Geologic structures, subsurface conditions, and reservoir production are described by a combination of diverse datasets generated or acquired throughout the reservoir lifecycle. Over time, these datasets essentially depict the reservoir evolution. Unfortunately, data diversity, proprietary formats, different timesteps, and discipline-specific software used to manage and analyze these datasets inherently limit their value. Individually, datasets tell a portion of the reservoir story. However, when combined and visualized they can reveal interrelationships of subsurface data. Both static and temporal data analysis is needed to fully understand reservoir performance.
The Value of 3D Visualization and Analysis
Integrating datasets from geologists, geophysicists, reservoir engineers, and drilling engineers—and visualizing them in 3D—enhances the inherent value of the data. Each member of the asset team can explore and evaluate their data in the context of other subsurface data. By integrating and visualizing diverse data such as seismic volumes and attributes, reservoir simulations, well events, borehole feeds, production tests, and fluid production, multi-disciplinary teams obtain a more detailed understanding of reservoir potential and performance. A few representative examples help illustrate the value of data integration visualization and analysis.
Data integration, visualization, and analysis allow asset teams to:
- model fluid contacts, diapirs, salt domes, and complexly faulted structures in 3D;
- create petroelastic models from rock physics parameters using Gassmann substitution;
- visualize property distributions to represent strata depositional history in faulted reservoirs;
- explore wellpath options using geologic, geophysical, and reservoir data to develop a drilling plan that minimizes cost and risk while maximizing recovery; and
- calculate overburden displacement, strain and seismic time-shifts due to reservoir thickness changes.
The ability to simultaneously view and interrogate relevant datasets that characterize subsurface structures and conditions in 3D enables asset teams to collaboratively reach decisions to maximize hydrocarbon recovery.
Challenges of Temporal Data Analysis
A 3D visualization of subsurface attributes depicts a reservoir (or model) at a specific point in time. While this static view provides valuable insight regarding reservoir attributes, it doesn’t fully capture the evolving reservoir conditions. Given the lifecycle of a reservoir, what’
However, the diversity of datasets and data formats presents a problem. Timesteps in datasets, such as reservoir simulation models, will not necessarily match the same times as those in sequential seismic surveys. For example, reservoir management workflows require integration of data that has been generated or captured at different points in time. To match these times requires considerable effort, manipulating and massaging individual data sets to ensure they all present correctly. When a limited number of time points are used (the result of the painstaking timestep manipulation across datasets), many details regarding reservoir changes may be missed. The inability to match timesteps across data hinders the full understanding of changing reservoir conditions.
The Value of Temporal Data Analysis
When temporal data can be automatically assimilated and visualized to present changing reservoir conditions over time (including those that have occurred and those predicted alongside newly acquired observed data), asset teams gain a clearer understanding of reservoir dynamics.
Combining and synchronizing static and temporal data (the 4th dimension)—geology, simulation models, well integrity, annulus recordings, logs, production, and seismic surveys—and viewing it over time gives asset teams the ability to:
- compare seismic response over time with a dynamic gas saturation model and production data;
- cross-correlate time-sequenced seismic data with reservoir models to track fluid migration and identify under- or over-produced locations;
- conduct spatiotemporal comparisons between fluid injection volumes and movement in the seismic signature;
- quantify water saturation changes over time and evaluate their effect on seismic amplitude;
- simultaneously modify and compare a range of different properties or time steps from a single simulation run to arrive at a more accurate model; and
- evaluate overburden deformation and time-shifts, based on reservoir compaction data.
Temporal data analysis facilitates the exploration of potentially complex data relationships, helping to determine cause and effect. Incorporating temporal data into the analysis brings added insight—a more granular analysis to support fully-informed planning and operational decisions.
CoViz 4D Excels at Temporal Data Analysis
CoViz 4D is a powerful software solution that easily integrates diverse spatial and temporal datasets. Its visualization capabilities enable members of the asset team to simultaneously view subsurface structures and conditions in 3D and analyze the data relationships that affect reservoir performance.
CoViz 4D facilitates an even greater understanding of reservoir behavior through temporal data analysis (4D) that shows how past development decisions have impacted reservoir performance and how proposed strategies can affect future performance.
CoViz 4D facilitates an even greater understanding of reservoir behavior through temporal data analysis (4D) that shows how past development decisions have impacted reservoir performance and how proposed strategies can affect future performance. CoViz 4D allows asset teams to realize the true value of 4D seismic data by providing insight into dynamic reservoir behavior at a temporal frequency not easily or economically achieved by typical streamer-acquired 4D seismic data. Fully integrating and analyzing time-series data alongside relevant subsurface data such as static geologic models, dynamic flow models, well data and logs, completion locations, and fluid rates lead to a better understanding of data correlations and optimized reservoir modeling decisions.