Volume 40 Issue 4
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SHENG Keming, JIANG Guancheng.Prediction of four kinds of sensibility damages to hydrocarbon reservoirs based on random forest algorithm[J]. Drilling Fluid & Completion Fluid，2023, 40（4）：423-430 doi: 10.12358/j.issn.1001-5620.2023.04.002
Citation: SHENG Keming, JIANG Guancheng.Prediction of four kinds of sensibility damages to hydrocarbon reservoirs based on random forest algorithm[J]. Drilling Fluid & Completion Fluid，2023, 40（4）：423-430 doi: 10.12358/j.issn.1001-5620.2023.04.002
shu

Prediction of Four Kinds of Sensibility Damages to Hydrocarbon Reservoirs Based on Random Forest Algorithm

doi: 10.12358/j.issn.1001-5620.2023.04.002
  • 1.

    College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249

  • 2.

    State Key Laboratory of Petroleum Resources and Prospecting·MOE Key Laboratory of Petroleum Engineering·China University of Petroleum (Beijing), Beijing 102249

  • Received Date: 2023-02-07
  • Rev Recd Date: 2023-03-14
  • Publish Date: 2023-07-30
    Abstract
  • Many kinds of hydrocarbon reservoir damages with complex mechanisms have been encountered in every phase of oil and gas field exploration and development. Conventional core flow test used in evaluating the sensibility damage of a reservoir can give reliable test results, however, this test is both expensive (coring, for instance) and time consuming. Researches have shown that a model established with neural network and random forest algorithm on small-scale samples can be used to save time and money in predicting the properties of samples. In this study, the data of a set of small-scale samples tested in laboratory is obtained from the block X. The training-sets and testing-sets are then selected on the samples. By extensively comparing the results of three algorithms, which are the BP neural network algorithm, the radial basis function neural network algorithm and the random forest algorithm, the random forest algorithm is finally selected as the main method of quantitatively diagnosing the sensitivity damage of hydrocarbon reservoirs. To improve the prediction accuracy, algorithms such as grid search are used in hyperparameter optimization, and data dimensionality reduction is performed based on factor weight. A complete model is finally established based on the studies conducted. The average R2 value of the four kinds of reservoir damage model is 0.852, with a prediction accuracy between 90.00% and 95.68%.

     

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