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Research Papers: Petroleum Engineering

State-of-the-Art Solution of Capacitance Resistance Model by Considering Dynamic Time Constants as a Realistic Assumption

[+] Author and Article Information
A. Lesan

Petroleum Exploration Department,
School of Mining Engineering,
College of Engineering,
University of Tehran,
Tehran 14395-515, Iran

S. Ehsan Eshraghi

Institute of Petroleum Engineering,
School of Chemical Engineering,
College of Engineering,
University of Tehran,
Tehran 14395-515, Iran;
Omid Petro Energy Khavaran, Co.
(Knowledge-Based),
Science and Technology Park,
Mashad, Khorasan Razavi, Iran
e-mail: eshraghi.ipe@ut.ac.ir

A. Bahroudi

Petroleum Exploration Department,
School of Mining Engineering,
College of Engineering,
University of Tehran,
Tehran 14395-515, Iran;
Department of Geosciences,
University of Calgary,
Calgary, AB T2N 1N4, Canada

M. Reza Rasaei

Institute of Petroleum Engineering,
School of Chemical Engineering,
College of Engineering,
University of Tehran,
Tehran 14395-515, Iran

H. Rahami

School of Engineering Science,
College of Engineering,
University of Tehran,
Tehran 14395-515, Iran

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received October 11, 2016; final manuscript received June 5, 2017; published online September 12, 2017. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 140(1), 012904 (Sep 12, 2017) (14 pages) Paper No: JERT-16-1403; doi: 10.1115/1.4037368 History: Received October 11, 2016; Revised June 05, 2017

To have an acceptable accuracy for water flooding projects, proper history matching is an important tool. Capacitance resistance model (CRM) simulates water flooding performance based on two tuning parameters of time constant and connectivity. Main advantages of CRM are its simplicity and fastness; furthermore, it needs only some field-available inputs like injection and production flow rates. CRM is reliable if producers receive the injection rate signal; in other words, duration of history matching must be enough so that the rate signal of injection is sensed in producers. It is a shortcoming of CRM that the results might not be accurate as a result of short history. In the common CRM, time constant is considered to be a static parameter (constant number) during the history of simulation. However, time constant is a time-dependent function that depends on the reservoir nature. In this paper, a new model has been developed as it decreases model dependency on the history matching length by shifting time axis. This new definition adds a rate shift constant to the model mathematics. Moreover, a new model is considering dynamic time constants. This new model is called dynamic capacitance resistance model (DCRM). Two reservoir models have been simulated to analyze the performance of DCRM, and, as a result, it is found that the static time constant is an erroneous assumption. Finally, the accuracy of the results has been improved since the degree-of-freedom of the CRM increased in the new version.

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References

Figures

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Fig. 1

A schematic to compare the different systems with different time constants

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Fig. 2

A group of wells with their related CRM parameters

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Fig. 3

New introduced algorithm

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Fig. 4

(a) Streak case schematic view and (b) homogeneous case schematic view

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Fig. 5

Injection rate pattern of: (a) the streak case and (b) of the homogeneous case

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Fig. 6

Comparison of liquid production rate estimation of DCRM (with S) and SCRM and real data for the streak case

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Fig. 7

Comparison of liquid production rate estimation of DCRM (without S) and SCRM and real data for the streak case

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Fig. 8

Dynamic time constants of: (a) the streak case and (b) the homogeneous case

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Fig. 9

Tuning parameter Ak of the: (a) the streak case and (b) the homogeneous case

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Fig. 10

Rate shift constant (S): (a) streak case and (b) homogeneous case

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Fig. 11

Comparison of liquid production rate estimation of DCRM (with S) and SCRM real data for the homogeneous case

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Fig. 12

Comparison of liquid production rate estimation of DCRM (without S) and SCRM real data for the homogeneous case

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