Improved volumes estimate by incorporating drill stem test data

Overview

Multi-billion dollar decisions are often made with very little data, and the uncertainties associated with these decisions are usually anchored to a single base case and/or a small set of scenarios. The key to reducing the risks associated with this approach is to create as large and diverse a set of subsurface models as possible which consistently honor all the available static and dynamic data.

During the exploration and field development phases, well test data are often gathered and used to assess a field’s potential. However, these data are not transferred to the reservoir model in a consistent manner. Therefore, valuable information about reservoir properties can be overlooked in the assessment.

Solution

Using ResX, dynamic data including drill stem test (DST) build-up pressure derivatives together with static data inputs are used to condition the models. The solution combines these data, the subsurface know-how of the asset team, reservoir physics, and machine learning algorithms to generate an ensemble of reservoir models. The results from this full suite of models provide a more reliable view of subsurface uncertainties, thus reducing the risk of the chosen development strategy.

Median of the difference in sand thickness for the initial 200 ensemble members (top) and the updated set conditioned to DST data (bottom). Red/blue colors indicate an increase/decrease in the sand thickness. Wells with DST data are labeled in black. (SPE 181352, Figure 12)

Solution highlights include:

• Static models were simultaneously matched with dynamic DST data from four tests.
• Large unphysical updates during dynamic data conditioning were avoided through small and local geostatistically consistent updates to many model variables, while ensuring that key subsurface uncertainties were captured and propagated throughout the entire reservoir modeling and data conditioning process.
• By including DST information in the reservoir modeling process, clear spatial trends in the sand thickness and permeability were revealed which improved the initial volume estimates.
• The solution enabled frequent model updates as new data arrived, and an automated workflow assisted in analyzing new results to support further reservoir management decisions.

Result

• Streamlined data integration and reservoir modeling
• Improved subsurface understanding of permeability and thickness by consistently using all available data (static and dynamic)
• More time for team to analyze results and focus on field planning

References: Sætrom, J., et al., 2016. Consistent Integration of Drill-Stem Test Data into Reservoir Models on a Giant Field Offshore Norway. Paper presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE, September 2016. Paper Number: SPE-181352-MS