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Research Papers: Oil/Gas Reservoirs

Characterizing Tight Oil Reservoirs With Dual- and Triple-Porosity Models

[+] Author and Article Information
Ezulike Daniel Obinna

Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
Donadeo Innovation Centre for Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ezulike@ualberta.ca

Dehghanpour Hassan

Department of Civil and
Environmental Engineering,
Donadeo Innovation Centre for Engineering,
School of Mining and Petroleum Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: dehghanpour@ualberta.ca

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received November 5, 2014; final manuscript received January 14, 2016; published online February 22, 2016. Assoc. Editor: Arash Dahi Taleghani.

J. Energy Resour. Technol 138(3), 032801 (Feb 22, 2016) (10 pages) Paper No: JERT-14-1368; doi: 10.1115/1.4032520 History: Received November 05, 2014; Revised January 14, 2016

The response of existing transient triple-porosity models for fractured horizontal wells do not converge to that of linear dual-porosity model (DPM) in the absence of natural/microfractures (MFs). The main reason is the assumption of sequential-depletion from matrix to MF, and from MF to hydraulic-fractures (HFs). This can result in unreasonable estimates of MF and/or HF parameters. Hence, the authors proposed a quadrilinear flow model (QFM) in a previous paper which relaxes this sequential-depletion assumption to allow simultaneous matrix–MF and matrix–HF depletion. Also, it is proved that QFM simplifies to both DPM and linear sequential triple-porosity model (STPM). This work considers the implications of applying QFM, STPM, and DPM type-curves and analysis equations on production data of two fractured horizontal wells completed in the Bakken and Cardium Formations. A comparative study of the reservoir parameters estimated from the application of these models to the same production data reveals two key results. First, the application of DPM on the production data from reservoirs with active MF could result in overestimation of HF half-length. This happens to compensate for the extra fluid depletion pathways provided by MF. Second, the application of STPM on the production data from the reservoirs with active matrix–HF communication could result in overestimation of the MF intensity. Results from this study are significant when selecting the appropriate model for interpreting production data from fractured horizontal wells completed in formations with or without active MF. The DPM is appropriate if analog studies (e.g., outcrop, microseismic and image log analyses) reveal high fracture spacing aspect ratio (negligible MF) in the reservoir. Fracture spacing aspect ratio is MF spacing divided by the HF spacing. The STPM is appropriate if analog studies reveal low spacing aspect ratio (e.g., matrix–HF face damage or high MF intensity within a given HF spacing). QFM is appropriate for all fracture spacing aspect ratios.

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Figures

Grahic Jump Location
Fig. 1

Linear DPM: (a) horizontal cross section of hydraulically fractured horizontal well in tight reservoirs and (b) conceptual flow physics

Grahic Jump Location
Fig. 2

Linear sequential triple-porosity model (STPM): (a) horizontal cross section of hydraulically fractured horizontal well in tight reservoirs and (b) conceptual flow physics

Grahic Jump Location
Fig. 3

QFM: (a) horizontal cross section of hydraulically fractured horizontal well in tight reservoirs and (b) conceptual flow physics

Grahic Jump Location
Fig. 4

DPM with negligible MFs: (a) horizontal cross section of reservoir model, (b) LT flow regime—LT, and (c) PSS flow regime—PSS

Grahic Jump Location
Fig. 5

Linear triple-porosity model with MFs: (a) horizontal cross section of reservoir model, (b) sequential matrix depletion (STPM), and (c) simultaneous matrix depletion (QFM)

Grahic Jump Location
Fig. 10

QFM type-curve match with production data from (a) well A and (b) well B; m is slope

Grahic Jump Location
Fig. 9

STPM type-curve match with production data from (a) well A and (b) well B; m is slope

Grahic Jump Location
Fig. 8

RNP against MBT plot of production data from well A

Grahic Jump Location
Fig. 7

DPM type-curve match with production data from (a) well A and (b) well B. m is slope.

Grahic Jump Location
Fig. 6

RNP against t plots (a) and (b) and RNP against MBT plots (c) and (d) of production data from wells A and B, respectively

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