The visualization above illustrates the 3D/4D spatiotemporal integration of geologic structure, fracture stage events, microseismic events, wellbore trajectories, and well logs.
Growth in the oil and gas industry has enabled the collection and utilization of large data volumes. These enormous data records are available through governmental or commercial agencies. Potentially hundreds of thousands of wells contribute to this collection, providing valuable information about drilling and completion histories and production performance. These historical datasets available from a wide range of sources are, however, difficult to handle due to their sheer volume and different origins, making analysis difficult for interpreters.
One option that oil and gas industries are using to make data analysis easier and more reliable is 4D visualization. Advanced software with 4D visualization capabilities can facilitate the development of correlation between a large amount of temporal and geospatial data, and also make it easier for interpretation with the ability to visualize related data at its correct geospatial location. With the understanding of the best approach to handle the data, it is easy for asset teams to develop a sustainable oil field best practice.
Oil Field Datasets
A large amount of data is captured in the course of asset development. The various lithological, petrophysical, production, and engineering data provide analysts with valuable insight into the reservoir characteristics and production prospects. As more wells are drilled and data gathered, the historical datasets can be utilized to increase the precision in drilling and placement, identify faults and horizons, and predict the behavior of the reservoir based on employed oil field best practices.
However, the analysis of such a massive amount of data is not a simple task. Depending on the tools employed for data collection, the storage format can be different for each type of data. Some datasets may have missing values or might simply have been labeled differently which raises a serious concern about data quality control.
With such big data, it can also often be taxing for computers to process the data for 3D or 4D analysis while it may be equally difficult to paint the larger temporal and geospatial picture with 2D analysis. Data visualization and analysis software with 4D visualization ability provides drilling and reservoir engineers, geologists, and geophysicists the confidence of drawing accurate conclusions in temporal and geospatial analysis of subsurface data.
4D Visualization Provides Accuracy and Confidence
Understanding the various attributes and their changes in the reservoir over time provides deeper insight into the oil field and its subsurface characteristics. The ability to visualize these changes in 4D brings even more clarity and helps decision-makers in making a robust conclusion. The ability of 4D visualization and analysis tools such as CoViz 4D enables:
- Recasting of property or attribute models onto the wells
- Adding all relevant data types to identify interrelationships between attributes
- Filtering the data to make a visual interpretation of only the attributes of importance
Based on the historical data and its 4D visual analysis, asset teams can make predictions regarding the selection of target zone, well planning, and efficient production strategies.
Solution for Developing Oil Field Best Practice
CoViz 4D is an ideal platform for handling and visualizing all types of data necessary for analysts to understand the oil field and make a robust decision regarding the drilling, completion, and production prospect. Engineers and geoscientists can extract historically based data to predict the future performance of the reservoir. The tool also enables correlation analysis among different data spectrum which is useful in analyzing how a change in one attribute can affect other prospects in the well drilling and recovery process. Among team members, the software fosters collaboration through the integration of different disciplines. Asset teams can extract historically based oil field best practices to improve data quality and predict behavior.