top of page

Safer and Lower Cost Well Logging

Formation evaluation logs in real-time or post-drilling, without the operational risks and at a significantly lower cost.


Data Driven Well Logging

QLog provides a suite of synthetic logs including shear, compressional, density and neutron. As a post-drilling or real-time service, this solution can help reduce the operational hazards of logging and eliminates the need for nuclear or acoustic tools in the borehole. With QLog, logging becomes a more cost effective and safer process.


Safer and Lower Cost Logging with Machine Learning

QLog provides a suite of synthetic logs including compressional and shear sonics, density, and neutron. QLog can be run for vertical, deviated or horizontal wells. Qualification tests with supermajors have shown QLog to provide the same accuracy as LWD tools in both deep-water and onshore wells. Additional benefits to the oil company include no nuclear or acoustic sources in the well; and savings up to 80% of conventional logging costs.


Lower Cost

QLog can deliver results within the same accuracy of LWD tools for a fraction of traditional methods.

No boots on the Ground

QLog can be run as SaaS solution or from our offices in Houston, TX with no need for personnel in the field.

Data Driven

The data driven approach
employed by QLog allows for real-time near bit rock property predictions.

No Nuclear Sources

Since QLog's predictive power comes from a state-of-the-art neural network, we can record complete logs without having to send a single nuclear source downhole. 

Drilling Data goes in, Rock Properties Come out

QLog is based on the simple principle that a fundamental physical relationship exists between drilling (EDR) data and the formation's elastic properties. Our approach, leverages neural network technology to uncover this relationship and derive triple-combo FE-logs from input EDR data. Key aspects of QLog have been awarded patent protection. This reinforces the growing importance of applying artificial intelligence to extract valuable rock properties information from the drilling data – and Quantico’s technology leadership in this area.  


QLog Funnel

Conditioned drilling data is used as an input to a trained neural network. The results are high resolution formation properties.

QLog Case Studies in and Gulf of Mexico Trinidad and Tobago

In 2019, Quantico published an SPE paper alongside Shell. This work was co-presented at SPE ATCE. The study focused on the use of QLog in two case studies in Gulf of Mexico and Trinidad and Tobago.

  • What properties can be predicted with QEarth?
    Virtually any property that is logged or measured in the wellbore, e.g., velocities, derivative attributes, Gamma Ray, Resistivity, Density, Stresses, TOC, Brittleness, Pore Pressure, etc. Also drilling measurements such as UCS, wellbore stability, WOB, etc.
  • What is the essential input data to QEarth?
    Seismic (stack and angle stack reflectivities and derived attributes) and interpreted surfaces. Well logs and well tops for training.
  • How much does it cost to build a hi-res earth mode with QEarth?
    If you provide # of wells with full log suites, areal extent of 3D seismic and number of properties you wish to have simulated, we can provide indicative pricing. Submit a Technical Presentation Request if interested in learning more
  • What type of neural network do you use? Is it a deep learning algorithm
    There’s generally not enough data to justify the use of a Deep Learning neural network so we generally use a simple ANN. Where the “secret sauce” comes into play is how one sets up the NN to reference the data properly. We don’t just look at one data point at the trace at a particular depth – we look at a user-specified number of samples within a sliding/overlapping reference window.
  • How does your ML inversion treat the seismic wavelet?
    We do not explicitly extract the seismic wavelet. We treat the seismic trace as a time (or depth) series and sample it at a resolution that approaches log scale.
  • How much input/decision making is typically required by a user in QEarth compared to conventional methods?
    Much of the QEarth process is automated. In addition, time consuming tasks such as rock physics modeling, wavelet extraction and low frequency model building are not performed in this data-driven approach. Hence the dramatic reduction in time-to-solution.
  • Where has QEarth been applied (a) conventional: clastics & carbonates (b) Unconventionals and what were the results?
    A) Sub-salt GOM and pre-salt Brazil, offshore Malaysia, offshore onshore Alaska, onshore Middle East, offshore Oman, onshore Colombia. B) Several in the Permian Basin and Eagle Ford in Texas. SCOOP/STACK play in Oklahoma. Results have mostly been good to excellent. Only one was disappointing due to poor well availability and imaging issues (poor seismic-to-well tie)
  • Do you have to input a low frequency trend or depth dependency?
    The low frequency trend/depth dependency is explicitly present in the input log data and is "learned" by the neural network during the training phase.
  • How do you address uncertainty in your reservoir property predictions?
    We use a process called Bagging (Bootstrap aggregation) where the algorithm randomly selects 20% - 30% of the input data and uses it to verify simulations. We also randomly withhold wells from the training set in a round-robin fashion. This is called Grid Search with K-Fold cross-validation.
  • What kind of seismic attributes do you put into QEarth? Is it just amplitude or are there other key attributes?
    We are quite flexible in terms of the input seismic attributes. Effectively we can use the same attributes as a standard seismic inversion, but we are not limited to amplitude data. We can even input AVO attributes and conventional inversion attributes. One key point to make is that we can explicitly invert for properties rather than deriving them mathematically from Ip, Is, and Density. This is especially important when one considers we can use QEarth to invert for drilling dynamics data, such as weight on bit, pore pressure, fracture gradient, wellbore stability and UCS, etc.
  • Are there any limits to the size of the seismic data volume size and the number of wells?
    No. It stands to reason that an inversion model would benefit from a larger number of wells, but over-training may happen. Depending on the size of the volumes and attributes of the data varying populations of wells are used.
  • How will QEarth handle poor well control or poorly sampled geology?
    Neural networks are not great extrapolators but Quantico will conduct extensive analysis of well control prior to kick-starting a project. Before a project starts, we will communicate concerns about poorly sampled areas of a volume and determine what KPIs are feasible given the data available.

© Quantico Energy Solutions 2020

bottom of page