Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network

Li, Ben; Zhou, Fujian; Li, Hui; Duguid, Andrew; Que, Liyong; Xue, Yanpeng; Tan, Yanxin

doi:10.1016/j.ijggc.2017.11.004

Cited by 23 publications

(7 citation statements)

References 40 publications

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“…Risk‐based approaches have been used in the past by investigators to estimate CO 2 leakage risk from storage zones via wellbores . Watson and Bachu developed a model to identify wells with higher leakage potential by using regulatory data.…”

Section: Introductionmentioning

confidence: 99%

“…studied the effects of depth on injection rates and showed that leakage risk decreases for deeper storage zones and for zones with smaller numbers of wells penetrating the storage zone. Li et al . in their study have used a neural network technique to access the well leakage risk.…”

Section: Introductionmentioning

confidence: 99%

“…1,6,7 The benefits and drawbacks of selecting a depleted oil and gas field as a potential storage site should be Depending on the structure of a well, its proximity to injection wells, the storage zone's pressure buildup, and the nature of the CO 2 spread in the field, wells coming into contact with the CO 2 plume may act as possible leakage pathways. 8,[12][13][14][15][16] The nature of the leakage pathways in a well, e.g., fractures in the cement sheath or along the cement-casing interface, corroded casing, and/or rock damaged by drilling activities, 17 can significantly affect the leakage potential of a well. 9,[17][18][19][20] For brine leakage, the pressure differential and a wellbore offering the least resistant path to flow may be the only necessary conditions for leakage to occur.…”

Section: Introductionmentioning

confidence: 99%

“…Risk-based approaches have been used in the past by investigators to estimate CO 2 leakage risk from storage zones via wellbores. 4,8,14,15,23,38,[48][49][50] Watson and Bachu 20 developed a model to identify wells with higher leakage potential by using regulatory data. They used a score-based approach to evaluate the deep and shallow leakage potential of a wellbore.…”

Section: Introductionmentioning

confidence: 99%

“…Celia et al 8 studied the effects of depth on injection rates and showed that leakage risk decreases for deeper storage zones and for zones with smaller numbers of wells penetrating the storage zone. Li et al 14 in their study have used a neural network technique to access the well leakage risk. They used well cement placement, cement type, cement sheath integrity, well age, and well plugging conditions in their calculations of well leakage probability.…”

Section: Introductionmentioning

confidence: 99%

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Field‐scale well leakage risk assessment using reduced‐order models

2019

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Oil wells that intersect a potential CO2 storage zone in a depleted oil and gas field may provide leakage pathways. It is essential to estimate the field‐scale leakage risk associated with these wells. In this study, a risk‐based approach is used to estimate the risk of leakage. Existing reduced‐order models for well leakage are used to quantitatively estimate well leakage rates for cased‐cemented, cased‐uncemented, and open wellbores. For each existing well that intersects the storage zone, we introduce the well leakage index (WLI), which accounts for wellbore geometry, distance from the injection well, buffer layers between the storage zone and underground sources of drinking water, and the nature of storage zone boundary type. For an initial injection well location, the total site well leakage index (SWLI) is calculated, which is the summation of the WLI for all of the wells. Next, the injector location is varied areally and SWLI is calculated for a specified number of potential injector well locations in the storage zone area. Small values for the SWLI correspond to low well leakage potential, indicating where injection well locations can be considered. The developed criterion provides a means to systemically find the areas with highest and lowest well leakage potential for a storage zone. Due to the reduced order nature of the developed method, it should be a useful tool in the planning and execution phase of the CO2 geological sequestration process. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Field‐scale well leakage risk assessment using reduced‐order models

2019

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A Comprehensive Review of Solar Photocatalysis & Photothermal Catalysis for Hydrogen Production from Biomass: from Material Characteristics to Engineering Application

Lei,

Wang,

et al. 2024

Chemistry A European J

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Biomass photoreforming is a promising way of producing sustainable hydrogen thanks to the abundant sources of biomass feedstocks. Solar energy provides the heat and driven force to initial biomass oxidation coupled with H2 evolution. Currently, biomass photoreforming is still far from plant‐scale applications due to the lower solar energy utilization efficiencies, the low H2 yield, and the lack of appropriate photoreactors. The production of H2 from photoreforming of native biomass and platform molecules was summarized and discussed with special attention to the prospects of scaling up the catalysis technology for mass production of hydrogen. The types of photoreforming including photocatalysis and photothermal catalysis were discussed consequently considering the different requirements for photoreactors. We also reviewed the photoreactors that support biomass photoreforming. Numerical simulation methods were implemented for the solid‐liquid two‐phase flow and inter‐particle radiative transfer involved in the reaction process. Developing concentrated photothermal catalytic flowed reactors is beneficial to scale‐up catalytic hydrogen production from biomass.

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Determination of Tensile Elastic Parameters from Brazilian Tensile Test: Theory and Experiments

Lai

Liu

2019

Rock Mech Rock Eng

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Prediction of CO2 leakage risk for wells in carbon sequestration fields with an optimal artificial neural network

Cited by 23 publications

References 40 publications

Field‐scale well leakage risk assessment using reduced‐order models

Field‐scale well leakage risk assessment using reduced‐order models

A Comprehensive Review of Solar Photocatalysis & Photothermal Catalysis for Hydrogen Production from Biomass: from Material Characteristics to Engineering Application

Determination of Tensile Elastic Parameters from Brazilian Tensile Test: Theory and Experiments

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