Click here to continue reading this article on the Value of Information that we co-authored with Palantir and which was published in OilVoice on the 1st of September. Palantir used their economic modeling and asset optimization software to determine the economics of Vaca Muerta shale development under four different scenarios, in which the quantity and quality of subsurface G&G information varied.
The conclusion: investing $1,000 per sqkm to acquire and interpret multi-physics data in an attempt to highgrade acreage and identify sweet spots has a 10x ROI and roughly doubles the resource additions from the 30 well program.
This transaction involves a group of about 25 Denver-based folks who originally started as AXIS Geophysics and which ION acquired back in 2002. This team commercialized the technologies and workflows for anisotropic and azimuthal processing, which ultimately found great utility in fracture detection and sweet spot imaging for hard-rock and unconventional source-rock reservoirs.
More recently, the Denver office has incorporated many of GXT’s depth migration and tomographic imaging techniques into its workflows, positioning the entity as an industry leader in onshore depth imaging for complex fold- and thrust-belt geologic regimes, as well as pre-salt plays like those found in Kazakhstan and in the onshore basins along the South Atlantic Margin, including those in Angola, Brazil and Gabon.
As our loyal Sweet Spot readers know, NEOS has focused on non-seismic imaging methods since our launch in 2011. What you may not know is that we have long coveted having an in-house seismic capability, and this acquisition now provides us with the ability to offer a true multi-physics imaging solution to our customers.
Though this group – which will be known moving forward as the NEOS Seismic Imaging Group (SIG) – will continue to offer stand-alone data processing and imaging services, we are also excited about how we can extract maximum value for our customers by combining seismic and non-seismic measurements, attributes and methodologies.
One of the first obvious areas we’ll be working on is the incorporation of seismic attributes at the reservoir interval (e.g., rock brittleness, fracture density, fracture orientation) into our Predictive Analytics methods. But of course there are many others, including the ability to undertake true multi-physics inversions.
Check back over the months ahead to learn more about this addition to the NEOS family.
NEOS identified four distinct play types in the 6,000 sqkm area assessed on the Lebanon neoBASIN project:
A Triassic interval (BLUE) which is geologically similar to the gas and condensate fields of Syria’s Palmyride Belt to the northeast of the survey area;
A deeper (and potentially more gas prone) Paleozoic interval (PURPLE) also analogous to the Palmyride fields;
A Triassic interval in the near-offshore (BRICK RED) which is likely to be gas prone;
A Jurassic interval (YELLOW) which is geologically similar to the oil & gas fields producing south of Lebanon.
The locations on the image above are intentionally disguisedin terms of their geo-spatial locations and size (some of the areas shown above are depicted as being smaller compared to those actually assessed as prospective); actual locations of these highgraded play types would, of course, be revealed to licensees of the Lebanon neoBASIN Knowledge Library.
It is interesting to note that the actual interpretation suggests that several areas are prospective for multiple play types, meaning we have ‘stacked plays’ that should reduce the overall exploration risk in these locations given the possibility of multiple targets.
On the Lebanon neoBASIN regional reconnaissance project, we acquired EM resistivity data from roughly 45 ground-based magnetotelluric (MT) stations that were deployed throughout the survey area. Challenging topography and dynamic geo-political conditions on the ground didn’t always let us deploy the stations where we wanted to, but we did get some interesting results nonetheless.
The MT method relies on three primary sources of electromagnetic (EM) energy, all with different frequency ranges and, therefore, depths of penetration. High-frequency signals originate from lightning, in particular ongoing equatorial lightning strikes; intermediate frequency signals come from ionospheric resonances; and low frequency signals are generated by variations in the solar wind.
Driven by advances in sensors, DP and modeling methods, can computing power, MT has become one of the most important tools in deep Earth imaging with a capability to image to subsurface depths of 10,000 meters or more.
For hydrocarbon exploration, MT is mainly used as a complement to seismic imaging. This is especially true for scenarios that can be problematic for seismic, such as sub-basalt and sub-salt plays. In addition, while seismic is able to image subsurface structure, it cannot detect changes in resistivity associated with hydrocarbons and hydrocarbon-bearing formations.
By measuring both electric and magnetic responses simultaneously and processing the data using statistically rigorous mathematics, MT allows resistivity variations to be mapped with depth in the subsurface. Under the right geologic conditions, MT can differentiate between structures bearing hydrocarbons and those that do not.
In the image above, we analyzed the MT data in several different ways, initially in an unconstrained fashion and then in a constrained inversion where we also considered the measurements from, and structrual models being generated using, airborne-acquired gravity and magnetic data.
The multi-measurement integration and interpretation yielded very good results. In the 2-D constrained EM resistivity section shown, you’ll note several interesting features:
First, the fairly clean resistivity delineations among the key stratigraphic horizons;
Second, the insight EM resistivity information can bring to fault mapping, including the displacement along faults;
Lastly, the interesting intra-horizon resistivity increase as one moves up-section within the Cretaceous.
The scale here is logarithmic, so this is roughly a 10x increase in resistivity taking place within a 500-800 meter thick interval. Equally curious is that it’s taking place along and adjacent to a deeply penetrating fault. Simply an aberration? Noise in the data? A change in lithology? Or a change in fluid type at the highs abutting the fault?