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Abstract In order to advance the sustainable development strategy of oil fields and cope with the crude oil prices fluctuations, the oilfield operators adopt a risk-sharing development mode of project contracting and EUR sharing for inefficient blocks with high barrel oil costs to reduce the investment risk. Focused on "single well production enhancement" and "cost control", improve the accuracy of geological and engineering sweet spot predictions via geology and engineering integrated study. Given the non-homogeneity and non-uniformity of hydraulic fracturing of unconventional reservoir, the application of hydraulic intelligence neural network technology is crucial. Fully leverage fracture information obtained from imaging logging and the reflection of various seismic attributes on the fracture to quantitatively study the distribution of fractures. The evaluation of the characteristics of stress, geomechanics, and the interaction between hydraulic and natural fractures are essential for more accurate simulation and prediction of the hydraulic fracturing. Via conducting hydraulic fracturing simulations based on differential stress distribution in horizontal wells, optimizing parameters such as fracture spacing, length, height, cluster combination, and pumping scale to achieve the best match between engineering technology and reservoir conditions, ultimately realizing efficient development.
Jia et al. (Mon,) studied this question.