An Automated Algorithm for Decline Analysis

Pyke, Randall (2001) An Automated Algorithm for Decline Analysis. Canadian Industrial Problem Solving Workshops > 5th IPSW [Seattle 18/5/2001 - 22/5/2001].

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Abstract/Summary

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.

Item Type:	Study Group Report
Study Group:	Canadian Industrial Problem Solving Workshops > 5th IPSW [Seattle 18/5/2001 - 22/5/2001]
Company Name:	Alberta Energy Company
Industrial Sector:	Energy and utilities
Additional Contributors:	Aggarwala, Rita and Alberts, Tom and Bose, Chris and Driga, Adrian and Espesset, Aude and Harlim, John and Jeon, Jihyoun and Jeon, Seungwon and Katatbeh, Qutaibeh and Kolasa, Larry and Leok, Melvin and Mahmoud, Mufeed and Meza, Rafael and Nettel-Aguirre, Alberto and Popescu, Christina and Teja, Mariana Carrasco
ID Code:	170
Deposited By:	Michele Taroni
Deposited On:	10 October 2008

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.

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