International Journal of Earth Sciences and Engineering

Shuai Gao ¹, Chen Chen ¹, Wei Wang ²

Affiliations
1 College of Construction Engineering, Jilin University, Changchun-130021, CN
2 Daqing Oilfield Production Engineering & Research Institute, Daqing-163000, CN

Abstract

At present, Most of the oil shale in-situ heating technology need to hydraulic fracturing in oil shale formation. Nearly all hydraulic fracturing is carried out after the casing perforation. Perforation parameters play an important role among hydraulic fracturing construction parameters, which even affect effect of oil shale in-situ heating. The hydraulic fracturing process of indoor physical simulation experiment is carried out with the same formation conditions and different perforation parameters. The effect of perforation diameter, perforation orientation (the angle between the direction of perforation and the direction of maximum horizontal stress) and perforation density to fracturing pressure and fracture propagation are researched. The experimental results show that fracturing pressure reduces with perforation diameter increasing, but the effect is not obvious. Fracturing pressure increases obviously with perforation orientation increasing, and fracture becomes more complex. Fracturing pressure decreases obviously with perforation density increasing, which has a very small impact on fracture propagation.

Keywords

Oil Shale, Hydraulic Fracturing, Perforation Parameters, Fracturing Pressure, Fracture Propagation.

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Shuai Gao ¹, Chen Chen ², Gang Li ³, Youhong Sun ¹

Affiliations
1 College of Construction Engineering, Jilin University, Changchun-130061, CN
2 Key Lab of Drilling and Exploitation Technology in Complex Conditions, Ministry of Land and Resource, Changchun-130061, CN
3 College of Applied Technology, Jilin University, Changchun-130061, CN

Abstract

Hydraulic Borehole Mining (HBM) method is an important mining technology that has advantage for some special ore formation. However, the traditional hydraulic borehole mining tool’s nozzle is fixed that limits the single well ore production. In this paper, hydraulic borehole mining tool with ratchet controlled expandable nozzle was designed. High pressure water channel, ore pulp discharge channel and ratchet controlled expandable nozzle are three important parts in this design process. Then, the tool was conducted preliminary filed experiment in nong’an area and infrared camera was used to observing breaking condition of the wall of experiment hole. The experiments showed that the ratchet controlled expandable nozzle can expand and back in the experiment hole. And the hydraulic borehole mining tool can break the wall of experiment hole obviously. This research is a preliminary study that provides an ideal mining method and mining tool for thin ore formation.

Keywords

Hydraulic Borehole Mining, Expandable Nozzle, Design and Field Experiment, Ore Particles.

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Fuzhang Zhao ¹, Chen Chen ¹, Bing Han ², Fang Qian ¹

Affiliations
1 Jilin University, College of Construction Engineering, Changchun Jilin, CN
2 Beijing Geo-Engineering Company, Beijing, CN

Abstract

During the foundation pit excavation, the deformation monitoring of supporting structure is directly related to the safety of construction. In this paper, the deformation monitoring and prediction of diaphragm wall are analyzed according to the construction conditions, the surrounding environment, security level and other buildings around. The single network model has the phenomenon of premature convergence and premature convergence. And the GRNN neural network has advantages in the fitting ability and computing speed. So the multi-source data BP-GRNN combined prediction model is established based on the analysis of the PSO-BP and GA-BP neural network prediction model, the model is demonstrated by examples. The prediction results show that the BP-GRNN combined prediction model has higher prediction accuracy and reliability. It will have a good application prospect in the deformation prediction of deep foundation pit and diaphragm wall.

Keywords

Deformation Prediction, Diaphragm Wall, PSO-BP Neural Network, GA-BP Neural Network, Multi-Source Data, Combined Prediction Model.

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