Suspended Load Prediction on Sucker Rod Suspension Load Based on Artificial Neural Network
Using the test data of suspendsion load on socker rod from oilfield database, a prediction model is presented, which adapted the improved L-M neural network algorithm and explored the 6 effect factors’ relationship: the rod stroke, frequency of strokes, rod diameter, pump diameter, submergence depth and pump setting depth. With training the model,and higher training accuracy is acquired, which shows using this method to predict the suspendsion load is effective.
R. F. Zhou et al., "Suspended Load Prediction on Sucker Rod Suspension Load Based on Artificial Neural Network", Advanced Materials Research, Vols. 217-218, pp. 1040-1043, 2011