FTT-Transformer: A Feature-Time Tokenization Approach with Multi-Head Self-Attention for Oilfield Production Forecasting

Oilfield production forecasting serves as the decision-making basis for monthly production allocation and injection–production system optimization. Existing mainstream prediction methods face significant limitations: the Arps decline curve extrapolates historical production trends, yet its accuracy degrades rapidly following adjustments to injection–production regimes; machine learning methods such as XGBoost can leverage extensive dynamic data but rely heavily on manual feature engineering and offer limited decision interpretability. This paper proposes the FTT-Transformer prediction model, whose core innovation is the Feature-Time Tokenizer (FTT). The FTT projects every scalar pair (time step, feature) in a multivariate time series matrix into a token of uniform dimensionality, superimposing three types of positional information—time embedding, feature embedding, and global position encoding. On this foundation, a multi-head self-attention mechanism performs end-to-end, full-capacity learning of nonlinear interactions across both the temporal and feature dimensions. The model is lightweight, requiring only 35,361 parameters for 13 input features and is readily deployable. Validation was conducted using production data from two independent waterflooding oilfields. On Dataset 2 (60 wells, 2012–2026), the model achieved an R2 of 0.819, achieving performance on par with XGBoost (0.813; DM test p = 0.620, indicating no statistically significant difference) and substantially outperforming temporal Transformer baselines PatchTST (R2 = 0.683) and iTransformer (R2 = 0.786). On Dataset 1 (96 wells), it reached an R2 of 0.930, statistically indistinguishable from XGBoost’s 0.943 (DM test p = 0.611). Five-fold temporal cross-validation yielded a mean R2 of 0.840 ± 0.036, confirming the model’s stability. Ablation experiments revealed that global position encoding contributed most significantly, with its removal causing a 3.9 percentage point reduction in R2 on Dataset 2. Composite feature-importance analysis showed that monthly liquid production and water cut are identified by the model as the two most predictive features, contributing 31.93% and 30.17% of the total importance, respectively. Multi-step forecasting results demonstrated that the model retains an R2 of 0.640 for predictions two months ahead, spanning one complete decision cycle of monthly production reallocation. The proposed architecture is not domain-specific; by adapting the feature embeddings and time encoding, it could potentially be extended to diverse multivariate time series forecasting applications. However, cross-domain validation remains future work.