Geoscience-Based Artificial Intelligence for the Subsurface
Drilling & Measurement | Formation Evaluation | Reservoir
70% Savings on Formation Evaluation
20% Savings on Drilling & Measurement
50% Savings on GeoModeling & Reservoir
Over 125+ Wells Serviced In Land and Offshore
No Tools in Well. No Field Personnel.

Who We Are

Formed in 2012, Quantico Energy Solutions is an artificial intelligence company based in Houston, Texas. The company’s focus is on providing the oil and gas industry with data-driven solutions that lower costs and improve quality across the main areas of subsurface-related oilfield services. Today,
our solutions are being adopted by the largest oil and service companies around the world.

Our company employs a leading team of professionals from the oil and gas industry. The team has expertise in artificial intelligence software, down-hole tool design and geoscience. Our solutions combine the most advanced methodologies in artificial intelligence while embedding a deep interconnection to geology and geophysics 

Why is this important? We believe that if oil companies have less expensive and riskless solutions for reservoir characterization, drilling and completions, then they can deliver outperformance in cost efficiencies while improving production. However, the key to successful AI implementation requires inter-coupling the software with the geophysics. Whether in conventional or unconventional assets, our AI approach can lower costs by millions of dollars in each aspect of the workflow ranging from drilling, geology, geophysics, reservoir and completions.

In our mission to apply best-in-class artificial intelligence to meaningful upstream challenges, we are fortunate to have partnerships with two of the world’s largest oil companies – Royal Dutch Shell and Statoil – as well as Nabors, one of the world’s largest drilling contractors, each with demonstrated track records of bringing major innovations to the industry.

Well Logging and Drilling Optimization Using Common Drilling Data



Quantico’s QLog service provides a suite of synthetic logs including shear, compressional, density and neutron. QLog can be run for vertical, deviated or horizontal wells. The benefits to the oil company are: No nuclear or acoustic sources in the well. Savings up to 80% of conventional logging costs. Results within minutes of drilling your well.


Sonic, density and neutron logs while drilling. Demonstrated to be the same accuracy as LWD tools in deepwater. Now the rest of the quad combo can be obtained by only running Gamma and Resistivity. QDrill is an ideal solution to log challenging zones due to drilling hazards, slim hole or highly deviated sections. Eliminate logging NPT and lost-in-hole charges. If an LWD tool malfunctions or loses circulation, QDrill can be turned on to drill ahead.


Quantico is able to deliver UCS data in real-time and at the point of the Gamma sensor. When MSE diverges from offset trends, real-time UCS is critical to distinguish between changes in formation properties versus energy loss due to drilling dysfunction. Optimal drilling parameters alongside real-time formation tops is provided to stay in the efficient drilling window.


Using the primary logs from QLog, additional properties including Poisson’s Ratio, Young’s Modulus, Brittleness, and Minimum Horizontal Stress can be derived. These properties can be used to derive vertical and horizontal stress profiles, model frac treatments and optimize the placement of perforation clusters and plugs to correspond to changes in rock characteristics.


Quantico’s sonic logs can be obtained for wells drilled recently or several years ago – at a fraction of conventional logging costs. Combined with robust anisotropy models, QLogs provide a high-frequency source of vertical velocities along the length of the lateral. This can shorten processing time on depth migrations by up to 50% and increase the accuracy of seismic inversion.

How Does It Work?
  • Step 1: Local model calibration performed.
  • Step 2: Drilling data sent to Quantico in real-time or after TD
  • Step 3: Quantico processes input data in proprietary software
  • Step 4: Logs sent to oil company
    • Files delivered in .las format if post-drill or via WITS feed if real-time
    • Usable in conventional log analysis software

*In US Land, one or more nearby vertical logs are used. No horizontal logs required in most basins. In international/offshore, offset well data is required.

Why Choose Quantico?

Cost Savings

Demonstrated savings of up to 80% of logging costs (not including NPT) and lower drilling AFE by up to 30%.

No Risk

No tools added to the BHA. No nuclear sources enter the well. No lost-in-hole charges. Ideal in sections with drilling hazards.


Proven to be the same accuracy as LWD tools in deepwater. Sonic logs more robust in thin beds. Density not affected by washouts.


Eliminate log-related NPT. No wireline engineers or equipment on-site means lower HSSE risk.


No malfunctions of tool memory or power. Ideal for challenging logging conditions.

Near-Bit Data

Log data available at the point of the Gamma sensor. This means sonic logs available 60-90ft earlier than LWD tools.

Quantico’s QFrac service represents a valuable ‘big data’ solution to the stimulation process. By using the drilling data you already have, this service makes it much easier and cost-effective to initiate fractures and equalize proppant distribution in multi-cluster stages.

StrataGen, Frac Consultant


1.How does QLog compare to conventional horizontal logging tools?
Consistent Accuracy

QLog simulations have been demonstrated in several blind tests to exhibit accuracy consistent with conventional horizontal logging tools. In order to perform the benchmarking, three wells were identified where log-off data was available from two conventional logging tools being run in the same wells. The mean squared error (MSE) was calculated between the two sets of conventional logs (shown in red in the graphic below). The MSE was also calculated between QLog and a set of logs from a conventional tool (shown in blue in the graphic below). As demonstrated in one blind test, the accuracy QLog was in-line with the accuracy of conventional logging tools. In other words, the accuracy of QLog is similar to what would be expected if the same conventional logging tool were to perform a re-log.

Reliable Sonic

After conventional logging tools have been run, proper waveform processing is required in order to generate a high quality sonic log. Sometimes, this processing may be performed manually by an engineer or it may be automated with software used in the field. This added step creates an opportunity for erroneous waveform selection in certain intervals of the well. Potential poor results include Compressional locked on Shear arrival, Shear locked on Stoneley arrival, or unrealistic wavy slowness. Reprocessing of the waveforms can be expensive and difficult of the original logging data was not retained. QLog avoids this process altogether in simulating its sonic logs.

Quality Measurement

With conventional logging tools, poor sensor contact, centralization and issues caused by washouts lead to erroneous measurements. Relative to vertical wells, the difficulties in horizontal wells of proper sensor pad contact and centralization are heightened. However, the input parameters utilized by QES’ machine learning software are not impacted by such impediments. QLog results are substantially less prone to these types of measurement errors sometimes witnessed in logs from conventional tools.

2.How does QLog compare to logs derived from Gamma only?
Drilling dynamics significantly increases reliability

The accessibility of Gamma logs has in part made them a widely used approximation when examining the rock mechanics along horizontal wells. However, it is also widely understood that Gamma does not reliably reveal changes in sonic and density properties. The histogram below compares the results of over 70 Permian simulations with A) Gamma and Drilling Dynamics data as the input parameters used to transpose measured horizontal logs from Well X to simulated horizontal logs in Well Y, versus B) doing the same using Gamma as the only input parameter. The models that included the Drilling Dynamics data exhibited significantly more reliable predictions – specifically, how often they were more accurate, and in such instances, the size of the accuracy gains.

3.How does one know whether engineering completions will help?

The key question for many operators is will engineering completions enhance production and/or lower cost? And will the gains be large enough to matter?

There are increasing data points in industry literature to suggest that changing the placement of stages and perforation clusters to coincide with rock mechanics and lithology can indeed help. However, there are also instances where the results from testing engineered completions on a handful of well did not lead to statistically significant uplift.

QES believes that every reservoir is unique. Thus, the results of engineering completions will vary by reservoir. The only way to know the answer on your reservoir is to perform tests with sufficient sample size to generate statistically significant results. And in today’s budget constrained environment, to do so in a manner that does not increase costs or jeopardize production. 

Production across shale basins in highly variable. The Utica data set shown below is as follows:

(a) Sample set of 641 Utica wells placed on production during 2014
(b) 90 day average daily production of 714 bpd
(c) +/- 34% of wells (1 standard deviation) high as 1,306 bpd, low as 121 bpd

The conclusions from this data set suggest:

(1) Large range in 90-day IP indicates high variability in geology; and drilling, stimulation and completion methods.

(2) Given such high variability, a large amount of well data is needed to identify methods to improve production. For example, if one only had 90-day IP data from 10 wells, then estimating with a 95% confidence interval what the expected production would be would lead to a range from 347 bpd to 1,081 bpd in the Utica. Conversely, if average 90-day IP were 714 bpd (based on this dataset), and one desires to predict with 95% confidence interval the expected range of production within plus/minus 116 bpd (20% of the average), then he/she would need to gather production data from 100 wells.

Assuming that the number of stages and perf clusters in an engineered well are designed to be the same as a geometrically completed well, then the primary cost of the study normally arises from the added logging costs and some incremental amount of engineering and interpretation time. With QLog, an operator could obtain logs on 50 wells for the same cost as logging 5 wells with conventional tools.

The high volume of wells being drilled in shale basins across North America means that even small improvements in field wide IP and/or capex will lead to significant NPV gains. If the logging costs can be removed as a constraint to testing engineering completions, then operators can finally determine the potential impact on their reservoir.

Reservoir Delineation

Neural network enhanced seismic inversions result in much higher resolution than traditional seismic inversions. Detailed maps of key attributes can be generated to place wells in the best production sweet spots.

Well Placement

Precision well steering is enabled by advanced analytics that continuously update formation tops and well ties. This automated approach locates precisely where the bit is at all times and provides a look-ahead view of upcoming features to make the right steering decisions.

Drilling Optimization

Real-Time UCS enables rapid identify and remediation of drilling dysfunction. Once vibration or other causes of energy loss are removed from the drill string, then advanced artificial intelligence methods recommend the optimal drilling parameters to achieve maximum ROP.

Logging While Drilling

Real-time or on-demand synthetic logging provides critical information for projects where conventional LWD services were previously not economically viable. For projects that run LWD tools, a software-based approach can serve as a reliable add-on service that increases reliability, reduces NPT and logs every trip.

Engineered Completions

Take the horizontal logging cost and risk factors out of your decision to engineer completions. Or have the liberty to conduct a multi-well program to test various placement and design criteria with minimal impact on the AFE.


QLog is the only solution that allows you to obtain open hole logs on wells already put on production. The ability to review formation properties on wells drilled years ago is critical to selecting candidate wells to re-fracture. Now utilize rock mechanics and lithology to selectively target promising intervals.

Case Studies


December 10, 2017 – QES Featured in 
Upstream Technology


September 29, 2017 – Shell Newsletter Features QES LWD
with Deepwater Appomattox Project


September 28, 2016 – QES Co-Authors Paper Presented at
SPE ATCE: Big Data Yields Completion Optimization


August 1, 2015 – QES Featured in 
Journal of Petroleum Technology


May 4, 2015 – QES Announces Investment from
Shell Technology Ventures and Statoil Technology Invest


Contact Us

If you would like to find out more,
please get in touch with us.

Quantico Energy Solutions, Inc.
5800 Ranchester Drive, Suite 200
Houston, TX 77036



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