to continuously monitor the injection and production once additional steam injection wells were drilled to increase oil recovery from the pad.
Rock physics model
In order to translate the reservoir changes into the seismic response to use in forward modeling (generating synthetic seismic) of reservoir models and scenarios, or in seismic inversion, it is first necessary to describe mathematically the relationship between reservoir properties (porosity, fluid types, saturations, pressure, and temperature) and elastic properties (Vp, Vs, and density). There has been a significant amount of work to describe the rock physics and we are building upon that foundation (Maron et al., 2005; Cabolova et al., 2014).
Joint inversion of PP and PS reflection data has been hindered by the difficult task of registration or correlation of PP and PS events. It can perhaps be achieved by registering the events during inversion but the resulting algorithm is generally computationally intensive. In this paper, we propose a stochastic inversion of PP and PS data which have been registered to the same PP time scale using a new interval velocity analysis technique. The prestack PP and PS wave joint stochastic inversion is achieved by using the PP and PS wave angle gathers using a very fast simulated annealing (VFSA) algorithm. The objective function attempts to match both PP and PS data; the starting models are drawn from fractional Gaussian distribution constructed from interpolated well logs. The proposed method has been applied to synthetic and real data; the inverted results from synthetic data inversion compare very well with model data, and inverted results for real data inversion are consistent with seismic data and log data. These also show that the proposed method has a higher accuracy for estimating rock physics parameters while it circumvents the horizon registration problem in the data interpretation. We also estimate uncertainty in our estimated results from multiple VFSA derived models.