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首页> 外文会议>SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition >Intelligent Prediction of Acid-fracturing Performance in Carbonates Reservoirs
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Intelligent Prediction of Acid-fracturing Performance in Carbonates Reservoirs

机译：碳酸盐储层中酸性压裂性能的智能预测

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摘要

Acidizing joint hydraulic fracturing is a widely used technique to increase the fracture conductivity in carbonate reservoirs stimulation. Predicting acid fracturing performance for optimum fracturing job design requires a detailed understanding of acid-rock reactions, rock strength and the stress applied to the rock and their effect on the fracture conductivity. The available models have many prediction limitations to acidized fracture conductivity with closure stress. Artificial intelligence is suggested to obtain a more precise prediction for acid-fracture conductivity. Artificial neural network (ANN) and adaptive network- based fuzzy inference system (ANFIS) are used to develop intelligent models capable of delivering an accurate design to acid-fracture conductivity. Published experimental data from the literature is used to train the models. 70% of the data was used for training and 30% was used in testing. The results showed that both ANN and ANFIS models outperformed the currently available models. ANFIS subtractive clustering with 0.4 cluster radius showed the best match to experimental data with 1.36% average percentage error and 0.998 R2 .
机译：酸化接合液压压裂是一种广泛使用的技术，以提高碳酸盐储层刺激中的断裂电导率。预测最佳压裂工作设计的酸性压裂性能需要详细了解酸岩反应，岩石强度和施加到岩石的应力及其对裂缝导电性的影响。可用型号具有许多预测限制，以酸化裂缝导电性与闭合应力。建议人工智能获得更精确的酸性断裂电导率预测。人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）用于开发能够提供精确设计的智能模型，以酸性裂缝导电性。来自文献的公布实验数据用于培训模型。 70％的数据用于培训，30％用于测试。结果表明，ANN和ANFIS模型两者都表现出目前可用的型号。具有0.4集群半径的ANFIS减法聚类显示与实验数据的最佳匹配，平均百分比误差为1.36％误差和0.998 R2。

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