Optimization of fracture design for horizontal wells in Mahu region based on machine learning

Ma, Junxiu; Shi, Shengnan; Jin, Chen; ZHANG, Jingchen; He, Xiaodong; Li, Xuechen; GUO, Dingfei

doi:10.3724/sp.j.1249.2021.06621

Cited by 2 publications

(1 citation statement)

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“…This model could predict production and optimize construction parameters. Taking 75 fractured horizontal wells in Mahu area as an example, Ma et al (2021) adopted the random forest algorithm to determine the main control factors of post-pressure productivity according to 16 influencing factors in two types of reservoirs and engineering. They established a productivity prediction model that is optimized by a genetic algorithm with inverse propagation algorithm and neural network, and then optimized the fracturing design of horizontal wells based on this.…”

Section: Introductionmentioning

confidence: 99%

Optimizing construction parameters for fractured horizontal wells in shale oil

Liu

Li²,

Jia³

et al. 2023

Front. Earth Sci.

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Shale oil is mainly extracted by fracturing. However, it is difficult to determine the optimum construction parameters to obtain maximum productivity. In this paper, a fuzzy comprehensive production evaluation model for fractured shale oil horizontal wells based on random forest algorithm and coordinated principal component analysis is proposed. The fracturing parameters of the target wells are optimized by combining this model with an orthogonal experimental design. The random forest algorithm was used to calculate the importance of data sample factors. The main controlling factors of the production of fractured horizontal wells in shale oil were obtained. To reduce the noise of the sample data, principal component analysis was used to reduce the dimensions of the main control factors. Furthermore, the random forest algorithm was used to determine the weight of the principal components after reducing the dimensionality. The membership function of the main control factors after reducing dimensionality was established by combining the fuzzy statistics and assignment methods. In addition, the membership matrix of the effect prediction of fractured horizontal wells in shale oil was determined. The fuzzy comprehensive evaluation method is used to score and evaluate the effect of fractured horizontal wells. Combined with the orthogonal experimental design method, the optimized parameter design of a fractured horizontal well considering the comprehensive action of multiple parameters is realized. After construction according to the optimized parameters, production following fracturing increases significantly. This verifies the rationality of the optimization method that is proposed in this paper.

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Section: Introductionmentioning

confidence: 99%