Fast Track Reservoir Modeling of Shale Formations in the Appalachian Basin, Application to Lower Huron Shale in Eastern Kentucky

摘要

In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history matching) this new approach starts by attempting to build a reservoir realization from well production history (Top to Bottom), augmented by core, well-log, well-test and seismic data in order to increase accuracy. This approach requires creation of a large spatial-temporal database that is efficiently handled with state of the art Artificial Intelligence and Data Mining techniques (AI & DM), and therefore it represents an elegant integration of reservoir engineering techniques with Artificial Intelligence and Data Mining. Advantages of this new technique are a) ease of development, b) limited data requirement (as compared to reservoir simulation), and c) speed of analysis. All of the 77 wells used in this study are completed in the Lower Huron Shale and are a part of the Big Sandy Gas field in Eastern Kentucky. Most of the wells have production profiles for more than twenty years. Porosity and thickness data was acquired from the available well logs, while permeability, natural fracture network properties, and fracture aperture data was acquired through a single well history matching process that uses the FRACGEN/NFFLOW simulator package. This technology, known as Top-Down Intelligent Reservoir Modeling, starts with performing conventional reservoir engineering analysis on individual wells such as decline curve analysis and volumetric reserves estimation. Statistical techniques along with information generated from the reservoir engineering analysis contribute to an extensive spatio-temporal database of reservoir behavior. The database is used to develop a cohesive model of the field using fuzzy pattern recognition or similar techniques. The reservoir model is calibrated (history matched) with production history from the most recently drilled wells. The calibrated model is then further used for field development strategies to improve and enhance gas recovery.