Predicting Hydraulic Fracture Direction of Propagation When Intersect With Natural Fracture by Using Artificial Neural Network (ANN)

• Main Author: Bt Abd Rani, Nurul Shahizatul Fazila
• Format: Final Year Project
• Language: English
• Published: IRC 2014
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://utpedia.utp.edu.my/14544/1/NurulShahizatulFazila_14359_PE_SEPT2014.pdf; http://utpedia.utp.edu.my/14544/

Successfulness of the hydraulic fracture treatment in unconventional reservoir especially in shale gas reservoir was depends on the communication between hydraulic fracture and natural fracture. Hydraulic fractures propagate across the reservoir during the treatment and intersect with discontinuities present in the reservoir, in this case is pre-existing natural fracture. At this point, several events might occur during intersection. Firstly, hydraulic fracture propagation might step over the pre-existing natural fracture. Secondly, hydraulic fracture is caught up by natural fracture and stop the propagation. Thirdly, hydraulic fracture tip turn into the natural fracture, dilating and opening the natural fracture as the fracture fluid infiltrate the natural fracture. In a very low permeability reservoir, effective treatment should step over the pre-existing natural fracture, extending the network deep into the reservoir, connecting all the natural fracture to increase fracture conductivity and optimizing production of natural resources especially in unconventional shale reservoir. Therefore, parameters that characterized under which condition hydraulic fracture will step over and arrested into natural fracture at the intersection point need to be study and fully understand for designing the best hydraulic fracture treatment. Parameters that affecting the course of fracture propagation, rock properties and fluid properties, was determined and a set of input data was prepared by collecting the data from the previous related research paper. Matlab software was used to develop the artificial neural network (ANN) model that give prediction on the course of hydraulic fracture propagation direction when intersecting with natural fracture by mapping a set of input data to a set of output. The ANN model has been trained, validated and tested by using 46 set of collected data and produced predicted output with good accuracy. Mean squares error (MSE) and regression analysis was used to calculate the output error to show the difference between predicted output and observed output from the experiment. By using the same model, sensitivity analysis was also conducted to see which parameter give the most effect and the least effect on the fracture propagation during intersection.