A well rate prediction method based on LSTM algorithm considering manual operations

Abstract

Manual operations such as changing the size of chokes as well as opening and closing of the well have a great impact on oil and gas production from the well. This scenario is not considered in most deep learning methods for predicting productivity. Therefore, a deep learning method based on a long short-term memory (LSTM) neural network model was established to predict well performance considering the manual operations. The input dataset was composed of data related to choke size, daily opening time series, and production; the first 90% of the dataset was used as the training set and the remaining 10% was used as the test set. The deep learning model was constructed using a LSTM module, regularization process, and dropout network. The formulated LSTM model was proficient compared with a model that did not consider the manual operation process, and showed better prediction accuracy. Through multiple experiments, the production-related time step was optimized at three, indicating that prediction for the subsequent step was most relevant to the initial three step inputs. Overall, the operation of opening and closing of wells, changing the size of chokes, and variations in daily production time can be considered in our LSTM deep learning model, which provides more reasonable results.