NEOS Tech Series: Predictive Analytics to Support Big Decisions

Geostatistical Predictive Analytics is a crucial part of the Multi-Measurement Interpretation (MMI) methodology. The term Predictive Analytics itself is a bit of a buzz term that can be used to describe any approach to data mining to predict trends or behaviors in the effort to identify risks and opportunities.

Among the various approaches to Geostatistical Predictive Analytics are Static Uncertainty Analysis and Correlative Predictive Analytics (CPA) – each deals with a multitude of input data where a toolkit of techniques is applied to produce useful outputs, such as sweet spot maps. At NEOS, we execute these two approaches to mathematically and objectively identify the most relevant geo-measurements that align with the most productive wells or with the location of known wells. In essence, it is this data that provides that key additional insight to the customer, allowing them to make the all-important big decisions.

But what specifically are Static Uncertainty Analysis and CPA? How do they differ and when does NEOS apply each? We’ll start with Uncertainty – stay tuned to Sweet Spots blog for more on CPA.

Quantitative Risk Analysis

We are each faced with uncertainty every day; by definition this means we encounter more than one possibility in many different situations. Who will win the World Cup? While uncertainty is everywhere, only those states of uncertainty where there are personal stakes involved include risk (such as a potential loss or catastrophe). Should I buy this stock? It is this risk that drives individual need to make an educated decision.

To better understand this concept, consider weather. Weather is uncertain. Each day quantitative risk analyses (aka – weather forecasts) are generated for you to make various decisions about how to approach your day. Quantitative risk analysis can be performed a couple of different ways. One source might predict the forecasted temperature for September 15th in Pittsburgh deterministically (with single-point estimates) while another might do so probabilistically (with a range of possible values). A deterministic outcome would give a single value, say 63F with no rain. A probabilistic outcome would give a distribution of temperatures, with a bell curve peaking at 63F and percentage probability (30%) of rain.

We know that the deterministic answer (no rain) is possible, but we understand that there is uncertainty in the forecast and we are accustomed to hearing terms such as “chance of rain” or “probability of precipitation”. While a deterministic answer is useful, a probabilistic answer is necessary to make an informed decision.

In this example, there is uncertainty in the estimate, but unless we have a personal stake and decision to make (we will be in Pittsburgh on September 15th and we need to decide whether to bring a raincoat) that uncertainty does not translate into a risk.

Geostatistical Uncertainty

Uncertainty exists in oil & gas exploration, where there are high stakes and [often] high risk, typically as a result of limited available data and information. This perfect storm scenario makes the uncertainty analysis necessary for operators to make those all too important decisions of where to explore, lease or drill.

“When data is sparse and uncertainty if high, that’s the best time to model things probabilistically.” Patrick Leach

NEOS takes both a deterministic and probabilistic approach to decision making in the presence of uncertainty, beginning with a single common base case conceptual model and ultimately creating a uncertainty assessment that addresses questions, intended for integrated team discussion, like what is the total volume of oil, what are the areas of interest for oil and what may contribute to uncertainty in the area.

As an example, the Oil in Place (STOIIP) model below defines how oil volume is calculated for a formation. While by no means the only important consideration, in place volume estimates and uncertainty models are the foundation of many decisions one has to make in the life of an oil or gas field. For each input parameter in STOIIP, we define an uncertainty model through a set of probability distributions or more complex spatial uncertainty models.  Through Monte Carlo Simulation, the input uncertainties are combined into a model of uncertainty for STOIIP.

Oil in Place Model

Oil in Place Model

Global uncertainty in STOIIP can be represented by its distribution, typically normalized per unit area (square miles in this case).

Global uncertainty in STOIIP (MMbbl/square mile)Local uncertainty can be represented through different maps, such as standard deviation maps, P10/P50/P90 maps or probability of being above a given cutoff.

Global uncertainty in STOIIP (MMbbl/square mile)

Local uncertainty can be represented through different maps, such as standard deviation maps, P10/P50/P90 maps or probability of being above a given cutoff.

Probability for STOIIP being above 50 MMbbl/square mile

Probability for STOIIP being above 50 MMbbl/square mile

Finally, it can be useful to look at the contributions of the different sources of uncertainty with tornado charts.

Contributions of the different sources of uncertainty to the global uncertaintyContributions of the different sources of uncertainty to the global uncertainty

Contributions of the different sources of uncertainty to the global uncertainty

As with our weather example, while a deterministic approach (STOIIP) is useful, including a probabilistic approach is necessary to make an informed decision, which can range from leasing an area, selling it, drilling exploration wells, designing data acquisition surveys, to development strategies.

This means that a good understanding of our clients’ objectives and in particular the decisions they are facing is crucial in order for NEOS to deliver information that helps make those all-too-important informed decisions.

If you would like to learn more about the NEOS approach the Predictive Analytics read this White Paper or see related Sweet Spots blog posts.

Putting Houston’s Size in Perspective

In our industry, it’s hard to avoid spending time in Houston or you might even be one of the lucky ones who calls it ‘home’.  Driving around the city, either as a visitor or as a local, you can’t argue that Houston is a massive metro area.  And it is continuing to grow at a rapid pace, thanks in part to a booming job market, specifically in the energy sector.

But really how big is Houston compared to other large cities? This is where a little perspective can be useful. Recently, Texas Monthly magazine featured an article that superimposed Houston’s city limits over other large metro areas. Take a look: our NEOS colleagues in the Bay Area don’t come close. Like I said, we already knew Houston was a huge city but these visuals really put things in perspective.

san francisco 8

Map showing the 88-miles of Houston’s Beltway 8 over the San Francisco Bay Area

And Houston is still growing. A third freeway loop, the appropriately named Grand Parkway, will soon join the well-traversed 610 and Beltway 8 loops, encircling this 600-square-mile city. I guess the saying stands true, ‘Everything is bigger in Texas.’

NEOS Tech Series: Using Magnetic Data to Locate Abandoned Wellbores

By: Maggie Baber, NEOS Geoscientist

Magnetic interpretation is an integral component of NEOS’s multi-measurement interpretation methodology (MMI). Magnetic data measures changes in a rock’s magnetic susceptibility, or the degree of magnetization in response to earth’s magnetic field. Rocks or materials with high magnetite content have a high magnetic susceptibility.

Magnetics, along with gravity and other MMI measurements, is used to better understand subsurface geology and can assist in mapping faults, folds, lithology changes, intrusions, and mineral deposits in the Earth’s upper crust. NEOS also uses magnetic data to observe changes in basement composition that may be linked to production in overlying shales.

But the application of magnetic data isn’t restricted to subsurface investigation: NEOS takes our magnetic interpretation one step further by interpreting surface and near-surface magnetics to find abandoned wellbores.

Abandoned wellbores, many of which are not logged in public records, pose a serious environmental threat: Wells that have not been properly sealed can leak methane to the surface and damage vegetation, contaminate water, and/or trigger explosions. The EPA estimates there are 1.2 million abandoned wellbores in the United States, many of which are decades old. Of those, 200,000 exist in Pennsylvania alone.

NEOS reduces the impact of abandoned wells on current drilling operations and supports environmentally sound operations by identifying, mapping, and characterizing these features for our clients. This method is especially useful in areas of high well density, where a wellbore may be forgotten in years since operation, as well as in densely forested areas, where wellbores may be hidden by trees.

To locate abandoned wells the NEOS methodology uses airborne acquired high resolution magnetic data. Via data processing, NEOS separates the magnetic signal into two products. The first is a low frequency ‘geologic magnetic’ grid or magnetic signal sourced from the geology alone. It is used to map subsurface geology and observe changes in basement lithology, but it is not useful in determining locations of objects on or near the surface.

The second product, the high frequency ‘cultural magnetic’ grid, is magnetic signature sourced solely from near surface objects. We use this grid to locate and categorize surface or near surface objects with high susceptibility, including abandoned wells.

Most magnetic anomalies we see on this ‘cultural’ grid are associated with known features, such as wells, pipelines, bridges, houses, and other mapped sources of magnetic susceptibility. We check each anomaly using mapped cultural data and satellite images and separate each anomaly into categories.

All remaining, unidentifiable point-sources we categorize as unknown magnetic material and potentially may be abandoned wells.

In a roughly 2000 square mile neoBASIN study of the Appalachian Basin in northwestern Pennsylvania, NEOS identified 9 untagged wells and 21 unknown sources, assumed to be abandoned wells or abandoned magnetic material, for our client.

The cultural grid is separated from the total magnetic intensity and then compared to mapped features, like wells and pipelines.

The cultural grid is separated from the total magnetic intensity and then compared to mapped features, like wells and pipelines.

Anomalies not corresponding to mapped features are checked using satellite imagery. Here are several examples of magnetic anomalies and corresponding Google EarthTM images.

Anomalies not corresponding to mapped features are checked using satellite imagery. Here are several examples of magnetic anomalies and corresponding Google EarthTM images.


Unlocking the Potential – NEOS in Upstream Technology


Andrew McBarnet explores NEOS’ innovative approach to MMI in the article “Silicon Valley Meets the Oil Patch” in Upstream Technology Magazine. Take a look!

The big idea driving NEOS is a rethinking of how geological and geophysical (G&G) data can be harnessed to provide rapid, cost effective answers for E&P companies under pressure to make decisions on where to lease and where to drill.

World Cup Fever: What Type of Fan Are You?

I will not deny that we have more than a few avid World Cup fans in our office. Water Cooler gossip has definitely reached a frenzied pitch. It’s even more fun with the multitude of nationalities that are represented (or simply just fanatically followed) therefore bringing some new [friendly] rivalries right to our [office] doors. As the games, and USA (!!!), move onto the next round, and things become more tense, its as good a time as any to step back, look around and truly see what kind of World Cup fans surround us. The NEOS fan type has definitely been represented. Where do you fit in?

The NEOS Soccer Fan

The NEOS Soccer Fan


NEOS to Present at SEG D&P Forum

SEG D&P ForumFor the first time in more than 20 years, the SEG Development & Production Forum will focus on Reservoir Characterization and Monitoring. Recognizing the vastly different geophysical space that we operate in today versus 20+ years ago, the forum will examine the role that advanced geophysical technologies play in today’s oil & gas landscape.

NEOS will participate this year with two speakers, and one session chair – David Alumbaugh, from of our Pleasanton, CA office. Here’s the complete Program.

>>Wednesday, 2 July – Session 5: Unconventionals @ 11:00AM

“A probabilistic approach to in-place volume estimation with application to an unconventional reservoir”, Emmanuel Schnetzler

[Keep Learning! The NEOS approach to Predictive Analytics]

>>Wednesday, 2 July – Session 6: Nonseismics @ 2:35PM

“Non-seismic characterization of basement architecture and composition and its relationship to hydrocarbon production”, Maria (Sole) Velasco

[Keep Learning! NEOS can help you understand what's in your basement.]

Discover more! Visit the NEOS website.

World Cup Fever: Remembering Paul the Octopus

It’s official, Soccer (Football/Fútbol/Calcio) Fever is in full swing – at least it is here at NEOS.  It’s the World Cup!!!  Everyone, whether an avid fan or simply a every-four-years World Cup fan, is engaged, excited and [beyond] enthusiastic. Google has joined the party and has released a new Google Doodle celebrating the world cup each day since the tournament began.  Today’s Doodle is particularly cheeky as it recalls the 2010 South Africa World Cup, where Paul the Octopus accurately predicted the final match (as well as a few others). Unfortunately, Paul died later in 2010, so he now [according to Google] is making his predictions from heaven.

GoogleDoodleGO USA!


NEOS Tech Series: Multi-Physics Reveals Basement Secrets

The NEOS MMI methodology, specifically with the use of potential methods, is greatly suited to provide understanding of the structural complexity of a basin, at the basement level.

While gravity and magnetic methods use different rock properties (density vs. susceptibility), these methods are diagnostic and when taken together can eliminate many geologic alternative scenarios therefore putting constraints on geologic models.

In oil and gas exploration, the understanding of depth and structural configuration of basement plays a significant role. The depth-to-basement estimates from gravity and magnetic data lead to an estimate of thickness, depth of burial and thermal maturity of a horizon of interest. These parameters are very important in modeling and determination of hydrocarbon generation.  In addition, the understanding of basement structure leads to the understanding of global plate tectonics. Many of the world’s rift basins and spreading centers are recognized based on the magnetic data, two examples are the Red Sea Rift basin and Mid-Atlantic Ridge.

mag data along mid atlantic ridge

Magnetic data along the Mid-Atlantic Ridge

Magnetic data also typically highlights the changes in composition of the basement rock that would lead to differential thermal conductivity and thermal maturity of the source rock. In some recently surveyed areas, NEOS Geoscientists were able to relate enhanced production in shales based on its thermal maturity due to differential basement lithology in the area.

Click here to learn more on NEOS’ Basement Applications of Multi-Measurement Interpretation. For a better understanding of MMI methods throughout the entire geologic column watch the narrated slideshow.

NEOS Tech Series: Gravity & Magnetics

A multi-physics approach to geoscience allows NEOS to simultaneously interpret several possible geophysical measurements in order to uncover deeper insights into regional prospectivity and well productivity. While there may be potential for the inclusion of a number of measurements within an interpretation, each is equally unique and beneficial on its own accord.


NEOS incorporates gravity and magnetics in all neoBASIN programs, as these measurements are fundamental to the company’s multi-measurement interpretation (MMI) methodology. Gravity and magnetic data is acquired, along with other MMI measurements, using airborne systems simultaneously in a fast, efficient and non-invasive way, which reduces the turnaround time for the data acquisition, integration and interpretation. The data collected gives NEOS insight into the subsurface over large regional, basin-scale areas or at the more detailed, prospect level.

Gravity and magnetic methods are commonly referred to as potential field methods because the measurements involve a function of the potential of the observed field of force (gravity or magnetic). The data is used to help delineate geologic features in the Earth’s upper crust related to lithologic changes caused by natural hazards (faults, volcanoes), natural resources (gas, minerals) and tectonic events such as the formation of mountain belts.

These measurements take advantage of the variation in different lithologic characteristics of subsurface rock in a given area and can quickly and easily map location, extent, depth and structure of sedimentary basins. In addition, these methods can quickly identify faults, mineral deposits, igneous intrusive and extrusive, and depth-to-crystalline basement.

While typically grouped together, it’s still important to understand how each functions, so let’s look at gravity and magnetics individually.

Left image is Gravity Map and right image is Magnetic Map of the Gulf of Mexico.

[Left] gravity image and [right] magnetic image of the Gulf of Mexico.


Gravity measures very small variations (anomalies) of the Earth’s gravitational field that are caused by lateral variations in the density of Earth material.

Specifically, gravity data is useful whenever the formation(s) of interest have densities that are different from surrounding formations. There is a wide range of densities within the Earth’s crust, from essentially zero density of air-filled voids in near-surface formations to the highest densities related to iron/magnesium-rich basement rocks and metallic ores. Because of the wide range of densities within all rock types, geoscientists interpret measurements and draw conclusions regarding the distribution of underground rock types that may be commonly favorable to trapping oil or gas. A typical gravity survey will map sedimentary basin configuration, structure and thickness, and faults. It will also map basement configuration where basement rocks are of higher density than sedimentary section, and salt bodies and distribution.

For example, salt has a very low density of 2.15. The identification of a salt dome would help to locate possible oil and gas reservoirs as many oil and gas deposits are located along the structures caused by salt movement within the sedimentary basin. A typical example of this is the large oil and gas reservoirs along the Gulf of Mexico.


The magnetic field of the Earth is generated by electrical currents in the liquid outer core. Rocks that have high magnetite content typically have the property ideal for magnetization, also known as susceptibility. Magnetic data measures the minute changes in a rock’s magnetic response caused by contrast in the susceptibility after an external magnetic field is removed. Field data responds to tectonic fabric, due to the redistribution of magnetite rich rocks and is typically used in mapping basement structure or the thickness of sedimentary section.

Faults and fractures are easily detected by magnetic method due to mineralization along fault planes. This method is widely used in the exploration and distribution of mineral deposits containing magnetite.   In oil and gas exploration, magnetic method is used in determination of depth-to-basement, fault detection and distribution, basement rock type leading to recognition of differential thermal conductivity or the thermal maturity of a basin.  

In the field of regional exploration, magnetic data is useful in delineating crystalline basement structures and tectonic elements as rift basins and basement involved tectonic elements. It also can delineate the distribution of volcanic material, both intrusive and extrusive. Recently, NEOS has used magnetic data to look for changes in basement lithology and successfully related it to enhanced production in overlying shales.

Click here for more detail on gravity and magnetic measurements.

No Permits Up Here (#3 in a Series)


In many parts of the world, but especially in the United States, airborne geophysical data acquisition has one key advantage over ground-based geophysical acquisition: no permits are required! This has many operational benefits, not the least of which is cycle time reduction. With airborne geophysics, landowners are taken out of the equation as they no longer have the ability to string things along by refusing to grant access or by negotiating for better access terms.

With airborne geophysical methods, oil & gas companies are able to obtain subsurface images more quickly, helping to inform their critical acreage acquisition (or disposition) and drill-well location decisions in a more timely way. Moreover, airborne methods don’t force one to interpret around ‘holes’ in ground-based geophysical data simply because a recalcitrant stakeholder refused to grant access to their land.

There are other benefits as well. Imagine a fellow operator has announced a new discovery (or a series of highly productive wells) on their acreage. You’d sure love to ‘see beneath the surface’ to get a sense of what geological and geophysical factors drove that discovery, which might position you to look for analogs on your owned acreage or offset acreage you could lease or farm into. What are the odds of this operator granting you the rights to acquire seismic over the area of interest? Pretty close to zero. But with airborne geophysical methods, you can acquire data over all areas of interest to you, whether you currently have these areas under lease or not.

Just as satellites give governments the ability to gather intelligence outside of their territorial borders, so too do airborne geophysical methods provide the savvy E&P operator with the ability to gather subsurface intelligence outside of their lease boundary.

Shorter cycle times. More information. Over broader areas of investigation. All with fewer headaches and boots on the ground. What’s not to like?

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