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Shale Analytics

Petroleum Data Analytics for Unconventional Resources

Shahab D. Mohaghegh - January 2017

“WITHOUT DATA, YOU ARE JUST ANOTHER PERSON WITH AN OPINION”
W.E. DEMING (1900-1993)

When W. E. Deming uttered these words in 1980s, world new nothing about the impact that shale will have in changing the energy landscape of the 21st century. Nevertheless, no other quotation can so vividly describe the state of our scientific and engineering knowledge of the physics of completion, stimulation and interaction between induced and natural fractures during the oil and gas production from shale. Many well intentioned engineers and scientists that are involved in the day to day operations of shale wells develop the intuition required to make the best of what is available to them in order to increase the efficiency and the recovery from the shale wells. However, a full understanding of the physics and the mechanics of the storage and transport phenomena and the production operation in shale has remained elusive to a large extent.

There are many opinions and speculations on what exactly happens as we embark upon drilling, completing and hydraulically fracturing shale wells. Much of the opinions and speculations are hardly ever supported by facts and data, and in the occasions that they are, it hardly ever transcends the anecdotal evidences that have been heard of, or been seen. However, the fact remains that as an industry, we collect a significant amount of data (field measurements - facts) during the operations that result in oil and gas production from shale. It is hard to imagine that these collected data do not contain the knowledge we need in order to optimize production and maximize recovery from this most prolific hydrocarbon resource.

It took our industry professionals several years to come to the inevitable conclusion that our conventional modeling techniques that were developed for carbonate and coalbed methane formations cannot substitute our lack of understanding of the physics and the mechanics of completion and production from shale wells. But now that this fact has become self-evident, maybe the speculative resistance to the required paradigm shift to move to a data-driven solution can be finally be overcome.

This book is dedicated to scratching the surface of all that is possible in the application of Petroleum Data Analytics to reservoir management and production operation of shale, what we have chosen to call “Shale Analytics”. Here we demonstrate how the existing data collected from the development of shale assets can help in developing a better understanding of the nuances associated with operating shale wells. How to learn from our experiences in order to optimize future operations. How to create a system of continuous learning from the data that is generated on a regular basis. In other words, using this technology we can make sure that every barrel of oil and every MCF of gas that is produced from our shale wells not only brings return to our investment, but also enriches our understanding of how this resource needs to be treated for maximum return.

Traditional Modeling

Petroleum industry has changed significantly since production of oil and natural gas from shale has become a profitable endeavor. Shale as a source for production of hydrocarbon is a revolutionary idea that has now become reality. The technology that has realized this revolution is innovative and game changing. However, as an industry we are still using old, conventional technologies to try to analyze, model, and optimize recovery from this resource. Our analyses and modeling efforts up to now have had limited success. Many engineers and managers in the industry now openly admit that numerical simulation and RTA (Rate Transient Analysis) add very little value to their operation.

This could have been foreseen (and actually was discussed and published in 2013 – (1 & 2)) based on the essence of these technologies and how they model the network of natural fractures, induced hydraulic fractures, and the way they are coupled and interact in shale. When it comes to production from shale using long horizontal wells that are hydraulically fractured in multiple stages, these conventional technologies are too simplistic and are not capable of realistically modeling the physics (as much of it as we understand) of the problem. Therefore, they make unreasonable simplifying assumptions to a degree that make their use all but irrelevant. However, in the absence of any other widely acceptable technology as an alternative for modeling the storage and transport phenomena in shale, these technologies flourished in the past several years.

A Paradigm Shift

Paradigm Shift, a term first coined by Thomas Kuhn (3), constitutes a change in basic assumptions within the ruling theory of science. According to Kuhn, "A paradigm is what members of a scientific community, and they alone, share" (4). Jim Gray, the American computer scientist who received the Turing Award for his seminal contribution to computer science and technology coined the term “Fourth Paradigm” of science referring to data-driven science and technology (5).

In his classification the first paradigm of science (thousands of years ago) was empirical describing the natural phenomena. The second paradigm of science (last few hundred years) was the theoretical branch of science where we using models and generalizations to describe nature, examples of which are Kepler’s Law, Newton’s Law of Motion, and Maxwell’s Equation. When the theoretical models grew too complicated to be solve analytically, the Third Paradigm of science was born (last few decades). This was the computational branch of science that includes simulating complex phenomena. Today, the Fourth Paradigm is the data-Intensive, data exploration, or “eScience”.

This paradigm unifies theory, experiment, and simulation where data is captured by instruments or generated by simulator and is processed by software and information/knowledge stored in computer. Scientists analyze database/files using data management tools and statistics. Based on Jim Gray’s view “The World of Science Has Changed. There is No Question About it. The techniques and technologies for data-intensive science are so different that is worth distinguishing data-intensive science from computational science as a new, fourth paradigm for scientific exploration.”

Petroleum Data Analytics that is the application of data-driven analytics in the upstream oil and gas represents the paradigm shift articulated by Gray (5). Petroleum Data Analytics helps petroleum engineers and geoscientist build predictive models by learning from hard data (field measurements). It has proven to be the alternative to traditional technologies. Data-driven analytics is gaining popularity among engineers and geoscientists as it proves its predictive capabilities and as more solutions in the form of software products (6) surface. The main objective of this book is to cover the state of the art in application of data-driven analytics in analysis, predictive modeling and optimization of hydrocarbon production from shale formations. We have named this technology “Shale Analytics”.

References

  1. Reservoir Modeling of Shale Formations. Mohaghegh, S. D. s.l. : Elsevier, 2013, Journal of Natural Gas Science and Engineering, Vol. 12, pp. 22-33.
  2. A Critical View of Current State of Reservoir Modeling of Shale Assets. Mohaghegh, S. D. Pittsburgh, PA : Society of Petroelum Engineers - SPE, 2013. 165713.
  3. Kuhn, Thomas S. The structure of scientific revolutions. Chicago, IL : University of Chicago Press, 1996. 0226458075.
  4. The Essential Tension: Selected Studies in Scientific Tradition and Change. Chicago, IL. : The University of Chicago Press, 1984. 0-226-45806-7.
  5. A New Series of Rate Decline Relations Based on the Diagnosis of Rate-Time Data. Boulis, A. S., Ilk, D., Blasingame, T. A. Calgary, Alberta : s.n., 2009. Canadian International Petroleum Conference (CIPC) .
  6. IMprove is a software application by Intelligent Solutions, Inc. that helps user to analyze hard data (field measurements), build and validate predictive models, and perform post-modeling analyses for production optimization from shale formations.

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