The intent of this study is to reassess the potential of New Albany Shale formation using a novel and integrated workflow, which incorporates field production data and well logs using a series of traditional reservoir engineering analyses complemented by artificial intelligence & data mining techniques. The model developed using this technology is a full filed model and its objective is to predict future reservoir/well performance in order to recommend field development strategies.;The impact of different reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas production in New Albany Shale have been presented.;The study was conducted using publicly available numerical model, specifically developed to simulate gas production from naturally fractured reservoirs.;The study focuses on several New Albany Shale (NAS) wells in Western Kentucky. Production from these wells is analyzed and history matched. During the history matching process, natural fracture length, density and orientations as well as fracture bedding of the New Albany Shale are modeled.;Sensitivity analysis is performed to identify the impact of reservoir characteristics and natural fracture aperture, density and length on gas production, using information found in the literature and outcrops and by performing sensitivity analysis on key reservoir and fracture parameters.;Then, the history-matched results of 87 NAS wells have been used to develop a full field reservoir model using an integrated workflow, named Top-Down, Intelligent Reservoir Modeling. In this integrated workflow unlike traditional reservoir simulation and modeling, we do not start from building a geo-cellular model. Top-Down intelligent reservoir modeling starts by analyzing the production data using traditional reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, Single-well History Matching, Volumetric Reserve Estimation and Recovery Factor. These analyses are performed on individual wells in a multi-well New Albany Shale gas reservoir in Western Kentucky that has a reasonable production history. Data driven techniques are used to develop single-well predictive models from the production history and the well logs (and any other available geologic and petrophysical data).;Upon completion of the abovementioned analyses a large database is generated. This database includes a large number of spatio-temporal snap shots of reservoir behavior. Artificial intelligence and data mining techniques are used to fuse all these information into a cohesive reservoir model. The reservoir model is calibrated (history matched) using the production history of the most recent set of wells that have been drilled in the field. The calibrated reservoir model is utilized for predictive purposes to identify the most effective field development strategies including locations of infill wells, remaining reserves, and under-performer wells. Capabilities of this new technique, ease of use and much shorter development and analysis time are advantages of Top-Down modeling as compared to the traditional simulation and modeling.;In addition, 31 recently drilled well in Christian county Western Kentucky-Halley's Mills quadrangle have been used to perform Top-down modeling. Zone manager feature of Geographix software is used. The available production data are going to be the attributes in this feature. The contours are generated and the results have been compared with the result of Top-down modeling (Fuzzy pattern recognition). Structural map, isopach map and the other geological map has been generated using Geographix.;Additionally, in order to indentify the effect of horizontal lateral length on well productivity from New Albany Shale, fracture network has been regenerated in order to represent the distribution of natural fracture in that formation.
Ensemble-Based Seismic and Production Data Assimilation Using Selection Kalman Model
