Structure Optimization of Slip by the Combination of Artificial Neural Network and Genetic Algorithm

Abstract

The bridge plug is a staple tool used in downhole operation and the performance of the slips has a directly influence on the oil well productivity and production safety. We raised an optimize method based on BP network and genetic algorithm to make sure the slips satisfy the high temperature and high pressure demands. Establishing the slips system and making finite element analysis by ANSYS, abtaining sixteen group datas to constitute the BP network training samples, establishing the BP simulation model reflecting curvature radius of the slip fluke, dip angle of the fluke, angle of the fluke and distance between flukes using nonlinearity mapping ability of the neural network, applying optimize design for the simulation model using global optimization ability of the genetic algorithm and abtaining the optimum structure parameters of the slip. The optimized results indicate the whole performance of the slips system has increased notably.

A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor

“…The artificial intelligence method can fit the relationship between the physical model results and the actual data in the drilling site well. A BP (back propagation) neural network, based on a GA (genetic algorithm), has a good fitting effect on nonlinear functions affected by multiple parameters, has been widely used in the drilling field, and has achieved satisfactory results in recent years, such as build-up rate prediction [11], crude oil output decline rate prediction [12], crude oil production prediction [13], overflow and leakage prediction [14][15][16], bottom hole pressure prediction [17], horizontal in situ stress and natural fracture property identification [18], oil saturation prediction [19], drilling tool structure optimization [20], optimal ROP calculation, and drilling parameter optimization [21]. The downhole toolface change value during the adjustment of the bent-housing motor toolface can be regarded as a nonlinear function affected by multiple parameters, and its influencing factors at least include well trajectory, wellbore structure, drilling assembly, drilling fluid property, bit performance, formation characteristics, the interaction between bit and formation, bent-housing motor performance, and drilling engineering parameters.…”