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Research Papers: Petroleum Engineering

An Efficient Workflow for Production Allocation During Water Flooding

[+] Author and Article Information
Vahid Azamipour

School of Chemical Engineering,
Iran University of Science and Technology,
Narmak, Tehran 16846-13114, Iran
e-mail: vahid_azami@alumni.ut.ac.ir

Mehdi Assareh

School of Chemical Engineering,
Iran University of Science and Technology,
Narmak, Tehran 16846-13114, Iran
e-mail: assarehm@iust.ac.ir

Mohammad Reza Dehghani

School of Chemical Engineering,
Iran University of Science and Technology,
Narmak, Tehran 16846-13114, Iran
e-mail: m_dehghani@iust.ac.ir

Georg M. Mittermeir

Heinemann Consulting,
Hauptplatz 13,
Leoben 8700, Austria
e-mail: gmittermeir@a1.net

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 30, 2016; final manuscript received August 24, 2016; published online October 10, 2016. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 139(3), 032902 (Oct 10, 2016) (10 pages) Paper No: JERT-16-1201; doi: 10.1115/1.4034808 History: Received April 30, 2016; Revised August 24, 2016

This paper presents an efficient production optimization scheme for an oil reservoir undergoing water injection by optimizing the production rate for each well. In this approach, an adaptive version of simulated annealing (ASA) is used in two steps. The optimization variables updating in the first stage is associated with a coarse grid model. In the second step, the fine grid model is used to provide more details in final solution search. The proposed method is formulated as a constrained optimization problem defining a desired objective function and a set of existing field/facility constraints. The use of polytope in the ASA ensures the best solution in each iteration. The objective function is based on net present value (NPV). The initial oil production rates for each well come from capacity and property of each well. The coarse grid block model is generated based on average horizon permeability. The proposed optimization workflow was implemented for a field sector model. The results showed that the improved rates optimize the total oil production. The optimization of oil production rates and total water injection rate leads to increase in the total oil production from 315.616 MSm3 (our initial guess) to 440.184 MSm3, and the recovery factor is increased to 26.37%; however, the initial rates are much higher than the optimized rates. Beside this, the recovery factor of optimized production schedule with optimized total injection rate is 3.26% larger than the initial production schedule with optimized total water injection rate.

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Figures

Grahic Jump Location
Fig. 1

Adaptive simulated annealing flow diagram

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Fig. 2

Coarse grid–fine grid combination in ASA

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Fig. 3

The coarse model (left), the fine model (middle), and the wells (right) which are in the edge of reservoir (83, 114, and 5) are water injection, and the wells which are in the middle of reservoir are production wells

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Fig. 4

The left picture is the map of porosity and the right one is permeability map

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Fig. 5

Field oil production rate against time for both coarse and fine models (left) and field pressure against time for both coarse and fine models (right)

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Fig. 6

Left plot: Well oil production rate against time for both coarse and fine models for M_P11-4. Right plot: Pressure decline for both coarse and fine models for M_P11-4.

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Fig. 7

Left plot: Well oil production rate against time for both coarse and fine models for M_P3-4. Right plot: Pressure decline for both coarse and fine models for M_P3-4.

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Fig. 8

Left plot: Field oil production rate comparison between the optimized scenarios (solid line) with the initial scenario (dashed line). Right plot: The total field oil production, comparison between the optimized (solid line) with the initial scenarios (dashed line).

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Fig. 9

Left plot: Field oil production rate comparison between the optimized scenarios (solid line) with the initial scenario with optimized water injection (dashed line). Right plot: Total field oil production comparison between the optimum scenarios (solid line) with the initial scenario with optimized water injection (dashed line).

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Fig. 10

Oil production rates comparison in well M_P3-4 (left plot) and the total oil production comparison in well M_P3-4 (right plot), among optimum scenario, initial scenario, and initial scenario with optimized water injection

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Fig. 11

Oil production rates comparison in well M_P10-4 (left plot) and the total oil production comparison in well M_P10-4 (right plot), among optimum scenario, initial scenario, and initial scenario with optimized water injection (Int)

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Fig. 12

Objective function versus number of iterations for the proposed optimization approach (left) and objective function versus number of iterations for pure SA (right)

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