eprintid: 170
rev_number: 4
eprint_status: archive
userid: 6
dir: disk0/00/00/01/70
datestamp: 2008-10-10
lastmod: 2015-05-29 19:48:37
status_changed: 2009-04-08 16:55:15
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Pyke, Randall
contributors_name: Aggarwala, Rita
contributors_name: Alberts, Tom
contributors_name: Bose, Chris
contributors_name: Driga, Adrian
contributors_name: Espesset, Aude
contributors_name: Harlim, John
contributors_name: Jeon, Jihyoun
contributors_name: Jeon, Seungwon
contributors_name: Katatbeh, Qutaibeh
contributors_name: Kolasa, Larry
contributors_name: Leok, Melvin
contributors_name: Mahmoud, Mufeed
contributors_name: Meza, Rafael
contributors_name: Nettel-Aguirre, Alberto
contributors_name: Popescu, Christina
contributors_name: Teja, Mariana Carrasco
title: An Automated Algorithm for Decline Analysis
ispublished: pub
subjects: utilities
studygroups: ipsw5
companyname: Alberta Energy Company
full_text_status: public
abstract: Oil and gas wells are regularly monitored for their production rates. For a variety of reasons, typical production rate data is noisy and highly discontinuous, and we wish to use this data to extrapolate trends in the production rate to forecast future production and ultimate cumulative reserve recovery.

The proposed solution consists of three main steps: (1) Segmentation of Data, (2) Curve fitting, and (3) a Decision Process. Segmentation of Data attempts to identify intervals in the data where a single trend is dominant. A curve from an appropriate family of functions is then fitted to this interval of data. The Decision Process gauges the quality of the trends identified and either formulates a final answer or, if the program cannot come to a reliable answer, '
flags' the well to be looked at by an operator.
problem_statement: Oil and gas wells are regularly monitored for their production rates. For a variety of reasons, typical production rate data is noisy and highly discontinuous.

Decline analysis is a process that extrapolates trends in the production rate data from oil and gas wells to forecast future production and ultimate cumulative reserve recovery. Current software often attempts a best fit approach through all  he data, but the result is erroneous in the majority of cases. A human operator  with an understanding of the factors that affect the behaviour of oil and gas wells can do a much better job of forecasting appropriately; however, it is a time-consuming process.

The goal is to find an algorithm that can be easily interfaced with standard industrial software and that incorporates some of the criteria used in the human analysis so as to perform acceptable forecasts in the majority of cases.
date: 2001
date_type: published
pages: 9
citation:   Pyke, Randall  (2001) An Automated Algorithm for Decline Analysis.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/170/1/decline_analysis.pdf