Importance of Modeling Heterogeneities and Correlation in Reservoir Properties in Unconventional Formations: Examples of Tight Gas Reservoirs

Li, Shengli; Zee, Y.; Gomez, Ernest

doi:10.1007/s12583-021-1430-2

Cited by 5 publications

(1 citation statement)

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“…The porosity value was assigned according to the pore permeability fitting relationship of the actual reservoir. Other geological and engineering parameters were generated by calling algorithms to generate 2000 groups of random combination schemes for productivity simulation [5,33]. Finally, the geological and engineering parameters of each scheme and the corresponding refracturing time were collected and counted.…”

Section: Sample Set Constructionmentioning

confidence: 99%

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Zhang

Ren²,

Feng

et al. 2021

Energies

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Refracturing technology can effectively improve the EUR of horizontal wells in tight reservoirs, and the determination of refracturing time is the key to ensuring the effects of refracturing measures. In view of different types of tight oil reservoirs in the Songliao Basin, a library of 1896 sets of learning samples, with 11 geological and engineering parameters and corresponding refracturing times as characteristic variables, was constructed by combining numerical simulation with field statistics. After a performance comparison and analysis of an artificial neural network, support vector machine and XGBoost algorithm, the support vector machine and XGBoost algorithm were chosen as the base model and fused by the stacking method of integrated learning. Then, a prediction method of refracturing timing of tight oil horizontal wells was established on the basis of an ensemble learning algorithm. Through the prediction and analysis of the refracturing timing corresponding to 257 groups of test data, the prediction results were in good agreement with the real value, and the correlation coefficient R2 was 0.945. The established prediction method can quickly and accurately predict the refracturing time, and effectively guide refracturing practices in the tight oil test area of the Songliao basin.

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Section: Sample Set Constructionmentioning

confidence: 99%