Updated: NEOS Narrated Slideshow

Well, after a year in the spotlight, it came to be time to retire our original narrated slideshow and to replace it with a newer model. Below you’ll find the latest edition, updated to reflect the latest round of projects we’ve been undertaking for our clients in conventional and unconventional basins around the world.

Click here or on the image below to start the slideshow, which will open in a new window.

Geostatistics Applied: Ventura Basin Case Study

NEOS’s multi-measurement methodology provides a variety of tools to help the explorationist interpret the subsurface with greater accuracy and confidence. One of these tools, embedded within our NeoSphere software platform, applies a proprietary statistical technique to all acquired geo-datasets and the attributes calculated therefrom (e.g., Bouger gravity, reduced-to-pole magnetic data, microseepage indicators) to mathematically determine the measurements and attributes that best correlate with known fields (or high producing wells) in a designated area of investigation.

Once these ‘correlative anomalies’ are determined (from among several dozen possible variables, the importance of which vary from basin to basin), the software applies pattern recognition algorithms to identify the same set of correlative anomalies in areas without well control. In essence, the software is searching for undiscovered fields (or well locations) that share the same set of ‘geo-anomalies’ as the discoveries and best producing wells in an area of interest.

The methodology is applicable in both conventional and unconventional plays, and can be used to ‘hunt’ for a variety of geological and reservoir attributes, including hydrocarbon accumlations (producing fields), reservoir sweet spots (high-production well locations), and fracture swarms.

In the image above, the geologic objective function was set to ‘probability of a hydrocarbon accumulation’ in the Pico formation of California’s Ventura Basin. The hottest colors correspond to the areas that are projected to be most conducive to liquid hydrocarbon accumulations. The black dots correspond to wells (or groups of wells) that have been drilled into, and produce from, the Pico. You will note that there is a very good correlation between the predicted hot spots and the well locations.

Even in this relatively shallow (and therefore highly drilled) geologic horizon, there are still several indicated hot spots that have yet to be drilled. The results become even more interesting at depth, where the number of well penetrations into the geostatistically identified anomalies is even smaller and, therefore, the corresponding exploration potential is even higher.

As with any measurement or tool, the interpreter doesn’t blindly follow the output, but instead uses the insights provided to de-risk previous exploration concepts and to identify possible new leads worthy of further study. Nonetheless, the methodology shows great potential for adding new life into this old basin, and for optimizing the deployment of human and financial capital to the most promising exploration and development opportunities in any operator’s portfolio.

NeoSphere Enables New Generation of 3-D Subsurface Interpreters

The NeoSphere – the result of ongoing research and development and ‘live fire’ project testing by the geoscientists at NEOS GeoSolutions – is designed to become the next generation platform for collaboration and 3-D subsurface interpretation within the global geoscience community.

The NeoSphere initiative was launched in 2009 based upon the premise that oil & gas and minerals’ explorationists stand to benefit if they can find more efficient and insightful ways to leverage the vast volumes of geophysical, geochemical, geological and well-level data that have been collected over the decades by the natural resource industries.

To imagine what the NeoSphere can do, we must imagine a new world of exploration.

  • Picture a future in which a geoscientist can seamlessly access all relevant data available for a target region, including data accessible in the public domain, available for license, or potentially accessible in private company archives.
  • View this geoscientist reaching beyond her desktop to access the global, distributed computing power made possible by ‘the cloud.’
  • See her tapping into the collective knowledge of geoscientists throughout the world to support a project, no matter where these geoscientists live or for whom they work – professors in Russian universities; data processing grad students in India; and retired interpreters living in the mountains of Colorado.

This is the future of geoscience that we imagine, and the world that the NeoSphere aims to enable. At present, the NeoSphere is an in-house tool used by NEOS geoscientists to:

  • Identify and aggregate public domain information;
  • Ingest and QC geophysical datasets that NEOS airborne assets have acquired in the field;
  • Support data processing workflows of the individual G&G datasets by enabling both 3rd party and in-house software to be used to convert raw, multi-measurement geophysical data into interpretation-ready formats;
  • Enable the interpretation process of individual or multiple G&G datasets in an iterative manner by enabling workflows that incorporate the sequential inputs of several geoscientists both inside and outside NEOS and client firewalls; and
  • Validate the packaging of final deliverables to ensure all of the work products are properly categorized, that all project obligations have been fulfilled, and that no unlicensed material has been included in the final package.

These tools are already being made available to natural resource explorers and, in collaboration with our industry and technology partners, we’re adding new tools and new features to the NeoSphere each and every day. If you think we’re dreaming big, you might be right.

But we’re not the only ones who see the possibilities for meta-scale data search and collaborative interpretation; Tony Hey, the VP of External R&D at Microsoft imagines similar possibilities across several data-intensive industries, views that are outlined in a November 2010 article in Harvard Business Review entitled, The Next Scientific Revolution.

We feel like we’re in good company by dreaming big. Stay tuned as there is much more to come…