English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Productivity prediction is an important aspect of oil and gas exploration and development. As the amount of field data has increased, traditional engineering methods have begun to face challenges. The prediction of shale gas well production is influenced by geological, drilling, and completion characteristics. The relationship between variables is highly nonlinear and non-intuitive. In this study, 384 production well data were collected, including 14 input features and one output feature, with a total of 5,760 data points, 80% of which were used for training processes and 20% for testing processes. Four machine learning methods, namely an extreme gradient lifting tree, random forest, artificial neural network and support vector machine, were used to construct a shale gas well productivity prediction model. It was found that the machine learning method could accurately can well reflect the nonlinear relationship between the influencing factors and productivity. After the data were entered into the model training, regression evaluation indexes, such as the determination coefficient R 2 , were used for measurement. Compared with other models, the support vector machine regression model with gamma (kernel coefficient) and C (punish coefficient) values of 1 and kernel function as the radial basis function have smaller errors. The mean absolute, mean square, and root mean square errors were 0.062, 0.006, and 0.077, respectively. The R 2  values for the training and testing sets were 0.861 and 0.843, respectively. The results indicated that the prediction accuracy of the support vector machine regression model is higher, and the accuracy of the training set reached 86%. The productivity prediction method we provide has a good guiding significance for other gas well productivity predictions while greatly reducing the uncertainty of gas field development and production.

Prediction of gas production potential based on machine learning in shale gas field: a case study