Shale Analytics

Management and Information Reservoir Descriptions and Dynamics


Data-Driven Analytics is becoming an important point of competitive differentiation in the upstream oil and gas industry. When it comes to production from Shale, companies are realizing that in “Data”, they possess a vast source of important facts and information. Since, in analysis and modeling of production from shale, our traditional techniques leave much to be desired, “Data”, reflecting the field measurements, can provide much needed insight. Data Driven Analytics is the set of tools and techniques that provides the means for extraction of patterns and trends in data and construction of predictive models that can assist in decision making and optimization.

In Advanced Data Driven Analytics, data from the well and the formation are integrated with field measurements that represent completion and hydraulic fracturing practices and are correlated with production from each well. As the number of wells in an asset increases, so does the accuracy and reliability of the Advanced Data Driven Analytics.

Attendees will become familiar with the fundamentals of data-driven analytics and the most popular techniques that are used to perform such tasks such as conventional statistics, artificial neural networks and fuzzy set theory.  

This course will demonstrate through actual case studies (real field data from thousands of shale wells) how to impact well placement, completion, and operational decision making based on field measurements rather than human biases and pre-conceived notions.

Topics Include:

  • Basics of Artificial Intelligence and Machine Learning
  • Descriptive Analytics:
    • Impact of reservoir, completion, and operational characteristics on production
    • Organize and prepare the data for predictive modeling
  • Predictive Analytics:
    • Honor the known physics of fluid flow in shale
    • Avoid over-training (memorization) while promoting generalization
  • Prescriptive Analytics:
    • Optimize completion practices
    • Optimize well spacing and stacking
    • Identify best service companies

Including an introduction to AI-based dynamid modeling to capture well and reservoir dynamics to address issues such as Frac-Hit.

Learning Level


Course Length

1 or 2 Days

Why Attend?

Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of researchers and practitioners have concentrated their efforts on providing the next generation of tools that incorporates this technology, for the petroleum industry. 

Data driven analytics have become an integrated part of many new technologies used by everyone on their day-to-day lives such as smart automatic-transmission in many cars, detecting explosives in the airport security systems, providing smooth rides in subway systems and preventing fraud in use of credit cards. They are extensively used in the financial market to predict chaotic stock market behavior, or optimize financial portfolios.

A large amount of data is routinely collected during the production operations in shale assets. The collected data can be utilized to gain a competitive advantage in optimizing production and increasing recovery.

Who Should Attend

This course is intended for completion engineers, production engineers and managers, reservoir engineers, geoscientists, asset managers, and team leaders.


0.8 or 1.6 CEUs (Continuing Education Units) will be awarded for this 1-day course.

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14 day window will not be refunded.  Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page.  Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will.  Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.

Full Regional cancellation policies can be found at the “Cancellation Policy” link on the SPE Training Course Catalog page:


Shahab D. Mohaghegh is the president and CEO of Intelligent Solutions, Inc. (ISI) the pioneers of data driven analytics in the upstream E&P industry. He is also professor of petroleum and natural gas engineering at West Virginia University. With more than 22 years of experience in the application of Artificial Intelligence & Data Mining in petroleum engineering, he has developed innovative workflows and technology that incorporates hybrid forms of neural networks, genetic algorithms and fuzzy logic in solving problems and building predictive models related to smart wells, smart completions, and smart fields as well as to drilling, completion, well stimulation, surface facility optimization, formation evaluation, seismic inversion, reservoir characterization, reservoir simulation, and reservoir management.

He has authored more than 180 technical papers. He was a SPE Distinguished Lecturer (2007-2008) and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. He was the former chair of the SPE Global Training Committee and currently chairs SPE Book committee.

He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources. Mohaghegh represents the Unites States in the International Organization for Standardization (ISO) for CO2 capture and storage.

Mohaghegh holds BS, MS and PhD degrees in petroleum and natural gas engineering.