Research Papers: Petroleum Engineering

Produced Water Re-Injection and Disposal in Low Permeable Reservoirs

[+] Author and Article Information
Azim Kalantariasl

Formation Damage and
Well Treatment Research Group,
IOR/EOR Research Institute,
Shiraz University,
Shiraz, 7193616511, Iran;
Department of Petroleum Engineering,
School of Chemical and
Petroleum Engineering,
Shiraz University,
Shiraz, 7193616511, Iran

Kai Schulze, Jöerg Storz, Christian Burmester, Soeren Küenckeler

Wietze Laboratory,
Industriestraße 2,
Wietze, 29323, Germany

Zhenjiang You

Australian School of Petroleum,
The University of Adelaide,
Adelaide, SA 5005, Australia;
School of Chemical Engineering,
The University of Queensland,
Brisbane, QLD 4072, Australia
e-mail: zhenjiang.you@gmail.com

Alexander Badalyan, Pavel Bedrikovetsky

Australian School of Petroleum,
The University of Adelaide,
Adelaide, SA 5005, Australia

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 29, 2017; final manuscript received October 2, 2018; published online January 18, 2019. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(7), 072905 (Jan 18, 2019) (13 pages) Paper No: JERT-17-1187; doi: 10.1115/1.4042230 History: Received April 29, 2017; Revised October 02, 2018

Produced water re-injection (PWRI) is an important economic and environmental-friendly option to convert waste to value with waterflooding operations. However, it often causes rapid injectivity decline. In the present study, a coreflood test on a low permeable core sample is carried out to investigate the injectivity decline behavior. An analytical model for well impedance (normalized reciprocal of injectivity) growth, along with probabilistic histograms of injectivity damage parameters, is applied to well injectivity decline prediction during produced water disposal in a thick low permeable formation (Völkersen field). An impedance curve with an unusual convex form is observed in both coreflood test and well behavior modeling; the impedance growth rate is lower during external filter cake build-up if compared with the deep bed filtration stage. Low reservoir rock permeability and, consequently, high values of filtration and formation damage coefficients lead to fast impedance growth during deep bed filtration; while external filter cake formation results in relatively slow impedance growth. A risk analysis employing probabilistic histograms of injectivity damage parameters is used to well behavior prediction under high uncertainty conditions.

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Grahic Jump Location
Fig. 1

Three stages of injectivity impairment from the beginning of injection: (a) schema of injectivity decline due to deep bed filtration (stage 1) and external cake formation (stage 2) and (b) stabilization of the external filter cake (stage 3)

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Fig. 2

Schematic of a real-time data acquisition and monitoring system for coreflood test. 1—rock core sample; 2—Viton sleeve; 3—high-pressure core holder; 4—manual pressure generator; 5, 9, 12, and 14–16—manual valves; 6, 10, 11, and 17—pressure gage with readout; 7—suspension with latex microspheres; 8—HPLC pump; 12—back-pressure regulator; 13—differential pressure transmitter; 18—ADAM-4019+ data acquisition module; 19—ADAM-5060 RS-232/RS-485/RS-422 signal converter; 20—personal computer; 21—beakers; 22—PAMAS S4031 GO portable particle counter.

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Fig. 3

Inlet face of the core: (a) photo image before injection, (b) high resolution magnification, (c) photo image after the back-flush, and (d) high resolution magnification

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Fig. 4

Comparison of impedance growth between measured data and model prediction

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Fig. 5

Probabilistic histograms of injectivity damage parameters as obtained from treatment of 35 field dataset: (a) filtration coefficient λ, (b) formation damage coefficient β, (c) external filter cake permeability kc, and (d) lever arm ratio l

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Fig. 6

(a) Injected particle size distributions for average particle sizes taken as half-maximum-sizes after filtering in order to assess the cake properties. (b) Pore size distributions in the external filter cake as obtained from Descartes' theorem using known particle size distributions.

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Fig. 7

Well impedance versus amount of injected particles for the realistic case of the reservoir permeability of 4 mD and three particle sizes: (a) the effect of the particle size on impedance growth and (b) zoom-in at the early stage of water injection

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Fig. 8

Pessimistic, realistic, and optimistic variants for impedance versus amount of injected particles for the medium particle size (rs = 1 μm): (a) the effect of cake permeability and (b) zoom-in at early stage

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Fig. 9

Effect of the particle size and concentration on the injection rate decline with time: (a) rs = 0.5 μm, (b) rs = 1.0 μm, and (c) rs = 2.5 μm



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