+1 510-522-0700

Well Location Optimization with Visual and Quantitative Analysis

An onshore team was tasked with producing a list of potential well landing locations based on attributes in a geological model. Four pad locations in the field were each given a set of 12 wells arranged in a radial pattern; the model’s potential target area covered a Z range of about 1650 ft. The well landing locations needed to be optimized to hit the model’s high Net to Gross (NTG) and Gamma Ray (GRD) areas because these two attributes had high confidence amongst the team. Finally, the potential well path landing options had to be easily visually ranked.

Figure 1. Map view of well geometry and geologic model. Model Data provided by RMOTC and US Department of Energy.

The team recognized that the CoViz 4D visualization environment has tools for sampling attributes between overlapping files of varying geometry and knew they could use them to accomplish the task. While the attribute sampling between overlapping files could have been done interactively in the Viewer, in this case a more automated approach was needed; the 12 wells for each pad had to be shifted in the Z direction over the full model range, and NETG and GRD in the different model layers sampled against each of the well points. After completing this iterative shifting and sampling, the final step to produce the actual well rankings would be to take the sampled NETG and GRD values and compute both a sum and average over the length of each potential well path.

Several command line modules underlying the CoViz 4D visualization environment were utilized in a customized script to complete the NETG and GRD sampling, well path Z-shifting, and attribute summing / averaging. This easily automated computation ran in a matter of minutes; the outcome of shifting each of the 48 well paths in Z was 6624 possible landing locations.

The results of the well path / geologic model intersections were loaded to the CoViz 4D visualization environment. The NTG and GRD values in the resulting well path file were easily visually filtered to show areas of both high NTG and GRD; this made it possible to filter the 6624 well options down to a handful of optimal landing locations.

Figure 2. Filtered results showing well landing locations having average NTG > 0.6 and average GRD > 80. Model Data provided by RMOTC and US Department of Energy.

This example shows the extensibility of the CoViz 4D environment; command line access to powerful underlying modules enabled the team to quickly and easily automate a workflow for calculating a large number of potential well landing options. Just as rapidly, by simply visually filtering the results in the interactive Viewer, the team was able to narrow down the landing options to a handful of optimal locations.

See the CoViz 4D page for more information.


Infill Drilling Placement using Conditional Seismic Attribute Filtering

Infill Drilling Placement using Conditional Seismic Attribute Filtering

Infill drilling aims to tap previously undrained reserves in a mature hydrocarbon field. By definition, infilling happens later in field life and hence involves the analysis of greater volumes and a greater diversity of a priori data. Furthermore, the economics of infill drilling can be marginal. Therefore, a rigorous quantitative decision-making process is necessary to justify the economic risk required to implement drilling and production plans.

InSAR Data Analysis of the Belridge Field

InSAR Data Analysis of the Belridge Field

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique that produces a very detailed picture of the shape of the ground surface. The data returned are so precise that they can reveal changes in the elevation of the earth down to the level of millimeters per day, enabling very small changes in surface elevations to be quickly discovered, which can be critical to land management.


Share on Social Media