About the Course
SPWLA members, view the course for free!
Carbonate rock heterogeneity makes it hard for Geoscientists and Reservoir Engineers to define universal classification method that honors critical reservoir static properties. Classifications like, lithofacies, capillarity, and textural methods based rock typing on one or two static properties, then tried to find analogue to other properties by clustering, then populate rock types across the field. However, from field observations and experiences of using these conventional techniques, they suffered from several gaps, like inability to have properties analogue consistent throughout whole reservoir. Besides, the clusters have large dispersion, producing overlaps, theoretically they could not fully honor the physics and rock properties links.
Our research work delivered rock typing model that honors static properties all together, through changing classification to resolve the gaps of traditional methods. The goal of reservoir characterization and rock typing is to enable building geological and simulation models, that best honor properties. To achieve target, we build framework that focused on analyzing effects of each rock property on another, adding to the model's most critical boundaries. This technique resolved the gap of pore and pore-throat network through Multiple Properties Intersection.
This Integrated Carbonate Rock Typing technique starts with capturing the heterogeneity of carbonate rock by creating a matrix of core permeability, capillary pressure (end point, threshold pressure, and Plateau), pore-throat size distribution and porosity. Then intersecting matrix properties to compose weighted links between properties and to identify unique groups. Resulted classes are new carbonate rock type classes that entered to feedback analysis node to explore and confirm linked physics to tune classes’ thresholds and ensure no overlap between groups. Finally, for using this technique in non-cored wells, we built an analogue with logging data through innovative permeability, capillary pressure, and saturation function technique. The novel function names are, CROPC-1, CROPC-2, (CROPC: Conjunction of Rock Properties Convergence) and RocMate C-Function (RocMate: Rock Type Matrix), while the novel permeability estimation technique is called MPTE, (MPTE: Multi Property Threshold Environment).
Omar Al-Farisi is the winner of “Most Likely to Write the Best-Selling Book” London Business School MBA Student Award in 2015. The year after, in 2016, he received the “Most Likely to Change the Wold Award” from London Business School Student Association. Published more than 20 Engineering, Scientific, Business, Economics, and Digital Technology papers. Five of his publications received Innovation Awards, 2006 (10th Rank), 2009 (8th Rank), 2010 (5th Rank), 2011 (3rd Rank) and 2012 (2nd Rank) among 100’s of competitive submissions for ADMA-OPCO Innovation Award. In 2010, his Data Classification Modeling put him on the Top 10 Young Professionals Award nomination of the International Society of Petroleum Engineers. Lectured Neural Network Prediction Modeling for Master of Engineering Graduate Students at the Khalifa University of Science, Technology, and Research, Petroleum Institute. He holds a BSc degree in Electronics and Communication Engineering, and an MSc in Petroleum Engineering, and 2017 he received his MBA Degree from London Business School. He is Interdisciplinary Engineering Ph.D. student in KUSTAR, Masdar Institute in collaboration with MIT (Massachusetts Institute of Technology). He initiated seven patents since 2006. He is writing a nonfiction book, related to solving World Economics challenges using latest digital advancement, created the concept of Hubnomics, www.hubnomics.com, and started writing a novel.
Start1. Introduction, Objectives, Benefits, Rock Typing, and Challenges (14:00)
Start2. Electrofacies Universal Rock, UROK (4:44)
Start3. 1st Novel Development (5:30)
Start4. 2nd Novel Development (3:11)
Start5. 3rd Novel Development (9:58)
Start6. Findings and Conclusions (6:26)