Abstract

Hydraulic fracturing is an economic way of increasing gas well productivity. Hydraulic fracturing is routinely performed on many gas wells in fields that contain hundreds of wells. Companies have developed databases that include information such as methods and materials used during the fracturing process of their wells. These databases usually include general information such as date of the job, name of the service company performing the job, fluid type and fluid amount, proppant type and proppant amount, and pumped rate. Sometimes more detail information may be available such as breakers, amount of nitrogen, and ISIP, to name a few.

These data usually is of little use if some of the complex 3-D hydraulic fracture simulators are used to analyze them. But valuable information can be deduced from such data using virtual intelligence tools. The process covered in this paper takes the available data and couples it with general information from each well (things like latitude, longitude and elevation), any information available from log analysis and production data and uses a data mining and knowledge discovery process to identify a set of best practices for the particular field. The technique is capable of patching the data in places that certain information is missing. Complex virtual intelligence routines are used to insure that the information content of the database is not compromised during the data patching process. The conclusion of analysis is a set of best practices that has been implemented in a particular field on a well or on a group of wells basis. Since the entire process is mostly data driven we let the data "speak for itself" and "tell us" what has "worked" and what "has not worked" in that particular field and how the process can be enhanced on a single well basis. In this paper the results of applying this process to Medina formation in New York State will be presented. This data set was furnished by Belden & Blake during a GRI / NYSERDA sponsored projects.

This process provides an important step toward achieving a comprehensive set of tools and processes for data mining, knowledge discovery, and data-knowledge fusion from data sets in oil and gas industry.

Background

Medina and Whirlpool sands of southwest New York State are considered to be tight gas sands. Most wells in the Northeast of the United States, including the wells that are drilled into the Medina and Whirlpool sands, are fractured upon completion to provide economic amounts of gas. A database that included about 100 Medina and Whirlpool wells was obtained from an operator in the region. This database included information on each well such as well location, reservoir quality, completion data, stimulation data and production data. Table 1 shows the data available on each well in the database.

The goal of the study was to use the available information in the database in order to identify the best practices in completion and hydraulic fracturing in this field. Furthermore, the objective of this study was to recommend locations, completion and stimulation practices for the future wells based on the findings of the study. The methodology incorporated in this study, with some minor modifications, can be used to identify best practices from any oil and gas database. Generally, the methodology used in this study is categorized as data mining, and knowledge discovery.

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