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?
We continue our Lebanon neoBASIN project series, this time, taking a look at some large, basement-driven structural highs that have been identified in the Triassic and Paleozoic intervals in the survey area.
Our highly constrained, multi-measurement methodology for developing these types of 2-D cross-sectional models is covered elsewhere (you might review our MMI 101 narrated slideshow (click here) or read our 2014 Marcellus case study from URTeC (click here) if you want a richer refresher.
In a nutshell, we develop these models by evaluating the response of actual acquired multi-physics geo-data (in this case, gravity, magnetic and EM resistivity measurements), making certain assumptions about the thickness and physical properties (density, magnetic susceptibility, and resistivity) of key intervals, and iterating until the model converges with all acquired data and with any other constraints we might have, such as outcrop, well or seismic (which we didn’t have in this case) information.
In the image above, you’ll note that we determined that there were some topographic highs in the basement (the red interval) and that these basement-involved features affected the younger intervals deposited above them – in particular, the Paleozoic (green) and Triassic (purple) horizons. Faults were mapped with other datasets, in particular magnetic but also EM. To learn more about the importance of basement topography, faulting and composition in hydrocarbon exploration, read our article in E&P.
These fault-bounded structural highs were seen in other parts of the survey area as well. In many cases, interpretations of the acquired Grav-Mag and EM datasets suggest that these features continue ‘into and out of the page’, thereby creating elongated anticlinal structures that could be intriguing exploration targets.
Now if only NEOS had some seismic imaging capabilities to further delineate the vertical and lateral extent of these anticlinal structures (???), but I digress…
In Part 1 of this series, we described the presence of oil seeps on the surface, in many cases, concentrated along faults and juxtaposed stratigraphic intervals outcropping on the surface. If the seeps were generated from Paleozoic or Triassic source rocks, what are the odds that some of the hydrocarbons became trapped in structures like these as they migrated towards the surface?
In an upcoming post, we’ll share some intra-horizon resistivity anomalies that indicate an increase in interval resistivity in structures similar to the ones highlighted here as one moves up the geologic column.