TY - JOUR

T1 - Probabilistic net present value analysis for designing techno-economically optimal sequential CO2 sequestration and geothermal energy extraction

AU - Mahdi Rajabi, Mohammad

AU - Chen, Mingjie

AU - Reza Hajizadeh Javaran, Mohammad

AU - Al-Maktoumi, Ali

AU - Izady, Azizallah

AU - Dong, Yanhui

N1 - Funding Information:
The study is supported by BP Oman (Project# BP-DVC-WRC-18-01), Sultan Qaboos University (Project# IG/DVC/WRC/22/02), and Oman National Research Grant (Project# RC/RG-DVC/WRC/21/02). Technical supports are provided by the members of the research group DR/RG/17 of Sultan Qaboos University, Oman.
Publisher Copyright:
© 2022 Elsevier B.V.

PY - 2022/9/1

Y1 - 2022/9/1

N2 - The use of CO2 as the heat transmission fluid, increases the efficiency of geothermal energy extraction from low-enthalpy resources such as depletion oil and gas reservoirs. In the resulting so-called CO2 plume geothermal (CPG) systems, the optimal choice of well position and operational parameters represents a strategic decision problem, due to its profound effect on efficiency. Combined simulation-optimization (Ssbnd O) schemes have been recognized as a valuable tool in making these strategic decisions. Noting that the total lifespan of a CPG system consists of a 'sequestration' and a 'circulation' stage, past CPG Ssbnd O studies only focus on the circulation stage, assuming that the reservoir is initially saturated with CO2. Hence they neglect the realistic state of the reservoir following CO2 sequestration, ignore brine-based power generation, and either neglect the sequestration costs or assume that the sequestration costs are part of the fixed initial investment. This study aims to fill this gap by developing a Ssbnd O algorithm that takes into account both the sequestration and circulation stages of a CPG system lifespan in choosing optimal well location and operations. We frame the problem as a probabilistic risk-minimization scheme to allow for the consideration of geological uncertainty, and solve it through the combined application of a multi-phase numerical model, artificial neural networks, and a hybrid Monte Carlo-genetic algorithm method. Under this context, we successfully minimize the probability of having a negative net present value from the operation. We also examine the influence of economic factors on the profitability of the proposed system, and show that the net CO2 storage income is the economic variable that most affects the risk of non-profitability. Our case study involves a homogeneous, fault-blocked, inclined thin formation that is commonly present in oil and gas fields, but has been the subject of a very limited number of CPG studies.

AB - The use of CO2 as the heat transmission fluid, increases the efficiency of geothermal energy extraction from low-enthalpy resources such as depletion oil and gas reservoirs. In the resulting so-called CO2 plume geothermal (CPG) systems, the optimal choice of well position and operational parameters represents a strategic decision problem, due to its profound effect on efficiency. Combined simulation-optimization (Ssbnd O) schemes have been recognized as a valuable tool in making these strategic decisions. Noting that the total lifespan of a CPG system consists of a 'sequestration' and a 'circulation' stage, past CPG Ssbnd O studies only focus on the circulation stage, assuming that the reservoir is initially saturated with CO2. Hence they neglect the realistic state of the reservoir following CO2 sequestration, ignore brine-based power generation, and either neglect the sequestration costs or assume that the sequestration costs are part of the fixed initial investment. This study aims to fill this gap by developing a Ssbnd O algorithm that takes into account both the sequestration and circulation stages of a CPG system lifespan in choosing optimal well location and operations. We frame the problem as a probabilistic risk-minimization scheme to allow for the consideration of geological uncertainty, and solve it through the combined application of a multi-phase numerical model, artificial neural networks, and a hybrid Monte Carlo-genetic algorithm method. Under this context, we successfully minimize the probability of having a negative net present value from the operation. We also examine the influence of economic factors on the profitability of the proposed system, and show that the net CO2 storage income is the economic variable that most affects the risk of non-profitability. Our case study involves a homogeneous, fault-blocked, inclined thin formation that is commonly present in oil and gas fields, but has been the subject of a very limited number of CPG studies.

KW - CO plume geothermal system

KW - Depleted oil reservoir

KW - Hybrid Monte Carlo-genetic algorithm

KW - Neural network

KW - Risk-aware design

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U2 - 10.1016/j.jhydrol.2022.128237

DO - 10.1016/j.jhydrol.2022.128237

M3 - Article

AN - SCOPUS:85134852956

SN - 0022-1694

VL - 612

SP - 128237

JO - Journal of Hydrology

JF - Journal of Hydrology

M1 - 128237

ER -