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 -