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Comprehensive Evaluation of Offshore Oilfield Development Plans Based on Grey Clustering Analysis with Cloud Model

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  • Chao Min
  • Guoquan Wen
  • Binrui Li
  • Xiaochan Zhao

Abstract

Taking the development plans of an offshore oilfield as an example, a new comprehensive evaluation method, the improved Grey Clustering Analysis based on the cloud model (GCAC), is presented in this paper, which takes the ambiguity, randomness, and uncertainty of data into account and overcomes the limits of the general methods, such as subjective prejudice and objective randomness. GCAC converts the data of the oilfield development plans into a cloud, which considers the data of fuzziness, randomness, and the relationship between them. The grey membership degree of each development plan is calculated by this cloud model and an improved grey whitened function is presented in this paper. Then the plans are reordered by their grey membership degrees. In order to make more reasonable consideration of the artificial or unartificial uncertainties, GCAC combines the Grey Entropy Weighting method, Analytical Hierarchy Process (AHP), and Expert Assessment method to determine the weights of each level of indexes, which makes the weights more reasonable and reduces the randomness and the fuzziness of data. GCAC can help obtain a better comparison between the development plans. The reliability of this method is verified by the calculation results.

Suggested Citation

  • Chao Min & Guoquan Wen & Binrui Li & Xiaochan Zhao, 2018. "Comprehensive Evaluation of Offshore Oilfield Development Plans Based on Grey Clustering Analysis with Cloud Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:4020753
    DOI: 10.1155/2018/4020753
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