Using AI and Machine Learning to Indicate Shale Anisotropy and Assist in Completions Design

Abstract

Operating companies in the unconventional Marcellus shale play have all faced a similar and problematic issue, while attempting to produce natural gas over the last decade. Companies have quickly realized that not every perforation along their horizontal wells are producing gas. In fact, producing perforations are only ranging from 15%-70% of the total perforations along the horizontal wellbore [1]. This unexplained issue results in millions of dollars in lost revenue per well, in addition to the sunk cost of paying for completions that are not actually yielding any produced gas.What is causing these perforations to have no produced gas? There are many theories being researched in the private sector and academia including: stress shadowing, proppant type and concentration, sand-outs, unconventional reservoir modeling, and improved geosteering. While any and all of those situations may have an impact on production, this study will focus on one potential issue with shale wells that may be the root cause of this phenomenon: the anisotropic nature of shale. By nature, shale is highly anisotropic, which means that the physical properties of shale change significantly from point to point in the x, y, and z directions. This is caused by the laminar structure of the shale due to the shales formation, effecting properties in the z-direction, as well as widespread natural fracturing effecting properties in the x-y directions [20]. Is it possible that the random and highly variable physical properties of the Marcellus shale are responsible for poor fracture propagation and production at various perforated clusters along the horizontal wellbore?First, I would like to thank Dr. Ming Gu for working so well with me over the last couple of years. I am so pleased to have conducted meaningful research that could impact the future of our industry together. I appreciate how Dr. Gu has always been there to help every step of the way, and I will never forget that he gave me the opportunity to join his research team at WVU. I would also like to thank Professor Sam Ameri, who's guidance and status as a parental figure within the college, helped bring me back to WVU to pursue my graduate degree and assisted in pairing me with Dr. Gu. Sam, your guidance and wisdom has always been cherished by me, and I will carry it with me for the rest of my life. I would also like to thank Dr. Shahab Mohaghegh and members of his LEADs study group for all of their amazing support. Finally, I want to acknowledge and appreciate the research start-up package provided by the P.N.G.E. Department at West Virginia University for funding my research, and to the Marcellus Shale Energy and Environmental Laboratory (MSEEL) for providing me a public database with which I was able to conduct and complete my research.