Field Development Strategies for Bakken Shale Formation

Bakken shale has been subjected to more attention during the last decade. Recently released reports discussing the high potential of the Bakken formation coupled with advancements in horizontal drilling, increased the interest of oil companies for investment in this field. Bakken formation is comprised of three layers. In this study upper and middle parts are the core of attention. Middle member which is believed to be the main reserve is mostly a limestone and the upper member is black shale. The upper member plays as a source and seal which has been subject to production in some parts as well. In this study, we implement Top-Down Intelligent Reservoir Modeling technique to a part of Bakken shale formation in Williston basin of North Dakota. In this study, two different Top-Down approaches have been followed for building reservoir models: Static Reservoir Modeling and Spontaneous History Matching-Predictive Modeling. This innovative technique utilizes a combination of conventional reservoir engineering methods, data mining and artificial intelligence to analyze the available data and to build a full field model that can be used for field development. Unlike conventional reservoir simulation techniques which require wide range of reservoir characteristics and geological data; Top-Down modeling utilizes the publicly available data (minimum required data: production data and well logs) in order to generate reservoir model. The model accuracy can be enhanced as more detail data becomes available. The model can be used for proposing development strategies. Static and predictive reservoir models for Bakken Shale formation are developed. The static reservoir model is then used to identify remaining reserves and sweet spots that can help operators identify infill locations. Furthermore economical analysis for some proposed new wells is performed. The intelligent predictive model was trained, calibrated and verified using production, log and completion data. The history matched predictive model can be further implemented for predicting the production. FIELD DEVELOPMENT STRATEGIES FOR BAKKEN SHALE FORMATION Saeed Zargari Hereby I am going to dedicate this thesis to my parents who has helped me to succeed and instilled in me the confidence that I am capable of doing anything I put my mind to. Thank you for all the unconditional love, guidance, and support that you have always given me. Acknowledgement I would like to show my gratitude and appreciation to my research advisor Dr. Shahab Mohaghegh for his advice, guidance, and encouragement during the course of this research. My appreciation goes to Dr. Samule Ameri, and Dr. Razi Gaskari, who generously accepted to be a member of my thesis committee and all made significant contribution to this work. Their continuous and constructive critiques and suggestions have helped me a lot to improve this work. Special thanks go to my professors in the PNGE Department for their support and for their time to share their knowledge with me. I express my thankfulness to the administrative associate of PNGE department, Beverly Matheny, for her kindness, friendship, and her presence to help the students. Also, I would like to express my gratitude to Department of energy for financially supporting the Project and Intelligent Solution Inc. for providing us with IPDA , IDEA and IMAGINE softwares to perform the reservoir simulations in this work. My profound gratitude is expended to my parents. Although they have been miles away from me I have always been supported by their understanding, trust and wholehearted help they have been giving to me. This research was performed in support of the NETL-RUAProject No. 4000.4.650.920.004, Reference No.: RES047, TPR-3280 Table of