Shale Analytics: AI-based Production Optimization in Shale


Disciplines: Data Science and Engineering Analytics | Reservoir

Course Description

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 they possess a vast source of important facts and information in their data. In analysis and modeling of production from shale, our traditional techniques leave much to be desired.  Field measurement data 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 analytics.

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

This course will demonstrate through actual case studies (and 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 preconceived notions.

Topics:

Basics of artificial intelligence (AI) 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 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

Introduction to AI-based dynamic modeling

  • Capture well and reservoir dynamics
  • Address issues such as frac hits

Learning Level

Intermediate

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 these technologies for the petroleum industry.

Data-driven analytics have become an integrated part of many new technologies used in our daily lives such as smart automatic transmissions in cars, the detection of explosives within airport security systems, smooth rides in complex subway systems, and the prevention of fraud in credit card use. They are extensively used to predict chaotic stock market behavior, and are increasingly being used to optimize financial portfolios.

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

Who Attends

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

CEUs

0.8 or 1.6 CEUs (Continuing Education Units) are awarded for this 1- or 2-day course.

Additional Resources

This course has a supplemental book located in our SPE Bookstore entitled Data-Driven Reservoir Modeling. Please check out this valuable resource!

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.

Instructor

Dr. Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).


Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011).


He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

Other courses by this instructor

AI-based (Top-Down) Full Field Reservoir Simulation and Modeling
Dr. Shahab D. Mohaghegh

Numerical Reservoir Simulation is a “Bottom-Up” Reservoir Modeling, while AI-based Reservoir Simulation is a “Top-Down” Reservoir Modeling. AI-based Reservoir Simulation is NOT a “Hybrid Model” through incorporation of the realistic, engineering applic...

(Read More)

Disciplines: Data Science and Engineering Analytics | Reservoir

CCS Analytics – AI-based Carbon Capture and Storage
Dr. Shahab D. Mohaghegh

Engineering application of Artificial Intelligence & Machine Learning will significantly address Climate Change in the next several decades. The main reason of positive and important contribution of Artificial Intelligence to Climate Change has muc...

(Read More)

Disciplines: Data Science and Engineering Analytics | Production and Operations | Reservoir

Data-Driven Reservoir Modeling
Dr. Shahab D. Mohaghegh

Data-Driven Reservoir Modeling (Reservoir Analytics) is defined as the application of Artificial Intelligence and Machine Learning in fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) is the manifestation of the digi...

(Read More)

Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir

Engineering Application of Artificial Intelligence and Machine Learning
Dr. Shahab D. Mohaghegh

It is very important to learn what are the main characteristics and requirements in applying Artificial Intelligence and Machine Learning to solve Engineering related problems. To learn and apply Science and Engineering, Homo Sapiens must take a seriou...

(Read More)

Disciplines: Data Science and Engineering Analytics

Petroleum Data Analytics - Engineering Application of Artificial Intelligence & Machine Learning
Dr. Shahab D. Mohaghegh

Artificial Intelligence and Machine Learning is revolutionizing many industries.  This technology is becoming an important point of competitive differentiation in the upstream oil and gas industry. Since optimization of production and enhanced recovery...

(Read More)

Disciplines: Completions | Production and Operations | Reservoir

Python for Petroleum Data Analytics
Dr. Shahab D. Mohaghegh

Combining petroleum engineering domain expertise with computer programming using "Python" as the most popular coding language for data science, artificial intelligence and machine learning, this course enables petroleum engineering professionals to bui...

(Read More)

Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Health, Safety, Environment, and Sustainability | Production and Operations | Projects, Facilities, and Construction | Reservoir

Smart Proxy Modeling – Engineering Application of Artificial Intelligence in Numerical Simulation
Dr. Shahab D. Mohaghegh

Smart Proxy Modeling is the application of Artificial Intelligence and Machine Learning in Numerical Simulation. Smart Proxy Modeling has already been successfully applied to Numerical Reservoir Simulation and Computational Fluid Dynamic. Details of Sm...

(Read More)

Disciplines: Data Science and Engineering Analytics | Reservoir

SPE Petroleum Data Analytics Series - Week one: Subsurface Analytics
Dr. Shahab D. Mohaghegh

Petroleum Data Analytics is the application of Artificial Intelligence and Machine Learning in the oil and gas industry. Future of our industry will be highly influenced by Petroleum Data Analytics. Engineering-domain experts who become highly skilled ...

(Read More)

Disciplines: Data Science and Engineering Analytics | Reservoir

(Cancelled)
06 Oct 2022
Houston, Texas, USA

Held in conjunction with SPE Annual Technical Conference and Exhibition

Limited seats, register now!

Pricing

Register

30 Oct 2022
Abu Dhabi, UAE

Held in conjunction with ADIPEC

Members
USD 500 + VAT

Nonmembers
USD 500 + VAT

Register