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.