Research on the Assessment on the Risk System Regarding Offshore Oil and Gas Field Development Projects Based on the Grey System Theory

Li, Jia Ming

doi:10.4028/www.scientific.net/amm.737.843

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The asset optimization process involves utilizing appropriate evaluation methods to optimally select oil and gas assets, with the development of disciplines such as probability theory, mathematical programming, fuzzy mathematics, and multi-attribute evaluation. A plethora of methods have been employed by previous researchers for project evaluation in the oil and gas field, such as gray theory [6,7], grey fuzzy that combines grey theory with fuzzy evaluation [8], cyclical convolution [9], Multi-Attribute Decision Making (MADM) in conjunction with Analytic Hierarchy Process (AHP) [10], Multi-Criteria Decision Analysis (MCDA) [11], as well as the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Fuzzy-TOPSIS [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

et al. 2023

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The rapid and accurate evaluation of oil and gas assets, specifically for new development projects, poses a significant challenge due to the various project types, limited data availability, brief periods for assessment and decision making, and constraints arising from varying contractual and taxation conditions, political stability, and societal factors. This study leverages the grading standards of the evaluation index system for new oil and gas field development projects, along with relevant mathematical theories and methods for project evaluation and optimization. We developed an asset evaluation approach for new oil and gas projects by analyzing the assets of six new oil and gas field development projects in Brazil. This analysis resulted in the grading and ranking of new projects, and we tested and demonstrated four asset optimization techniques. After a comparative analysis with conventional evaluation results, we established an oil and gas project asset optimization approach centered on the cloud model comprehensive evaluation and linear weighted ranking, exhibiting Kendall’s tau coefficient of 0.8667 with conventional methods. The findings suggest that the combination of the cloud model comprehensive evaluation method with the linear weighted ranking method can facilitate asset optimization for oil and gas field development projects, meeting the practical needs for fast selection among various new projects. Furthermore, this research offers a technical and theoretical foundation for rapid evaluation and decision making regarding new assets.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

et al. 2023

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“…The spatial distribution of ash and heat is predicted by using the Kriging interpolation method. In [7,8], a method that is based on dimensionless parameters and SVM was proposed for coal-rock interface identification.…”

Section: Introductionmentioning

confidence: 99%

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

Kang

Zhang

et al. 2019

Applied Sciences

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Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.

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Research on the Assessment on the Risk System Regarding Offshore Oil and Gas Field Development Projects Based on the Grey System Theory

Abstract: the risk assessment for development project is simply and highly efficient, requires less data, and can clearly uncover the problems. It is plausible to evaluate a plenty of development projects through computers

Cited by 2 publications

References 4 publications

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements

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