Prediction of the effect of acidizing measures for gas wells based on deep learning and transfer learning

To address the challenges of small sample sizes and data scarcity in acidized wells, this study proposes an acidization treatment performance prediction method based on transfer learning and deep learning. First, reservoir engineering methods are employed to preprocess the data by incorporating well-specific characteristics, thereby enhancing data completeness and reliability. Subsequently, transfer learning techniques are utilized to migrate rich data features from the source domain (foam drainage and gas lift wells) to the target domain (acidized wells), improving the model’s generalization capability. For model selection, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is adopted for modeling, and comparative experiments are conducted with LSTM and Attention_LSTM models. The results demonstrate that Bi-LSTM exhibits higher accuracy and stability in predicting the production of acidized wells. This method effectively resolves the prediction challenges associated with small-sample data, providing a scientific basis for optimizing acidization treatments and demonstrating significant practical application value.